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Publisher: Elsevier   (Total: 3160 journals)

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Showing 1201 - 1400 of 3160 Journals sorted alphabetically
Graphical Models     Hybrid Journal   (Followers: 3, SJR: 0.454, CiteScore: 2)
Groundwater for Sustainable Development     Full-text available via subscription   (Followers: 4, SJR: 0.329, CiteScore: 1)
Growth Factors and Cytokines in Health and Disease     Full-text available via subscription   (Followers: 1)
Growth Hormone & IGF Research     Hybrid Journal   (Followers: 17, SJR: 1.059, CiteScore: 2)
Gynecologic Oncology     Hybrid Journal   (Followers: 26, SJR: 2.339, CiteScore: 4)
Gynecologic Oncology Reports     Open Access   (Followers: 11, SJR: 0.307, CiteScore: 1)
Gynécologie Obstétrique & Fertilité     Full-text available via subscription   (Followers: 1)
Habitat Intl.     Hybrid Journal   (Followers: 6, SJR: 1.336, CiteScore: 3)
Hand Clinics     Full-text available via subscription   (Followers: 5, SJR: 0.556, CiteScore: 1)
Hand Surgery and Rehabilitation     Full-text available via subscription   (Followers: 4, SJR: 0.358, CiteScore: 1)
Handai Nanophotonics     Full-text available via subscription  
Handbook of Adhesives and Sealants     Full-text available via subscription   (Followers: 2)
Handbook of Agricultural Economics     Full-text available via subscription   (Followers: 3)
Handbook of Algebra     Full-text available via subscription  
Handbook of Analytical Separations     Full-text available via subscription   (Followers: 3)
Handbook of Behavioral Neuroscience     Full-text available via subscription   (Followers: 3)
Handbook of Biological Physics     Full-text available via subscription  
Handbook of Chemical Neuroanatomy     Full-text available via subscription  
Handbook of Clinical Neurology     Full-text available via subscription   (Followers: 2, SJR: 1.007, CiteScore: 2)
Handbook of Clinical Neurophysiology     Full-text available via subscription  
Handbook of Complex Analysis     Full-text available via subscription  
Handbook of Computational Economics     Full-text available via subscription   (Followers: 2, SJR: 4.16, CiteScore: 2)
Handbook of Defense Economics     Full-text available via subscription   (Followers: 2)
Handbook of Development Economics     Full-text available via subscription   (Followers: 8)
Handbook of Differential Equations: Evolutionary Equations     Full-text available via subscription  
Handbook of Differential Equations: Ordinary Differential Equations     Full-text available via subscription  
Handbook of Differential Equations: Stationary Partial Differential Equations     Full-text available via subscription   (Followers: 2)
Handbook of Differential Geometry     Full-text available via subscription  
Handbook of Dynamical Systems     Full-text available via subscription   (Followers: 1)
Handbook of Econometrics     Full-text available via subscription   (Followers: 10)
Handbook of Economic Forecasting     Full-text available via subscription   (Followers: 4)
Handbook of Economic Growth     Full-text available via subscription   (Followers: 3)
Handbook of Environmental Economics     Full-text available via subscription   (Followers: 3)
Handbook of Experimental Economics Results     Full-text available via subscription   (Followers: 6)
Handbook of Exploration and Environmental Geochemistry     Full-text available via subscription   (Followers: 2)
Handbook of Exploration Geochemistry     Full-text available via subscription   (Followers: 1)
Handbook of Ferromagnetic Materials     Full-text available via subscription   (Followers: 1)
Handbook of Game Theory with Economic Applications     Full-text available via subscription   (Followers: 1)
Handbook of Geophysical Exploration: Seismic Exploration     Full-text available via subscription  
Handbook of Health Economics     Full-text available via subscription   (Followers: 13)
Handbook of Immunohistochemistry and in Situ Hybridization of Human Carcinomas     Full-text available via subscription   (Followers: 1)
Handbook of Income Distribution     Full-text available via subscription   (Followers: 4)
Handbook of Industrial Organization     Full-text available via subscription   (Followers: 6)
Handbook of Intl. Economics     Full-text available via subscription  
Handbook of Labor Economics     Full-text available via subscription   (Followers: 14)
Handbook of Law and Economics     Full-text available via subscription   (Followers: 17)
Handbook of Macroeconomics     Full-text available via subscription   (Followers: 10, SJR: 0, CiteScore: 2)
Handbook of Magnetic Materials     Full-text available via subscription   (Followers: 2, SJR: 0.467, CiteScore: 2)
Handbook of Mathematical Economics     Full-text available via subscription  
Handbook of Mathematical Fluid Dynamics     Full-text available via subscription   (Followers: 2)
Handbook of Metal Physics     Full-text available via subscription  
Handbook of Monetary Economics     Full-text available via subscription   (Followers: 9)
Handbook of Natural Resource and Energy Economics     Full-text available via subscription   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 5)
Handbook of Perception and Action     Full-text available via subscription   (Followers: 2)
Handbook of Petroleum Exploration and Production     Full-text available via subscription   (Followers: 2)
Handbook of Population and Family Economics     Full-text available via subscription   (Followers: 4)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 6)
Handbook of Public Economics     Full-text available via subscription   (Followers: 1)
Handbook of Regional and Urban Economics     Full-text available via subscription   (Followers: 3)
Handbook of Sensors and Actuators     Full-text available via subscription   (Followers: 10)
Handbook of Social Choice and Welfare     Full-text available via subscription   (Followers: 3)
Handbook of Statistics     Full-text available via subscription   (Followers: 7, SJR: 0.102, CiteScore: 0)
Handbook of Surface Science     Full-text available via subscription   (Followers: 4, SJR: 0.193, CiteScore: 0)
Handbook of Systemic Autoimmune Diseases     Full-text available via subscription   (Followers: 2)
Handbook of the Economics of Art and Culture     Full-text available via subscription   (Followers: 3)
Handbook of the Economics of Education     Full-text available via subscription   (Followers: 9, SJR: 0, CiteScore: 2)
Handbook of the Economics of Finance     Full-text available via subscription   (Followers: 7)
Handbook of the Economics of Giving, Altruism and Reciprocity     Full-text available via subscription   (Followers: 2)
Handbook of the Geometry of Banach Spaces     Full-text available via subscription   (Followers: 1)
Handbook of the History of Logic     Full-text available via subscription   (Followers: 1)
Handbook of Thermal Analysis and Calorimetry     Full-text available via subscription   (Followers: 1)
Handbook of Thermal Conductivity     Full-text available via subscription   (Followers: 4)
Handbook of Vapor Pressure     Full-text available via subscription  
Handbook on the Physics and Chemistry of Rare Earths     Full-text available via subscription   (Followers: 3, SJR: 0.755, CiteScore: 3)
Handbooks of Management Accounting Research     Full-text available via subscription   (Followers: 4)
HardwareX     Open Access  
Harmful Algae     Hybrid Journal   (Followers: 6, SJR: 1.531, CiteScore: 4)
HBRC J.     Open Access   (Followers: 2)
Health & Place     Hybrid Journal   (Followers: 15, SJR: 1.506, CiteScore: 3)
Health Outcomes Research in Medicine     Hybrid Journal   (Followers: 3)
Health Policy     Hybrid Journal   (Followers: 43, SJR: 1.252, CiteScore: 2)
Health Policy and Technology     Hybrid Journal   (Followers: 4, SJR: 0.322, CiteScore: 1)
Health Professions Education     Open Access   (Followers: 3)
Healthcare : The J. of Delivery Science and Innovation     Full-text available via subscription   (Followers: 1)
Hearing Research     Hybrid Journal   (Followers: 11, SJR: 1.35, CiteScore: 3)
Heart & Lung: The J. of Acute and Critical Care     Hybrid Journal   (Followers: 11, SJR: 0.757, CiteScore: 2)
Heart Failure Clinics     Full-text available via subscription   (Followers: 2, SJR: 1.153, CiteScore: 2)
Heart Rhythm     Hybrid Journal   (Followers: 11, SJR: 3.231, CiteScore: 4)
Heart, Lung and Circulation     Full-text available via subscription   (Followers: 9, SJR: 0.599, CiteScore: 1)
HeartRhythm Case Reports     Open Access   (SJR: 0.232, CiteScore: 0)
Heliyon     Open Access   (SJR: 0.355, CiteScore: 1)
Hellenic J. of Cardiology     Open Access   (Followers: 1, SJR: 0.479, CiteScore: 1)
Hematology, Transfusion and Cell Therapy     Open Access   (Followers: 1)
Hematology/Oncology and Stem Cell Therapy     Open Access   (Followers: 4, SJR: 0.532, CiteScore: 1)
Hematology/Oncology Clinics of North America     Full-text available via subscription   (Followers: 6, SJR: 1.282, CiteScore: 3)
Hepatobiliary & Pancreatic Diseases Intl.     Full-text available via subscription   (Followers: 2, SJR: 0.711, CiteScore: 2)
High Energy Density Physics     Hybrid Journal   (Followers: 2, SJR: 0.933, CiteScore: 2)
Hipertensión y Riesgo Vascular     Full-text available via subscription   (SJR: 0.115, CiteScore: 0)
Historia Mathematica     Full-text available via subscription   (Followers: 1, SJR: 0.174, CiteScore: 0)
History of CERN     Full-text available via subscription   (Followers: 1)
History of Neuroscience in Autobiography     Full-text available via subscription   (Followers: 3)
HIV & AIDS Review     Full-text available via subscription   (Followers: 12, SJR: 0.134, CiteScore: 0)
Homeopathy     Hybrid Journal   (Followers: 6, SJR: 0.678, CiteScore: 1)
HOMO - J. of Comparative Human Biology     Hybrid Journal   (Followers: 2, SJR: 0.335, CiteScore: 1)
Hong Kong J. of Nephrology     Open Access   (Followers: 2, SJR: 0.137, CiteScore: 0)
Hong Kong J. of Occupational Therapy     Open Access   (Followers: 45, SJR: 0.237, CiteScore: 1)
Hong Kong Physiotherapy J.     Open Access   (Followers: 14, SJR: 0.183, CiteScore: 0)
Hormigón y Acero     Full-text available via subscription  
Hormones and Behavior     Hybrid Journal   (Followers: 13, SJR: 1.638, CiteScore: 4)
Horticultural Plant J.     Open Access   (Followers: 5)
Hospital Medicine Clinics     Full-text available via subscription   (Followers: 2, SJR: 0.107, CiteScore: 0)
Human Factors in Information Technology     Full-text available via subscription   (Followers: 35)
Human Immunology     Hybrid Journal   (Followers: 18, SJR: 0.856, CiteScore: 2)
Human Movement Science     Hybrid Journal   (Followers: 15, SJR: 0.756, CiteScore: 2)
Human Pathology     Hybrid Journal   (Followers: 28, SJR: 1.304, CiteScore: 3)
Human Resource Management Review     Hybrid Journal   (Followers: 57, SJR: 1.675, CiteScore: 4)
Hydrometallurgy     Hybrid Journal   (Followers: 13, SJR: 1.208, CiteScore: 3)
IATSS Research     Open Access   (SJR: 0.37, CiteScore: 1)
Icarus     Hybrid Journal   (Followers: 75, SJR: 2.037, CiteScore: 3)
ICT Express     Open Access   (SJR: 0.234, CiteScore: 1)
IDCases     Open Access   (SJR: 0.344, CiteScore: 1)
IERI Procedia     Open Access   (Followers: 1)
IFAC-PapersOnLine     Open Access   (SJR: 0.26, CiteScore: 1)
IIMB Management Review     Open Access   (Followers: 9, SJR: 0.24, CiteScore: 1)
IJC Heart & Vessels     Open Access   (Followers: 1)
IJC Heart & Vasculature     Open Access   (Followers: 1, SJR: 0.342, CiteScore: 1)
IJC Metabolic & Endocrine     Open Access   (Followers: 1, SJR: 0.4, CiteScore: 1)
Image and Vision Computing     Hybrid Journal   (Followers: 15, SJR: 0.612, CiteScore: 3)
Imagen Diagnóstica     Full-text available via subscription   (SJR: 0.106, CiteScore: 0)
Imagerie de la Femme     Full-text available via subscription   (Followers: 1, SJR: 0.124, CiteScore: 0)
Immunity     Full-text available via subscription   (Followers: 57, SJR: 13.393, CiteScore: 16)
Immuno-analyse & Biologie Spécialisée     Full-text available via subscription   (Followers: 2)
Immunobiology     Hybrid Journal   (Followers: 9, SJR: 1.1, CiteScore: 3)
Immunology and Allergy Clinics of North America     Full-text available via subscription   (Followers: 6, SJR: 1.132, CiteScore: 3)
Immunology Letters     Hybrid Journal   (Followers: 13, SJR: 1.168, CiteScore: 3)
Immunotoxicology of Drugs and Chemicals: an Experimental and Clinical Approach     Full-text available via subscription   (Followers: 1)
Implantodontie     Full-text available via subscription  
Indagationes Mathematicae     Open Access   (Followers: 1, SJR: 0.685, CiteScore: 1)
Indian Heart J.     Open Access   (Followers: 5, SJR: 0.333, CiteScore: 1)
Indian J. of Medical Specialities     Hybrid Journal   (SJR: 0.118, CiteScore: 0)
Indian J. of Tuberculosis     Full-text available via subscription   (SJR: 0.221, CiteScore: 0)
Indian Pacing and Electrophysiology J.     Open Access   (Followers: 1, SJR: 0.273, CiteScore: 0)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 3)
Industrial Crops and Products     Hybrid Journal   (Followers: 6, SJR: 1.091, CiteScore: 4)
Industrial Marketing Management     Hybrid Journal   (Followers: 23, SJR: 1.663, CiteScore: 4)
Industrial Safety Series     Full-text available via subscription   (Followers: 17)
Infant Behavior and Development     Hybrid Journal   (Followers: 14, SJR: 0.784, CiteScore: 2)
Infectio     Open Access   (SJR: 0.133, CiteScore: 0)
Infection, Disease & Health     Open Access   (Followers: 8, SJR: 0.23, CiteScore: 1)
Infection, Genetics and Evolution     Hybrid Journal   (Followers: 5, SJR: 1.278, CiteScore: 3)
Infectious Disease Clinics of North America     Full-text available via subscription   (Followers: 6, SJR: 2.359, CiteScore: 5)
Informatics in Medicine Unlocked     Open Access   (SJR: 0.224, CiteScore: 1)
Information & Management     Hybrid Journal   (Followers: 56, SJR: 1.628, CiteScore: 5)
Information and Computation     Hybrid Journal   (Followers: 4, SJR: 0.504, CiteScore: 1)
Information and Organization     Hybrid Journal   (Followers: 39, SJR: 1.202, CiteScore: 3)
Information and Software Technology     Hybrid Journal   (Followers: 6, SJR: 0.581, CiteScore: 4)
Information Economics and Policy     Hybrid Journal   (Followers: 5, SJR: 0.63, CiteScore: 1)
Information Fusion     Hybrid Journal   (Followers: 2, SJR: 1.832, CiteScore: 7)
Information Processing & Management     Hybrid Journal   (Followers: 475, SJR: 0.92, CiteScore: 4)
Information Processing in Agriculture     Open Access   (SJR: 0.352, CiteScore: 2)
Information Processing Letters     Hybrid Journal   (Followers: 6, SJR: 0.412, CiteScore: 1)
Information Sciences     Hybrid Journal   (Followers: 540, SJR: 1.635, CiteScore: 5)
Information Security Technical Report     Full-text available via subscription   (Followers: 12)
Information Systems     Hybrid Journal   (Followers: 13, SJR: 0.805, CiteScore: 4)
Infosecurity     Full-text available via subscription   (Followers: 11)
Infrared Physics & Technology     Hybrid Journal   (Followers: 12, SJR: 0.54, CiteScore: 2)
Injury     Hybrid Journal   (Followers: 18, SJR: 0.99, CiteScore: 2)
Injury Extra     Open Access   (Followers: 2)
Inmunología     Full-text available via subscription   (Followers: 2)
Innovative Food Science & Emerging Technologies     Hybrid Journal   (Followers: 6, SJR: 1.201, CiteScore: 3)
Inorganic Chemistry Communications     Hybrid Journal   (Followers: 13, SJR: 0.43, CiteScore: 2)
Inorganica Chimica Acta     Hybrid Journal   (Followers: 9, SJR: 0.485, CiteScore: 2)
Insect Biochemistry and Molecular Biology     Hybrid Journal   (Followers: 3, SJR: 1.912, CiteScore: 4)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Insulin     Full-text available via subscription   (Followers: 6)
Insurance: Mathematics and Economics     Hybrid Journal   (Followers: 9, SJR: 1.083, CiteScore: 2)
Integration, the VLSI J.     Hybrid Journal   (Followers: 6, SJR: 0.223, CiteScore: 1)
Integrative Medicine Research     Open Access   (Followers: 3)
Intellectual Economics     Open Access  
Intelligence     Hybrid Journal   (Followers: 7, SJR: 1.633, CiteScore: 3)
Intensive and Critical Care Nursing     Hybrid Journal   (Followers: 30, SJR: 0.611, CiteScore: 2)
Interdisciplinary Neurosurgery     Open Access   (SJR: 0.164, CiteScore: 0)
Interface Science and Technology     Full-text available via subscription  
Intermetallics     Hybrid Journal   (Followers: 22, SJR: 1.568, CiteScore: 4)
Internet Interventions : The application of information technology in mental and behavioural health     Open Access   (Followers: 4, SJR: 1.962, CiteScore: 4)
Interventional Cardiology Clinics     Full-text available via subscription   (Followers: 3, SJR: 0.156, CiteScore: 0)
Intl. Biodeterioration & Biodegradation     Hybrid Journal   (Followers: 1, SJR: 1.086, CiteScore: 4)
Intl. Business Review     Hybrid Journal   (Followers: 10, SJR: 1.012, CiteScore: 3)
Intl. Communications in Heat and Mass Transfer     Hybrid Journal   (Followers: 21, SJR: 1.553, CiteScore: 5)
Intl. Comparative Jurisprudence     Open Access   (Followers: 2)
Intl. Dairy J.     Hybrid Journal   (Followers: 7, SJR: 1.051, CiteScore: 2)
Intl. Economics     Hybrid Journal   (Followers: 3, SJR: 0.451, CiteScore: 1)
Intl. Emergency Nursing     Hybrid Journal   (Followers: 10, SJR: 0.461, CiteScore: 1)
Intl. Geophysics     Full-text available via subscription   (Followers: 3)
Intl. Immunopharmacology     Hybrid Journal   (Followers: 2, SJR: 1.051, CiteScore: 3)
Intl. J. for Parasitology     Hybrid Journal   (Followers: 11, SJR: 1.638, CiteScore: 4)
Intl. J. for Parasitology : Drugs and Drug Resistance     Open Access   (Followers: 4, SJR: 1.556, CiteScore: 4)
Intl. J. for Parasitology : Parasites and Wildlife     Open Access   (Followers: 2, SJR: 1.455, CiteScore: 3)
Intl. J. of Accounting     Hybrid Journal   (Followers: 1)

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Similar Journals
Journal Cover
Information Processing in Agriculture
Journal Prestige (SJR): 0.352
Citation Impact (citeScore): 2
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2214-3173
Published by Elsevier Homepage  [3160 journals]
  • Service Delivery Effectiveness of Farmers’ Information and Advice
           Centres (FIACS) in Dinajpur Sadar Upazila of Bangladesh

    • Abstract: Publication date: Available online 21 March 2019Source: Information Processing in AgricultureAuthor(s): Md. Sadekur Rahman, Aliyu Akilu Barau, Md. Rubayet Al Ferdous Noman The indispensability of information in agricultural development led to the setting up of Farmers’ Information and Advice Centres (FIACs) by the government of Bangladesh all over the country. The FIACs mainly provide information related to different sectors like crop, fisheries and livestock with the overall aim of improving farmers’ livelihood. Hence, the effectiveness along with problems and suggestions for improvement of the FIACs’ services were examined in this study by surveying the two FIACs of Dinajpur sadar upazila. The effectiveness of FIAC was determined on some selected farm (crop, fisheries and livestock) information based on three dimensions viz. information received, its understanding and application. The data were collected using interview schedule from a sample of 96 different farmers selected by proportionate random sampling method. Besides the usual descriptive statistics, Pearson’s Product Moment Correlation Coefficient (r) was also used in the analysis. In terms of crop production sub-sector, all the respondents (100%) received, understood and applied ‘tomato seed production technology’ information, followed by ‘rice seed production and preservation technology’ (96.9%). In terms of livestock sub-sector, all the respondents (100%) received, understood and applied ‘beef fattening through UMS method’ information, followed by ‘livestock rearing’ (89.5%). Similarly, in case of fisheries sub-sector, all the respondents (100%) receive, understand and apply ‘Pangas (Pangasius pangasius) culture method’ information, followed by ‘preparation of an ideal pond’ (92.3%). In essence, the FIACs are considerably effective in their service delivery. Among the seven selected characteristics of the respondents, educational qualification, family income and perception on the service delivery efficiency of FIACs showed positive significant association with the effectiveness of the FIAC. The foremost problems mentioned by the respondents were FIACs staff inefficiency (83.33%) and lack of effective long term training program for both farmers and staff (73.84%). Recruiting more staff especially the competent ones (78.13%) and organizing more training programs for both staff and farmers (67.71 percent) are two most important suggestions given by the respondents to overcome the problems.
       
  • Correlation and path coefficient analyses of yield and yield components of
           eggplant (Solanum melongena) in a coarse-textured Ultisol

    • Abstract: Publication date: Available online 21 March 2019Source: Information Processing in AgricultureAuthor(s): Vincent N. Onyia, Uchechukwu Paschal. Chukwudi, Augustu Chika. Ezea, Agatha I. Atugwu, Chikezie O. Ene Assessment of variability and understanding of traits relationship in eggplant species are vital pre-requisite for formulating an effective breeding programme. We studied 23 genotypes of eggplants in a coarse-textured Ultisol using a randomized complete block design experiment with three replications. Data were collected on number of branches, number of leaves, number of nodes and internodes, stem diameter, plant height, floral and yield traits. Results revealed significant (p=0.05) variation in the flowering and fruiting pattern of the genotypes. ‘Yalo x K3BC2P1’ gave the highest individual fruit weight of 80.8g. Individual fruit weight contributed the highest direct positive effect on the fruit yield. It acted majorly through plant height, number of branches, number of leaves and number of days to first fruit set as revealed in the path coefficient result. Individual fruit weight, number of fruits per plant, plant height, number of days to 50% flowering, number of branches, numbers of days to first flowering and 50% fruit set had positive direct effect on yield. These traits should be considered in developing high yielding eggplant breeding programme.
       
  • Optimal dairy feed input selection under alternative feeds availability
           and relative prices

    • Abstract: Publication date: Available online 21 March 2019Source: Information Processing in AgricultureAuthor(s): Othman Alqaisi, Luis Eduardo Moraes, Oghaiki Asaah Ndambi, Ryan Blake Williams Feed formulation is essential in the dairy production chain from economic, nutritional, and environmental perspectives. Optimizing the feed formulation across those three domains – given uncertainty of input prices, input availability, and regional climatic conditions – is a challenge for those in the industry. The diet formulation method that is widely used by trading firms and feed production facilities employs a static linear programming (LP) approach. This approach does not allow for intertemporal feed formulations and switches between dietary feed commodities under feed availability conditions, which result in foregone economic gains for feed producers.The current study develops a multi-period LP feed model that uses historical data to capture ration switch opportunities between available feed resources for dairy cows and demonstrates the potential use of the method in different commodity feed availability situations. We apply 14 diet formulations, each covering 150 months, representing a total of 2,100 diets. The diet formulation considers a specific milk production level for a “model cow”, alternative feed formulations available, and volatility in feed prices. The results demonstrate that there is an opportunity for efficiency gains in the dairy industry with respect to feed formulation.Based on dietary feed inclusion and price spreads, barley can be an important dairy feed grain which completely replaces wheat, corn, and sorghum at price spreads of less than 94%, less than 78%, and less than 67%, respectively. Grain-based feed scenarios represent the lowest nutrient variation while multiple meal feeds had the lowest costs. Furthermore, and on average, multiple meal feed scenarios provided 10% higher dietary crude protein contents compared to grain based feed scenarios (i.e. 163 vs 179 g/kg DM formulated feed). Meanwhile, multiple meal feeding cost was 11% lower than that in the grain based feeding scenarios. Additionally, the use of multiple meals reduces alfalfa dietary inclusion by 7% on dry matter basis. Our analysis shows a strong reduction in feed cost associated with dietary crude protein reduction equivalent to 7.6 USD/tonne per 1% reduction in dietary crude protein level.The modeling approach allows for the interaction between feed components over time taking into consideration volatile global feed prices, thereby improving feed availability and feed formulation. Overall, the model provides a decision making tool to improve the use of feed resources in the dairy sector.
       
  • Voice-driven fleet management system for agricultural operations

    • Abstract: Publication date: Available online 11 March 2019Source: Information Processing in AgricultureAuthor(s): Ch. Achillas, D. Bochtis, D. Aidonis, V. Marinoudi, D. Folinas Food consumption is constantly increasing at global scale. In this light, agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products. However, due to by environmental and biological factors (e.g. soil compaction) the weight and size of the machinery cannot be further physically optimized. Thus, only marginal improvements are possible to increase equipment effectiveness. On the contrary, late technological advances in ICT provide the ground for significant improvements in agri-production efficiency. In this work, the V-Agrifleet tool is presented and demonstrated. V-Agrifleet is developed to provide a “hands-free” interface for information exchange and an “Olympic view” to all coordinated users, giving them the ability for decentralized decision-making. The proposed tool can be used by the end-users (e.g. farmers, contractors, farm associations, agri-products storage and processing facilities, etc.) order to optimize task and time management. The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations. Its vendor-independent architecture, voice-driven interaction, context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system.
       
  • GIS approach for assessment of land suitability for different land use
           alternatives in semi arid environment in Jordan: Case study (Al Gadeer
           Alabyad-Mafraq)

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Safa Mazahreh, Majed Bsoul, Doaa Abu Hamoor The semi arid lands of Jordan are fragile and severely degraded due to low rainfall and mismanagement of natural resources. As human demands increase, sustaining the productivity of land becomes more and more important. Land suitability evaluation can contribute towards better land management; mitigation of land degradation; and designing land use pattern that prevents environmental problems through segregation of competing land uses. Suitability analysis allows identifying the main limiting factors for the agricultural production and enables decision makers to develop crop managements able to increase the land productivity.The purpose of this study was to develop a Geographic Information System (GIS) based approach for land use suitability assessment in order to assist land managers to identify areas with physical limitations for different land use alternatives based on research criteria developed by FAO and modified by stakeholders.This study was conducted using various data and maps incorporated within (GIS) in order to derive potential suitability for different Land Utilization Types (LUTs). Land suitability mapping was developed using an innovative approach that integrates soil and climatic data for land suitability assessment.Suitability maps for each land use were developed to show the suitability classes and display the spatial representation of soils suitable for agriculture. The output of suitability analyses provided not only the type of land use for which the land was suitable, but also information about the type of limitation (s) facing the utilization of the land. Optimum land use alternatives (scenarios) were formulated to improve and optimize the agricultural production in the study area.
       
  • Heating demand and economic feasibility analysis for year-round vegetable
           production in Canadian Prairies greenhouses

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Md Shamim Ahamed, Huiqing Guo, Lisa Taylor, Karen Tanino Greenhouse vegetable production in Canadian Prairies is important for creating a sustainable regional food economy, especially in northern communities. This study included the estimation of heating demand for year-round production and evaluation of the economic feasibility of greenhouse vegetable production (tomato, cucumber, and pepper) in a conceptually designed greenhouse (0.6 ha) located in remote northern communities in Saskatchewan, Canada. The heating simulation was based on a greenhouse heating simulation model (GREENHEAT) developed by the authors recently. The simulation results showed that the annual heating requirement for the production of tomato, cucumber, and pepper are 1486 MJ m−2, 1657 MJ m−2, and 1754 MJ m−2, respectively. The economic analysis indicates the net return (NR) from the production of tomato, cucumber, and pepper, are C$69.2/m2 (in Canadian dollar, CAD), C$41.5/m2, and C$43.8/m2, respectively, based on the market price C$3.5/kg, C$2.7/kg, and C$8.0/kg, and yields of 55.0 kg m−2, 65.0 kg m−2, and 23.0 kg m−2. The net present value (NPV) for the tomato, cucumber, and pepper production are C$1.9 M, C$1.2 M, and C$1.1 M, respectively, and the benefit-cost ratio (BCR) are 1.38, 1.21, and 1.21. The economic feasibility analysis indicates the year-round production of vegetables in a greenhouse at remote northern Saskatchewan would be economically profitable.
       
  • Study of LED array fill light based on parallel particle swarm
           optimization in greenhouse planting

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Feifei He, Lihua Zeng, Dongming Li, Zhenhui Ren Agricultural productivity is crucial to the economy. The output and quality of crops have a direct impact on people’s daily lives and a country's food and clothing. Therefore, harvesting high-quality crops efficiently and maximizing yield per unit area are the most important goals pursued by farmers. As an important parameter of plant growth, light intensity is one of the important factors that affects plant growth and development, morphological establishment and accumulation of functional chemical substances. When light intensity cannot meet the plant’s needs, the optimal light intensity or uneven light distribution will have a greater impact on plant growth and development. This paper aims to address the optimal plant light intensity problem. The paper presents an expert system technology database storing the empirical value of real-time light intensity values and compares it with a parallel particle swarm optimization algorithm to identify the optimal locations where LED lights need to be turned on and where drive circuit lit LED arrays need to be situated, to identify the number of LED fill lights and to solve light intensity optimization problems.
       
  • Assessing model parameters sensitivity and uncertainty of streamflow,
           sediment, and nutrient transport using SWAT

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Abdullah O. Dakhlalla, Prem B. Parajuli The objectives of this study were to (1) simulate streamflow, total sediment (TS), total phosphorus (TP), and total nitrogen (TN) loads; and (2) use the sequential uncertainty fitting (SUFI-2) algorithm to quantify the model parameter sensitivity and uncertainty in simulating streamflow, TS, TP, and TN loads using Soil and Water Assessment (SWAT) model in Big Sunflower River watershed (BSRW). The model was calibrated from 1996 to 2003 and validated from 2004 to 2010 for daily streamflow, TS load, TP load, and TN load. The model performed well simulating daily streamflow (R2 = 0.58–0.75, NSE = 0.47–0.75), TS load (R2 = 0.50–0.72, NSE = 0.47–0.66), and TP load (R2 = 0.79–0.82, NSE = 0.73–0.77), and the model performance was slightly low for TN load (R2 = 0.13–0.31, NSE = −0.09 to 0.07). This study determined that parameter uncertainty was greatest for simulating TN load (p-factor = 0.48, r-factor = 1.25) and that parameter uncertainty was lowest for simulating streamflow (p-factor = 0.70–0.78, r-factor = 1.18–1.19). Output uncertainty was much greater during peak streamflow and peak pollutant loads compared to periods of low streamflow and low pollutant loads. The sensitivity analyses found that streamflow was most sensitive to Manning’s roughness coefficient for the main channel (CH_N2), TS load was most sensitive the peak rate adjustment factor for sediment routing in the tributary channels (ADJ_PKR), TP load was most sensitive to the Phosphorus enrichment ratio for loading with sediments (ERORGP), and TN load was most sensitive to the denitrification exponential rate coefficient (CDN). Uncertainty was found to be much greater during peak streamflow and peak pollutant loads compared to periods of low streamflow and low pollutant loads.
       
  • Automated recognition and classification of adulteration levels from bulk
           paddy grain samples

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah Fraudulent labeling and adulteration are the major concerns in the global rice industry. Almost all the paddy varieties being sold in the market are prone to adulteration. It is very difficult to differentiate paddy grains of various varieties in the mixed bulk sample based on visual observation. Currently, there is no sophisticated appearance-based commercial scale technology to reliably detect and quantify adulteration in bulk paddy grain samples. The paper presents a cost-effective image processing technique for the recognition of adulteration and classification of adulteration levels (%) from the images of adulterated bulk paddy samples using state-of-the-art color and texture features. In this work, seven adulterated bulk paddy samples are considered and each of the samples is prepared by mixing a premium paddy variety with the identical looking and commercially inferior paddy variety at five different adulteration levels (weight ratios) of 10%, 15%, 20%, 25% and 30%. The study compares the performances of three different classification models, namely, Multilayer Back Propagation Neural Network (BPNN), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). The Principal Component Analysis (PCA) and Sequential Forward Floating Selection (SFFS) methods have been employed separately for the automatic selection of optimal feature subsets from the combined color and texture features. The maximum average adulteration level classification accuracy of 93.31% is obtained using the BPNN classification model trained with PCA-based reduced features. The proposed technique can be used as an economic, rapid, non-destructive and quantitative technique for testing adulteration, authenticity, and quality of bulk paddy grain samples.
       
  • An ICT model for increased adoption of farm input information in
           developing countries: A case in Sikasso, Mali

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Macire Kante, Robert Oboko, Christopher Chepken Information and Communication Technologies (ICT) play a key role in the dissemination of information on farm inputs for their increased adoption. Small-scale cereal farmers have been exposed to ICT-based farm input information in developing countries. However, an underuse of these ICTs services has led to an insignificant adoption of farm input information. That underuse was due to certain factors. The purpose of this study was to propose an ICT model for increased adoption of farm input information by establishing these factors and their relationships. A convenient sample of 300 small-scale cereal farmers was selected in Sikasso, Mali to gather data. The partial least squares structural equation modelling technique was used to assess the model that was being proposed. We used the technique to establish the measurement model validity and to assess the structural model (hypotheses). The result suggested that the model was highly predictive of the Use of ICT-based farm input information (80.7%). In addition, use of these ICT-based farm input information was also highly predictive (68.4%) of the Increased Adoption of farm input information by small-scale cereal farmers in developing countries.
       
  • Determination of the oxidative stability of olive oil using an integrated
           system based on dielectric spectroscopy and computer vision

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Alireza Sanaeifar, Abdolabbas Jafari During storage, olive oil may suffer degradation leading to an inferior quality level when purchased and consumed. Oxidative stability is one of the most important parameters for maintaining the quality of olive oil, which affects its acceptability and market value. The current methods of predicting the oxidative stability of edible oils are costly and time-consuming. The aim of the present research is to demonstrate the use of dielectric spectroscopy integrated with computer vision for determining the oxidative stability index (OSI) of olive oil. The most effective features were selected from the extracted dielectric and visual features for each olive oil sample. Three machine learning techniques were employed to process the raw data to develop an oxidative stability prediction algorithm, including artificial neural network (ANN), support vector machine (SVM) and multiple linear regression (MLR). The predictive models showed a great agreement with the results obtained by the Rancimat instrument that was used as a reference method. The best result for modelling the oxidative stability of olive oil was obtained using SVM technique with the R-value of 0.979. It can be concluded that this new approach may be utilized as a perfect replacement for quicker and cheaper assessment of olive oil oxidation.
       
  • Use fuzzy interface systems to optimize land suitability evaluation for
           surface and trickle irrigation

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Yaser Hoseini In this study fuzzy logic system was used for optimization parametric evaluation system in surface and trickle irrigations. This study was performed on a surface area of 5175 ha in fathali region located in Ardabil province of Iran. It was indicated that for trickle and surface irrigation respectively an area of about 2941.35 ha (56.77%) and 159.81 ha (3%) of the lands is “highly suitable” and an area of about 246.43 ha (4.7%) and 312.69 ha (6%) is “moderately suitable”. About 797.1 ha (15.4%) and 2744.17 ha (53.02%) were respectively “marginally suitable” for trickle and surface irrigations. “Currently not suitable” suitability included about 737.58 ha (14.2%) and 1746.05 ha (33.7%) and “Permanently not suitable” suitability matched 458.54 ha (8.86%) and 212.28 ha (4.1%) in the zone under study. According to the results, there is a major difference between the “highly suitable” lands obtained through the two methods and the area of “highly suitable” lands in the trickle method is about 18 times of the area of ‘highly suitable” obtained through the surface irrigation method. As a result, considering the gradual changes of soil parameters, fuzzy evaluation leads to more accuracy compared to the parametric non fuzzy method. By and large, it can be said that fuzzy method, shows higher qualities about the suitability of lands for trickle irrigation.
       
  • Path-tracking control based on a dynamic trigonometric function

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Yunyi Wang, Shuo Zhang, Zhongxiang Zhu, Zhen Li, Yuefeng Du, Lizhi Fang With the rapid development of the modern vehicle industry, the automated control of new vehicles is in increasing demand. However, traditional course control has been unable to meet the actual needs of such demand. To solve this problem, more precise path-tracking control technologies have attracted increased attention. This paper presents a new algorithm based on the latitude and longitude information, as well as a dynamic trigonometric function, to improve the accuracy of position deviation. First, the algorithm takes the course deviation and adjustment time as the optimization objectives and the given path and speed as the constraints. The controller continuously adjusts the output through a cyclic “adjustment and detection” process. Second, through an integration of the steering, positioning, and speed control systems, an experimental platform of a path-tracking control system based on the National Instruments (NI) myRIO controller and LabVIEW was developed. In addition, path-tracking experiments were carried out along a linear path, while changing lanes, and on a curved path. When comparing and analyzing the experimental results, it can be seen that the average deviation in lateral displacement along the linear and curved paths was 0.32 and −0.8 cm, and the standard deviation of the lateral displacement was 2.65 and 2.39 cm, respectively. When changing lanes, the total adjustment time for the vehicle close to the target line to reach stability was about 1.5 s. Finally, the experimental results indicate that the new algorithm achieves good stability and high control accuracy, and can overcome directional and positional errors caused by road interference while driving, meeting the precision requirements of automated vehicle control.
       
  • Prognostication of energy indices of tractor-implement utilizing soft
           computing techniques

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): S.M. Shafaei, M. Loghavi, S. Kamgar Energy indices (energy requirement for tillage implement (ERTI) and tractor overall energy efficiency (TOEE)) of tractor-implement during tillage operations were aimed to be investigated in this study. To generate a new comprehensive model, the effects of forward speed at three levels (2, 4 and 6 km/h) and plowing depth at three levels (10, 20 and 30 cm) on energy indices were experimentally evaluated. Two soft computing techniques, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were employed to prognosticate energy indices. Comparison between the best developed structure of each soft computing technique demonstrated that one comprehensive ANN model was preferred than two individual ANFIS models. According to the ANN prognostication results, simultaneous increase of forward speed from 2 to 6 km/h along with plowing depth increment from 10 to 30 cm led to nonlinear increment of the ERTI and TOEE from 33.87 to 122.66 MJ/ha and 4.65 to 17.85%, respectively. Moreover, interaction of forward speed and plowing depth on energy indices was congruent. Development of comprehensive ANN model now makes it possible to answer fundamental questions in domain of the effect of plowing depth and forward speed on energy indices of tractor-implement that were previously intractable. Hence, to properly manage energy indices and reduce energy dissipation of tractor-implement, application of the new developed ANN model is strongly recommended.
       
  • Social network structures among the livestock farmers vis a vis calcium
           supplement technology

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Sreeram Vishnu, Jancy Gupta, S.P. Subash This study aims to delineate and analyze the configuration of social networks of farmers with respect to the acquisition of information on vital livestock technology. Three stage sampling was carried out by interviewing 320 technology-adopter farmers from four districts of Kerala State in India. For mapping the network, social network analysis (SNA) was used, which revealed the important sources as well as patterns of information access by farmers. Results established the predominance of a formal communication source (veterinary doctor) in the study locales followed by small-sized peer groups of livestock farmers for crucial information support on technology use. The trend is observed irrespective of their gender in various study areas. Significantly the study thus, underscored the role of homogenous peer groups of farmers in facilitating meaningful interactions as well as information sharing on the technology. Given the low level of adoption of most livestock technologies along with the weak livestock extension machinery in the country, these findings could be used by extension agencies to strategize future technological interventions.
       
  • Effective plant discrimination based on the combination of local binary
           pattern operators and multiclass support vector machine methods

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Vi Nguyen Thanh Le, Beniamin Apopei, Kamal Alameh Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes.
       
  • Mathematical modeling of drying characteristics of Jew’s mallow
           (Corchorus olitorius) leaves

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Adewale Olusegun Omolola, Patrick Francis Kapila, Henry Mbitha Silungwe Drying behaviour of Jew’s mallow leaves using an oven dryer was studied. The influence of drying temperatures (50, 60 and 70 °C) on moisture content of the leaves at stable air velocity was considered. Five drying models, including, simple exponential, Page, Two-term exponential, Logarithmic, and Wang and Singh were fitted to drying data. Two-term exponential model adequately express the drying behaviour of Jew’s mallow leaves. Effective moisture diffusivity of Jew’s mallow leaves ranged from 8.18 × 10−8 to 1.13 × 10−7 m2/s. Dependence of the computed effective diffusivity on oven temperature was obvious. The energy required for oven drying of Jew’s mallow leaves was found to be 14.84 kJ/mol. The L∗, a∗, b∗, ΔE, a∗/b∗ colour characteristics of the dried leaves range from 31.8 to 32.87, −3.73 to −4.37, 13.6 to 16.47, 69.00 to 69.73, and −0.26 to −0.34 respectively. Oven drying conditions of 50 °C 150 min and 70 °C 90 min resulted to dried leaves with desirable colour characteristics.
       
  • Heterosis and combining ability in cucumber (Cucumis sativus L.)

    • Abstract: Publication date: March 2019Source: Information Processing in Agriculture, Volume 6, Issue 1Author(s): Chikezie Onuora Ene, Peter Ejimofor Ogbonna, Christian Ugwu Agbo, Uchechukwu Paschal Chukwudi The objective of this work was to evaluate four parental lines of cucumber which included: ‘Zeina’ (Zna), ‘Beit Alpha’ (BA), ‘Straight 8’ (Strght 8) and ‘Calypso’ (Capso) and six F1s generated by diallel cross, using randomized complete block design with three replications to study heterosis and combining ability for yield and yield component traits. Estimated heterosis showed that cross ‘Zna × Capso’ had the highest Better Parent (BP) heterosis in total fruit yield/ha while ‘BA × Capso’ had the highest Mid Parent (MP) heterosis in the same trait. Significant general (GCA) and specific (SCA) combining ability variances were obtained in all the traits implying that both the additive and non-additive gene effects operated in the genetic expression of the traits. Relative magnitude of GCA and SCA variances indicated preponderance of additive gene action for all the traits. ‘Beit Alpha’ and ‘Straight 8’ are best general combiners while ‘BA × Capso’ and ‘Capso × Strght 8’ were the best specific combiners for total fruit yield.
       
  • A survey of semantic web technology for agriculture

    • Abstract: Publication date: Available online 1 March 2019Source: Information Processing in AgricultureAuthor(s): Brett Drury, Robson Fernandes, Maria-Fernanda Moura, Alneu de Andrade Lopes Semantic web technologies have become a popular technique to apply meaning to unstructured data. They have been infrequently applied to problems within the agricultural domain when compared to complementary domains. Despite this lack of application, agriculture has a large number of semantic resources that have been developed by large NGOs such as the Food and Agriculture Organization (FAO). This survey is intended to motivate further research in the application of semantic web technologies for agricultural problems, by making available a self contained reference that provides: a comprehensive review of preexisting semantic resources and their construction methods, data interchange standards, as well as a survey of the current applications of semantic web technologies.
       
  • Dielectric spectroscopy as a potential technique for prediction of
           kiwifruit quality indices during storage

    • Abstract: Publication date: Available online 28 February 2019Source: Information Processing in AgricultureAuthor(s): Atefeh Fazayeli, Saadat Kamgar, Seyed Mehdi Nassiri, Hassan Fazayeli, Miguel de la Guardia Dielectric spectroscopy has been employed as a simple, low cost and a non-destructive way for prediction of some physicochemical indices of kiwifruit during storage. A parallel-plate capacitor was developed and supplied with sinusoidal voltage waves within a frequency range of 40 kHz – 20 MHz. Dielectric properties of samples were measured by the dielectric sensor. Additionally, changes associated with fruit ripening properties, including firmness, total soluble solid (TSS) and pH were determined as a function of time at 2 °C. The results showed that storage time significantly affected the quality characteristics of kiwifruit. Artificial neural networks (ANNs) were employed to develop models for prediction of quality indices from dielectric properties at the swept frequencies. Dielectric property features were selected as inputs while the quality indices including firmness, TSS and pH were chosen as output for the ANNs. The obtained models were able to predict the firmness, soluble solids content, and pH of kiwifruit non-destructively. Among predictive models, an ANN with a topology of 20-19-1 gave a perfect capability to predict the kiwifruit firmness with R2 value of 0.92. Results of this research show that this technique can be used as an efficient and non-destructive method for kiwifruit quality evaluation and monitoring the ripening.
       
  • Irrigated pinto bean crop stress and yield assessment using ground based
           low altitude remote sensing technology

    • Abstract: Publication date: Available online 29 January 2019Source: Information Processing in AgricultureAuthor(s): Rakesh Ranjan, Abhilash K. Chandel, Lav R. Khot, Haitham Y. Bahlol, Jianfeng Zhou, Rick A. Boydston, Phillip N. Miklas The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs > 0.60, coefficient of determination (R2): 0.50–0.56, P 
       
  • Traceability implementation in food supply chain: A grey-DEMATEL approach

    • Abstract: Publication date: Available online 23 January 2019Source: Information Processing in AgricultureAuthor(s): Abid Haleem, Shahbaz Khan, Mohd Imran Khan Numerous incidents of food adulteration, fraudulence and foodborne disease outbreaks have shaken the consumer confidence towards the food they consume. These incidents compel the Food Supply Chain (FSC) partners to implement an appropriate traceability system in their respective supply chains to sustain the consumer confidence. The objective of this research is to identify the drivers (major factors) which play a significant role in the successful implementation of the traceability system in FSC and evaluate the causal relationships developing therein. Twelve drivers are identified towards implementation of the traceability system in FSC through literature review and supported with expert’s opinion. The grey-based DEMATEL approach is identified to evaluate these relationships among the drivers according to their net effect. Further, these drivers ranked based on the prominence and effect score. The finding of this research shows that the drivers are clustered into two groups namely: influential (cause) and influenced (effect) group. Four drivers belong to the influential group, and remaining eight are from the influenced group. The most influential driver is the “food safety and quality” which provide a significant effect on the implementation of a traceability system. This research can be a building block to develop a framework to implement the traceability system within FSC and assist the policymakers, and practitioners to identify and evaluate drivers related to the implementation of traceability system in FSC. This paper also provides a useful insight & support to the practitioners and managers in decision making for traceability implementation related issues.
       
  • Prediction of the biogas production using GA and ACO input features
           selection method for ANN model

    • Abstract: Publication date: Available online 11 January 2019Source: Information Processing in AgricultureAuthor(s): Tanja Beltramo, Michael Klocke, Bernd Hitzmann This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9.
       
  • Neural computing efforts for integrated simulation of ultrasound-assisted
           hydration kinetics of wheat

    • Abstract: Publication date: Available online 11 January 2019Source: Information Processing in AgricultureAuthor(s): S.M. Shafaei, A. Nourmohamadi-Moghadami, H. Rahmanian-Koushkaki, S. Kamgar This study is dedicated to examine predictive ability of neural computing environments, based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) strategies, for integrated simulation of ultrasound-assisted hydration kinetics of wheat kernel. Hydration process was accomplished at five hydration temperatures of 30, 40, 50, 60 and 70 °C in ultrasonication conditions named control (without ultrasound treatment), US1 (25 kHz, 360 W) and US2 (40 kHz, 480 W). The hydration temperature, ultrasonication condition, and hydration time were used as input variables and moisture content was taken as output variable in the neural computing simulation environments. On account of statistical performance criteria, the distinguished ANFIS simulation environment with coefficient of determination of 0.991, root mean square error of 2.478% d.b., mean relative deviation modulus of 4.301% and average of absolute values of simulation residual errors of 1.863% d.b. was better performed than the distinguished ANN simulation environment. The ANFIS simulation results showed that individual or simultaneous increment of hydration temperature and hydration time caused nonlinear increment of moisture content at any given ultrasonication condition. Moreover, physical perception obtained from the integrated ANFIS simulation results indicated congruency effect (sponge and acoustic cavitation) of cutting-edge ultrasound technology on water absorption. The ANFIS simulation results improved the state of art in domain of studying ultrasound-assisted hydration process of wheat. Therefore, the distinguished ANFIS simulation environment is suggested to be served as an effective step towards management of ultrasound-assisted hydration process of wheat in seed priming, flour milling (tempering), making dough, and wet storage processes.Graphical abstractGraphical abstract for this article
       
  • Enhancing aquaponics management with IoT-based Predictive Analytics for
           efficient information utilization

    • Abstract: Publication date: Available online 11 January 2019Source: Information Processing in AgricultureAuthor(s): Divas Karimanzira, Thomas Rauschenbach Modern aquaponic systems can be highly successful, but they require intensive monitoring, control and management. Consequently, the Automation Pyramid (AP) with its layers of Supervisory Control and Data Acquisition (SCADA), Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) is applied for process control. With cloud-based IoT-based Predictive Analytics at the fore marsh, it is worth finding out if IoT will make these technologies obsolete, or they can work together to gain more beneficial results. In this paper, we will discuss the enhancement of SCADA, ERP and MES with IoT in aquaponics and likewise how IoT-based Predictive Analytics can help to get more out of it. An example use case of an aquaponics project with five demonstration sites in different geographical locations will be presented to show the benefits of IoT on example Predictive Analytics services. Innovative is the collection of data from the five demonstration sites over IoT to make the models of fish, tomatoes, technical components such as filters used for remote monitoring, predictive remote maintenance and economical optimization of the individual plants robust. Robustness of the various models, fish and crop growth models, models for econometric optimization were evaluated using Monte Carlo Simulations revealing as expected the superiority of the IoT-based models. Our analysis suggest that the models are generally tolerant to the temperature coefficient variations of up to 15% and the econometric models tolerated a variation of for example feed ration size for fish of up to 4% and by the energy optimization models a tolerance of up to 14% by variations of solar radiation could be noticed. Furthermore, from the analysis made, it can be concluded that MES has several capabilities which cannot be replaced by IoT such as responsiveness to trigger changes on anomalies. It act as proxy when there is no case for sensors and reliably ensure correct execution in the aquaponics plants. IoT systems can produce unprecedented improvements in many areas but need MES to leverage their true potential and benefits.
       
  • Water absorption characteristics of Canarium Schweinfurthii
           fruits

    • Abstract: Publication date: Available online 28 December 2018Source: Information Processing in AgricultureAuthor(s): James Chinaka Ehiem, Victor Ifeanyichukwu Obiora Ndirika, Udochukwu Nelson Onwuka, Yvan Gariepy, Vijayan Raghavan Water absorption characteristics of two varieties of Canarium Schweinfurthii engl. fruit (Canarium Schweinfurthii engl. long and short) essential for predicting their suitable absorption conditions was investigated at three different temperatures (35, 50, 65 °C). Increase in moisture content of the fruits was measured at one-hour interval until constant values were obtained after five successive intervals of moisture measurements. Loss of soluble constituents, textural and nutritive qualities of the rehydrated products and their thermodynamic behavior were also measured and calculated. The results obtained revealed that saturation time for 35, 50 and 65 °C of long and short varieties are 14, 18 and 40 h and 18, 22 and 36 h respectively. Rate of absorption of the fruits differ significantly (p > 0.05) with temperature and not with the variety. Water absorption rate of Canarium Schweinfurthii engl. long and short varieties are 2.71 and 2.25 kg/h respectively. The moisture bearing capacity, textural, and nutritive qualities of the reconstituted products showed no significant difference among varieties at different temperatures used. Fruits soaked at 35 °C produced reusable residual water, retained their nutritive values and soluble constituent more than other soaking temperatures studied. However, the absorption reaction is endothermic with negative entropy and Gibbs energy values were above zero. Midilli model had the best quality for describing the absorption characteristics of both Canarium Schweifurthii engl. fruits.
       
  • Tea leaf’s microstructure and ultrastructure response to low temperature
           in indicating critical damage temperature

    • Abstract: Publication date: Available online 21 December 2018Source: Information Processing in AgricultureAuthor(s): Yongzong Lu, Yongguang Hu, Richard L. Snyder, Eric R. Kent To find out the critical damage temperature of tea leaf, a new method of subzero treatment was provided by fitting the air temperature data from six heavy frost events. Furthermore, the study explored the characteristics of Fuding Dabai tea plant response to low temperature stress of 2, 0, −2, −4, −8, −10 and −15 °C by observing the microstructure and ultrastructure changes of the leaves. All samples were collected in an ambient temperature of 8.6 °C which served as control. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to observe the microstructure and ultrastructure of stomata and mesophyll. SEM observation results indicated that stomata of tea leaves have an obvious low temperature stress when the temperature was lower than −2 °C. The extent of opening of the stomata increased and enhanced guard cell protection of tea leaves against cold injury. However, dehydration, shrinkage and deformation of cells occurred as the temperature decreased from −2 °C to −15 °C. TEM observations showed that the cell nucleus, cell walls, chloroplasts and mitochondria all had normal structure at a temperature of 8.6 °C where the membrane and granum lamella were clearly visible. As the temperature decreased to −2 °C, the membrane system of tea leaf was the first to be damaged and the cell walls became fuzzy. Therefore, the leaf microstructure and ultrastructure showed obvious changes at −2 °C, which might define the critical damage temperature for freeze damage of Fuding Dabai tea. Control strategy based this critical damage temperature is useful for wind machine frost protection in tea fields within the Yangtze River region.
       
  • Comparative study of green peas using with blanching & without blanching
           techniques

    • Abstract: Publication date: Available online 12 December 2018Source: Information Processing in AgricultureAuthor(s): Om Prakash Pandey, Bimal Kumar Mishra, Ashok Misra This paper attempts to analyze the kinetics involved in the drying of green peas in ‘with blanching’ and ‘without blanching’ techniques. Blanching by hot water mixed with a solution of citric acid (0.1–0.2 mg/ml) at 70 °C–100 °C is the treatment provided to the samples of green peas. Experimental analysis shows that the moisture content in green peas of three different sizes is reduced at different temperatures and demonstrates the effect of the rate of drying of the moisture content. Under different diameters and temperatures, the parameters are analyzed using ‘with blanching’ and ‘without blanching’ techniques. It is observed that ‘with blanching’ process plays a significant role in the reduction of moisture content under different temperatures and diameters in a lesser time as compared to ‘without blanching’. The operative activation energy and moisture diffusivity are described by using Fick’s law of diffusion. The calculation of effective moisture diffusivity can be done via the utilization of slope. The drying data is subjected to two models of mathematical nature: Simple Exponential model, and Page model. The performance of these models is examined by means of comparing the coefficient of determination (R2), chi-square (χ2) and root mean square error (RMSE) between the experimental and forecasted value of moisture ratio obtained ‘with blanching’ and ‘without blanching’. The experimental data was seen to be in accordance with the Page model. The comparisons of energy consumption, energy efficiency and also the cost of the drying processes for ‘with blanching’ and ‘without blanching’ have been accomplished to optimize and reduce process condition and the cost of the process, respectively.
       
  • Feasibility of implementation of intelligent simulation configurations
           based on data mining methodologies for prediction of tractor wheel slip

    • Abstract: Publication date: Available online 9 December 2018Source: Information Processing in AgricultureAuthor(s): S.M. Shafaei, M. Loghavi, S. Kamgar This paper deals with implementation of intelligent simulation configurations for prediction of tractor wheel slip in tillage operations. The effects of numeral variables of forward speed (2, 4, and 6 km/h) and plowing depth (10, 20, and 30 cm), and nominal variable of tractor driving mode (two-wheel drive (2WD) and four-wheel drive (4WD)) on tractor rear wheel slip were intelligently simulated utilizing data mining methodologies of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Neuro-fuzzy potential of the ANFIS simulation framework against neural ability of the ANN simulation framework was apprised. Results confirmed higher efficiency of the best configuration of the ANFIS simulation framework with satisfactory statistical performance criteria of coefficient of determination (0.981), root mean square error (1.124%), mean absolute percentage error (1.515%), and mean of absolute values of prediction residual errors (1.135%) than that of the ANN simulation framework. Physical perception obtained from the ANFIS simulation results demonstrated that the wheel slip increased nonlinearly with increment of forward speed and plowing depth, while it decreased as tractor driving mode changed from the 2WD to 4WD. Therefore, the best configuration of the ANFIS based intelligent simulation framework implemented in this study can be used for further relevant studies of tractor rear wheel slip as a reference.Graphical abstractGraphical abstract for this article
       
  • Influence of temperature and light gradient on leaf arrangement and
           geometry in cucumber canopies: Structural phenotyping analysis and
           modelling

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): Tingting Qian, Xiuguo Zheng, Xinyu Guo, Weiliang Wen, Juan Yang, Shenglian Lu Accurate structural phenotyping analysis is essential to understand plant architectural adaptation strategy to environment change. The aim of this study was to analyze leaf arrangement and geometry influenced by azimuthally generated light gradient; and to simulate static and heterogeneous cucumber canopies using regression equations by considering more geometric parameters. Three continuous measurements of structural organ parameters were obtained to fit the organ initiation and expansion curves. Four measurements with three density treatments were obtained to validate model accuracy. To describe leaf distribution and orientation characteristics in more detail, azimuth and elevation models were introduced into canopy structure modelling. Leaf distribution frequency was simulated based on leaf area index and solar elevation angle while leaf elevation was simulated based on leaf azimuth and acropetal phytomer number. This study provides an important basis for structural phenotyping analysis of cucumber canopy, which is essential for more accurate functional-structural modelling in the future.
       
  • Using Support Vector Machines and neural networks to classify Merlot wines
           from South America

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): Nattane Luíza Costa, Laura Andrea García Llobodanin, Inar Alves Castro, Rommel Barbosa Wines with a clear geographical origin are an issue of interest for consumers and food industries. This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geographical origin. A group of samples from Argentina (n = 17), Brazil (n = 12), Chile (n = 48), and Uruguay (n = 6) was analyzed. Twenty chemical compounds were determined by high-performance liquid chromatography (HPLC). These compounds include antioxidant activity, total polyphenols, total anthocyanins, individual anthocyanins and color. Four binary classification problems were performed (Brazil versus non-Brazil, Argentina versus non-Argentina, Chile versus non-Chile, and Uruguay versus non-Uruguay) to investigate the geographic characteristics of each country. Through the evaluation of binary classifications in our dataset it was possible to identify the main variables (chemical compounds) that discriminate between the countries. We used the following algorithms: Synthetic Minority over-sample Technique and under-sampling to balance the dataset of each classification approach, the Relief algorithm to obtain a variable importance ranking and the classifiers Support Vector Machines, Multilayer Perceptron and Radial Basis Function Network with dynamic decay adjustment. SVM model obtained the highest performance measures among the classifiers for each dataset (93.73% of accuracy for the Brazil versus non-Brazil, 91.18% for the Argentina versus non-Argentina, 79.16% for the Chile versus non-Chile, and 91.67% for the Uruguay versus non-Uruguay classification). These accuracies were achieved by the search of the possible variable subsets according to Relief for each classification approach. We found that some variables, such as DPPH, wine color and individual anthocyanins, are among the most important variables in the characterization of Merlot wines.
       
  • Micro-sonic sensor technology enables enhanced grass height measurement by
           a Rising Plate Meter

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): D. McSweeney, N.E. Coughlan, R.N. Cuthbert, P. Halton, S. Ivanov Globally, the Rising Plate Meter (RPM) is a device used to measure compressed sward height, to enable estimation of herbage mass. Despite improved farm management practices aided by a variety of technological advances, the standard design of a RPM has remained relatively unchanged. Recently, however, a RPM utilising a micro-sonic sensor, with digital data capture capability via a Bluetooth communications link to a smart device application, has been developed. Here, we assess the comparable ability of both a standard cumulative ratchet counter RPM and the micro-sonic sensor RPM, to accurately and precisely measure fixed heights. Moreover, as correct allocation of grazing area requires accurate geolocation positioning, we assess the associated GPS technology. The micro-sonic sensor RPM was significantly more accurate for height capture than the cumulative ratchet counter RPM. Overall, across all heights, the cumulative ratchet counter RPM underestimated height by 7.68 ± 0.06 mm (mean ± SE). Alternatively, the micro-sonic sensor RPM overestimated height by 0.18 ± 0.08 mm. In relation to a practical applications, these discrepancies can result in an under- and overestimation of dry matter yield by 13.71% and 0.32% kilograms per hectare, respectively. The performance of the on-board GPS did not significantly differ from that of a tertiary device. Overall, the wireless technology, integrated mapping, and decision support tools offered by the innovative micro-sonic sensor RPM provides for a highly efficacious grassland management tool.
       
  • Image based leaf segmentation and counting in rosette plants

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): J. Praveen Kumar, S. Domnic This paper proposes an efficient method to extract the leaf region and count the number of leaves in digital plant images. The plant image analysis plays a significant role in viable and productive agriculture. It is used to record the plant growth, plant yield, chlorophyll fluorescence, plant width and tallness, leaf area, etc. frequently and accurately. Plant growth is a major character to be analyzed among these plant characters and it directly depends on the number of leaves in the plants. In this paper, a new method is presented for leaf region extraction from plant images and counting the number of leaves. The proposed method has three steps. The first step involves a new statistical based technique for image enhancement. The second step involves in the extraction of leaf region in plant image using a graph based method. The third step involves in counting the number of leaves in the plant image by applying Circular Hough Transform (CHT). The proposed work has been experimented on benchmark datasets of Leaf Segmentation Challenge (LSC). The proposed method achieves the segmentation accuracy of 95.4% and it also achieves the counting accuracy of −0.7 (DiC) and 2.3 ( DiC ) for datasets (A1, A2 and A3), which are better than the state-of-the-art methods.
       
  • Prediction of the diet nutrients digestibility of dairy cows using
           Gaussian process regression

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): Qiang Fu, Weizheng Shen, Xiaoli Wei, Ping Zheng, Hangshu Xin, Chunjiang Zhao In order to effectively evaluate the diet nutritional value of dairy cows, it is essential to accurately predict the diet nutrients digestibility (DND). Conventional predicting DND methods are usually based on the least squares linear regression analysis (LS-LRA), which often relies on a large amount of training samples to accomplish reliable predictions. However, in real-world applications, it is often extremely difficult, costly and time-consuming to obtain a large number of measured samples, especially for the DND prediction of dairy cows. This paper applies a Gaussian process regression (GPR) technique to predict the DND indicators of dairy cows in small samples. To evaluate prediction accuracy effectively, we compared the GPR technique with the LS-LRA, radial basis function artificial neural network (RBF-ANN), support vector regression (SVR) and least squares support vector regression (LS-SVR) methods, using the required sample data obtained from actual digestion experiments. The prediction results indicate that the GPR technique is superior to other conventional methods (especially the LS-LRA method) in predicting the main DND indicators of dairy cows such as dry matter digestibility (DMD), organic matter digestibility (OMD), neutral detergent fiber (NDFD), acid detergent fiber (ADFD), and crude protein digestibility (CPD). It is worth mentioning that the developed GPR-based prediction technique is more suitable for the prediction problems with small samples, which is often the case in the prediction of DND indicators of dairy cows, and then more coincide with actual needs.
       
  • Textural, color and sensory attributes of peanut kernels as affected by
           infrared roasting method

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): Hadi Bagheri, Mahdi Kashaninejad, Aman Mohammad Ziaiifar, Mehran Aalami Roasting is one of the widespread methods for processing of nuts that significantly enhances the flavor, color, texture and appearance of products. In this research, the response surface methodology was used to optimize the roasting process over a range of infrared power (250–450 W) and roasting times (10–30 min). The moisture content, color parameters (L*, a*, b *and total color difference (ΔE)), textural characteristics (hardness and compressive energy), energy consumption and sensory evaluation (total acceptation) were determined after roasting and modeled by response surface methodology (RSM). Increasing in roasting IR power and time caused increasing in the energy consumption, a*, b* and ΔE values. The L* value, moisture content, hardness and compressive energy also decreased with increasing roasting IR power and time. The full quadratic model developed by RSM adequately described the changes in the b* value, moisture content and hardness. The result of RSM analysis showed that all color and textural parameters could be used to monitor the roasting of peanut kernels in an infrared roaster, while application of RSM for developing a predictive model that described the total acceptance changes during roasting of peanut kernels was not successful. To obtain the desired color, moisture, texture and acceptation, the optimum roasting range for production of snack was determined as 370 W for 20 min.
       
  • An equine disease diagnosis expert system based on improved reasoning of
           evidence credibility

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): Hongyan Gao, Guimiao Jiang, Xiang Gao, Jianhua Xiao, Hongbin Wang In China, there is a troubling shortage of well-trained equine veterinarians, leaving the needs of many equine farmers unmet. This is especially true with respect to the diagnosis of equine diseases. To solve this shortcoming, an equine disease diagnosis expert system was developed. For the aspect of knowledge representation, the structure of equine disease diagnosis knowledge was analyzed using an ontology system. Next, the clinical signs were described using an object-attribute-value (O-A-V) format, and the knowledge representation was then expressed using production rules. With respect to the reasoning mechanism, the weights of the clinical signs and promoted confidence factors (PCF) were combined to express information and rules pertaining to clinical signs with an associated level of uncertainty. The model was established based on improved reasoning of evidence credibility. Finally, using the ASP.Net platform and the SQL Server 2008 database, the equine disease diagnosis expert system based on the B/S structure has been developed, and is capable of reliably diagnosing 40 of the most common equine diseases. A functional evaluation of the system was conducted, and the diagnostic accuracy was observed to be 88%. This study demonstrates a bright prospect for the popularization and application of the system through continuous system maintenance and knowledge-based updates.
       
  • An adaptive segmentation method combining MSRCR and mean shift algorithm
           with K-means correction of green apples in natural environment

    • Abstract: Publication date: Available online 7 December 2018Source: Information Processing in AgricultureAuthor(s): Sashuang Sun, Huaibo Song, Dongjian He, Yan Long During the recognition and localization process of green apple targets, problems such as uneven illumination, occlusion of branches and leaves need to be solved. In this study, the multi-scale Retinex with color restoration (MSRCR) algorithm was applied to enhance the original green apple images captured in an orchard environment, aiming to minimize the impacts of varying light conditions. The enhanced images were then explicitly segmented using the mean shift algorithm, leading to a consistent gray value of the internal pixels in an independent fruit. After that, the fuzzy attention based on information maximization algorithm (FAIM) was developed to detect the incomplete growth position and realize threshold segmentation. Finally, the poorly segmented images were corrected using the K-means algorithm according to the shape, color and texture features. The users intuitively acquire the minimum enclosing rectangle localization results on a PC. A total of 500 green apple images were tested in this study. Compared with the manifold ranking algorithm, the K-means clustering algorithm and the traditional mean shift algorithm, the segmentation accuracy of the proposed method was 86.67%, which was 13.32%, 19.82% and 9.23% higher than that of the other three algorithms, respectively. Additionally, the false positive and false negative errors were 0.58% and 11.64%, respectively, which were all lower than the other three compared algorithms. The proposed method accurately recognized the green apples under complex illumination conditions and growth environments. Additionally, it provided effective references for intelligent growth monitoring and yield estimation of fruits.
       
  • Impact of the Basil and Balangu gums on physicochemical properties of part
           baked frozen Barbari bread

    • Abstract: Publication date: Available online 6 December 2018Source: Information Processing in AgricultureAuthor(s): Toktam Hejrani, Zahra Sheikholeslami, S. Ali Mortazavi, Mahdi Karimi, Amir Hosesein Elhamirad Part baking of bread and frozen storage as the new methods have attracted a lot of attention due to an increase in shelf life and the availability of fresh bread at any time. Replacing traditional additives with natural gums such as plant gum for producing bread with long shelf life is considered as a major technological challenge in bakery industry. The present study aimed to evaluate the effects of 0, 0.3%, and 0.5% concentrations of plant gums including Basil and Balangu, compared to guar gum at 0.4% on the physicochemical properties such as specific volume, extensibility, hardness, and color parameter, as well as sensory properties of part baked frozen bread. The results indicated adding gums to bread decreased in hardness and increased in specific volume, extensibility, color parameter and sensory properties. Based on the comparison between plant gums and guar, Basil and Balangu could improve volume, porosity, and sensory score more than guar against the guar which was more effective on moisture content and firmness of Barbari bread. The best results were obtained in the interaction between Basil and Balangu gums on 0.5% concentration.Graphical abstractFig. 1. Firmness of PBF Barbari bread after 15 day.Graphical abstract for this article
       
  • A segmentation method for processing greenhouse vegetable foliar disease
           symptom images

    • Abstract: Publication date: Available online 6 December 2018Source: Information Processing in AgricultureAuthor(s): Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhongfu Sun Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images. In order to achieve robust segmentation, a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper. The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure. Firstly, a coarse decision tree was built by the CART (Classification and Regression Tree) algorithm with a feature subset. The feature subset consisted of color features that was selected by Pearson’s Rank correlations. Then, the coarse decision tree was optimized by pruning. Using the optimized decision tree, segmentation of disease symptom images was achieved by conducting pixel-wise classification. In order to evaluate the robustness and accuracy of the proposed method, an experiment was performed using greenhouse cucumber downy mildew images. Results showed that the proposed method achieved an overall accuracy of 90.67%, indicating that the method was able to obtain robust segmentation of disease symptom images.
       
  • A prediction model of NH3 concentration for swine house in cold region
           based on Empirical Mode Decomposition and Elman neural network

    • Abstract: Publication date: Available online 6 December 2018Source: Information Processing in AgricultureAuthor(s): Weizheng Shen, Xiao Fu, Runtao Wang, Yanling Yin, Yan Zhang, Udaybeer Singh, Bilegtsaikhan Lkhagva, Jian Sun In order to improve the accuracy and reliability of ammonia (NH3) concentration prediction, which can provides a support to the ventilation control strategy, so as to reduce the impact of NH3 on the health and productivity of swine, this paper proposed an NH3 concentration prediction method based on Empirical Mode Decomposition (EMD) and Elman neural network modelling. The NH3 concentration and other four environmental parameters including temperature, humidity, carbon dioxide and light intensity were decomposed into several different time-scale intrinsic mode functions (IMFs). Then, the Elman neural network prediction model was used to predict each IMF. The predicted NH3 was obtained by reconstructing all the IMFs by EMD. The results show that for the proposed method, the determination coefficient between the predicted and real measured value is 0.9856, the Mean Absolute Error is 0.7088 ppm, the Root Mean Square Error is 0.9096 ppm, and the Mean Absolute Percentage Error is 0.41%. Compared with the Elman neural network, the proposed method has a good improvement in the accuracy, and provide effective parameters for the environmental monitoring of the swine house and the regulation of the NH3 concentration.
       
  • Forecasting soil temperature at multiple-depth with a hybrid artificial
           neural network model coupled-hybrid firefly optimizer algorithm

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Saeed Samadianfard, Mohammad Ali Ghorbani, Babak Mohammadi Forecasting soil temperature at multiple depths is considered to be a core decision-making task for examining future changes in surface and sub-surface meteorological processes, land–atmosphere energy exchange, resilient agricultural systems for improved crop health and eco-environmental risk assessment. The aim of this paper is to estimate monthly soil temperature (ST) at multiple depth: 5, 10, 20, 50 and 100 cm with a hybrid multi-layer perceptron algorithm integrated with the firefly optimizer algorithm (MLP-FFA). To develop the hybrid MLP-FFA model, the monthly ST and relevant meteorological variables for the city of Adana (Turkey) are collated for the period of 2000–2007. Construction of hybrid MLP-FFA model is drawn upon a limited set of predictors, denoted as soil depth, periodicity (or the respective month), air temperature, pressure and solar radiation, while the objective variable for MLP-FFA model is the forecasted ST at multiple depths. To the evaluate MLP-FFA, statistical metrics applied to test the model’s performance are: the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean bias error (MBE) where the sign of the difference is also considered. In conjunction with statistical metrics, a Taylor diagram is utilized to visualize the degree of similarity between the observed and forecasted soil moisture. In terms of the forecasted results, the hybrid MLP-FFA model is seen to outperform the standalone MLP model. The optimal MLP-FFA is attained for soil temperature forecasting at a depth of 20 cm (RMSE, MAPE of 0.546 °C, 2.40%) whereas the optimal MLP is attained for soil temperature forecasting at a depth of 50 cm (RMSE of 0.544 °C, 2.21%). Conclusively, the study advocates through statistical metrics attained the better utility of the hybrid MLP-FFA hybrid model. Given its superior performance, it is ascertained that the hybrid MLP model integrated with Firefly optimizer is a qualified ancillary tool that can be applied to generate precise soil temperature forecasts at multiple soil depths.
       
  • Mechanized technologies for scaffolding cultivation in the kiwifruit
           industry: A review

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Longtao Mu, Haozhou Liu, Yongjie Cui, Longsheng Fu, Yoshinori Gejima The success of organic and green agricultural fruit production depends on quality and cost. As the kiwifruit industry becomes ever more commercialized, it is in the interests of the industry to mechanize production, which can promote industrialization and improve industrial value and market prospects. Currently, New Zealand, Italy, Chile, and China carry out research into the mechanism of kiwifruit production. This review describes in detail the current state of the art of pollination, harvesting and grading equipment, including detection and identification, non-destructive end effector, harvesting robots and grading devices. Process technologies that include artificial pollination, harvest mechanization, grading and standardization of production problems are analysed and compared. These problems directly affect the quality of kiwifruit products. Finally, to solve the various problems that the kiwifruit industry experiences, it is necessary to accelerate the development of mechanized kiwifruit production, realize the mechanization of information acquisition and standardization in order to advance precision agriculture and agricultural wisdom for the future. Mechanization of the kiwifruit industry must adapt to adjustments in how China’s economic structure develops.
       
  • Tractor path tracking control based on binocular vision

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Shuo Zhang, Yunyi Wang, Zhongxiang Zhu, Zhen Li, Yuefeng Du, Enrong Mao In the process of field operation management, determining how to accurately realize crop row identification and path tracking control is an essential part of tractor automatic navigation. According to the linear operation in the process of cotton field management, the tractor path tracking control system was designed based on binocular vision and the pure pursuit model. A new crop row detection method based on the Census transform and the PID control algorithm with dead zone were used. First, the upper computer software was developed by C++ with the functions of parameter setting and image acquisition and processing. Second, an automatic steering controller was developed based on microprocessor MC9S12XS128 of Freescale. The control program was developed based on modular design using CodeWarrior during development of the PID-based automatic steering control strategy. Finally, a field experiment platform of tractor path tracking control was built, and field experiments under the actual cotton were conducted. The optimal visibility distance was determined by several previous experiments. When the tractor tracks the path with the optimal visibility distance in the growth environment of actual cotton crops, the mean absolute deviation of course angle was 0.95°, and the standard deviation was 1.26°; the mean absolute deviation of lateral position was 4.00 cm, and the standard deviation was 4.97 cm; the mean absolute deviation of front wheel angle was 2.99°, and the standard deviation was 3.67°. The experimental results show that (1) the crop row detection method based on Census transform can identify the crop line and plan the navigation path well, and (2) the tractor path tracking control system based on binocular vision has good stability and high control precision; thus, the control system can realize the automatic path tracking control of cotton line operation and meets the agricultural requirements of cotton field operation management.
       
  • Image enhancement for crop trait information acquisition system

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Zhibin Wang, Kaiyi Wang, Feng Yang, Shouhui Pan, Yanyun Han, Xiangyu Zhao Collecting images using portable devices is an effective and convenient method for acquiring crop trait information. Because of uncertain environmental conditions in the field, enhancement is necessary to improve the visual quality of images. With this aim, here we propose an adaptive image enhancement method based on guided filtering. Our method automatically calculates the enhancement weights of the detail in an image according to the distribution characteristics of the illumination intensity of a crop image, so as to adaptively adjust the contrast of the image. To verify the effectiveness of the proposed algorithm, we performed enhancement experiments on 50 images of four kinds of cucumber leaf tissues, namely, leaves infected with target spot, powdery mildew, and downy mildew, and healthy leaves. The results showed that our proposed method substantially improved the visual quality of the images. Moreover, the mean ratios of the contrast to color difference obtained using the proposed method were higher than the mean ratios obtained using five conventional enhancement methods. We consider the proposed method for image enhancement will be a valuable addition to the crop trait information acquisition system (http://ebreed.com.cn/).
       
  • High-throughput phenotyping by applying digital morphometrics and
           fluorescence induction curves in seeds to identifying variations: A case
           study of Annona (Annonaceae) species

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Montcharles S. Pontes, Carlos V. Montefusco-Pereira, Biswapriya B. Misra, Howard L. Ribeiro-Junior, Daniela E. Graciano, Jaqueline S. Santos, Michele A.S. Nobrega, Shaline S.L. Fernandes, Anderson R.L. Caires, Etenaldo F. Santiago Differences in physical and structural characteristics of seeds may indicate variability within and between plant populations. In the present study, we performed a close characterization of dimension, shape, and tegument delayed chlorophyll fluorescence in seeds obtained from three species of the genus Annona (Annonaceae), i.e., Annona coriacea, A. montana, A. squamosa. Results showed that studied seeds may be sorted as scalene ellipsoids expressing low values for the seed sphericity. The morphological estimates suggested differences in seed shape for all species. A high correlation was observed between surface area and volume (r2 > 99%) for all the three species suggesting that in addition to structural shape. In addition, we also observed very high positive correlations (Rho = 1.000, p 
       
  • Transition towards sustainability in agriculture and food systems: Role of
           information and communication technologies

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Hamid El Bilali, Mohammad Sadegh Allahyari Food sustainability transitions refer to transformation processes necessary to move towards sustainable food systems. Digitization is one of the most important ongoing transformation processes in global agriculture and food chains. The review paper explores the contribution of information and communication technologies (ICTs) to transition towards sustainability along the food chain (production, processing, distribution, consumption). A particular attention is devoted to precision agriculture as a food production model that integrates many ICTs. ICTs can contribute to agro-food sustainability transition by increasing resource productivity, reducing inefficiencies, decreasing management costs, and improving food chain coordination. The paper also explores some drawbacks of ICTs as well as the factors limiting their uptake in agriculture.
       
  • Theoretical and experimental research on effect of fins attachment on
           operating parameters and thermal efficiency of solar air collector

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Ali Daliran, Yahya Ajabshirchi Flat plate air collector is a type of heat exchanger which absorbs radiated solar energy and exchanges it to heat. According to low efficiency of this type of collectors, a suitable approach is investigated in this paper so as to increase thermal performance of the system. Thermal efficiency of solar collector for two models C1 (without fins) and C2 (with fins) both of 1 m2 surface area with forced convection flow is studied theoretically and experimentally. Rectangular fins are attached over back board in air channel to create turbulence in air flow. In order to measure air temperature, 17 thermal sensors (LM35) are exploited, among which 11 were mounted on absorber plate and the remaining 6 on the back board. Physical design of experimental model are performed in Solidwork and programming of theoretical work in Matlab software. In this research, a fan with constant mass flow rate of 0.033 kg/s is utilized for producing air flow. Results indicate that applying fins in air channel not only reduces Nusselt number from 19.67 to 16.23, but also due to decreasing hydraulic diameter and creating air flow turbulence, causes increase of heat transfer coefficient from absorber plate to air flow and consequently reduction of total heat loss and higher outlet air temperatures. Average difference of outlet air temperature between experimental and theoretical results for both collectors (C1 and C2) was recorded respectively as 7.6% and 9.4%. Thermal efficiency was respectively calculated 30% and 51% for experimental types with and without fins and 33% and 55% for those of theoretical work which generally seem reasonable.
       
  • Decomposition of influencing factors and its spatial-temporal
           characteristics of vegetable production: A case study of China

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Jieqiong Wang, Zetian Fu, Biao Zhang, Fei Yang, Lingxian Zhang, Bo Shi China is the largest producer and consumer of vegetables, its vegetable industry is playing an important role in the domestic agricultural sector and global vegetable export market. It is important to promote the long-term sustainable development of Chinese vegetable industry for food security and quality of people’s lives. To find out the intrinsic way to promote the development of Chinese vegetable industry, this paper analyzed the influencing factors of Chinese vegetable production by utilizing the LMDI method and demonstrated the spatial-temporal characteristics of vegetable production through application of the ArcGIS spatial autocorrelation analysis method. The results showed that the influencing factors of vegetable production were the cultivated land area, multiple cropping index, vegetable planting proportion and vegetable yield per hectare in China. The major driving factor had changed from vegetable planting proportion to vegetable yield per hectare. The influencing degrees of factors on vegetable production are different in different regions, regionalization is therefore a major feature of Chinese vegetable production. The government should take production technology, regionalization-driven effect, and marketing integration into consideration to promote the development of Chinese vegetable industry.
       
  • Development of a method for condensation rate measurement on flat surfaces

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Jingjing Han, Huiqing Guo Condensation on greenhouse interior surfaces plays an important role in reducing indoor air humidity. There is no standard method to measure condensation rate in greenhouses or in any other buildings. In this study, a commercially available leaf wetness sensor was calibrated in an environment chamber under different room temperature and RH conditions, which included five temperatures of 18, 20, 22, 24, and 26 °C, and five RH levels of 40, 55, 65, 75, and 85%. The sensor surface temperature was maintained the same as the room temperature. Room temperature water was sprayed on the sensor surface, simulating condensate. The voltage output of the sensor changed due to varying amounts of condensate on the sensor surface. The amount of condensate on the sensor surface was divided into five groups from 0 to 0.5 g (or 0–0.015 g per square centimeter of sensor surface area) with an interval of 0.1 g. The statistical analysis showed that both sensor temperature and indoor RH had no significant effect on the sensor voltage output. The voltage output was solely determined by the amount of condensate mass on the sensor surface. A linear regression model was developed between the voltage output and the amount of condensate. This tool is considered as a breakthrough of technology for condensation rate measurement on greenhouse interiors surface, or on any other surfaces with condensation. Anyone can use this sensor and the development relationship for measuring condensation rate as the sensor is not pricy and the method is easy to use, thus the method should be widely used as a standard method.
       
  • Apple fruit size estimation using a 3D machine vision system

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): A. Gongal, M. Karkee, S. Amatya Estimation of fruit size in tree fruit crops is essential for selective robotic harvesting and crop-load estimation. Machine vision systems for fruit detection and localization have been studied widely for robotic harvesting and crop-load estimation. However, only a few studies have been carried out to estimate fruit size in orchards using machine vision systems. This study was carried out to develop a machine vision system consisting of a color CCD camera and a time-of-flight (TOF) light-based 3D camera for estimating apple size in tree canopies. As a measure of fruit size, the major axis (longest axis) was estimated based on (i) the 3D coordinates of pixels on corresponding apple surfaces, and (ii) the 2D size of individual pixels within apple surfaces. In the 3D coordinates-based method, the distance between pairs of pixels within apple regions were calculated using 3D coordinates, and the maximum distance between all pixel pairs within an apple region was estimated to be the major axis. The accuracy of estimating the major axis using 3D coordinates was 69.1%. In the pixel-size-based method, the physical sizes of pixels were estimated using a calibration model developed based on pixel coordinates and the distance to pixels from the camera. The major axis length was then estimated by summing the size of individual pixels along the major axis of the fruit. The accuracy of size estimation increased to 84.8% when the pixel size-based method was used. The results showed the potential for estimating fruit size in outdoor environments using a 3D machine vision system.
       
  • Comparison of Sick and Hokuyo UTM-30LX laser sensors in canopy detection
           for variable-rate sprayer

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Hui Liu, Bin Gao, Yue Shen, Fida Hussain, Destaw Addis, Cheng Kai Pan Accurate acquisition of canopy structures, sizes and shapes of plants are significant in providing technical data for variable-rate precision spraying and plant growth estimation in modern agriculture. This research work proposes a comparative analysis of two mainstream brands of laser sensor scanners for canopy detection. A Hokuyo UTM-30LX laser sensor and a Sick LMS151-10100 laser sensor was used to detect spray targets including two artificial trees and a cuboid foam box, respectively. Two data acquisition and storage algorithms based on C++ language have been developed to collect real-time data from the given targets based on the sensors. A 3-dimentional image reconstruction algorithm was proposed to construct the detection targets using MATLAB software. In this experiment, the detection distances between laser sensors and targets range from 1.8 to 2.2 m, and the traveling speed of laser sensors ranges from 0.5 to 2.0 m/s, and the size of trees 2.15 × 1.24 × 0.70 m3 have been taken for verification of proposed method. The detection accuracies of both laser sensors have been compared by utilizing the given targets under indoor laboratory conditions, and detection accuracies of 3-dimentional reconstruction images of the target are analyzed by the root-mean-square error (RMSE), the coefficient of variation (CV) and edge similarity score (ESS). The experimental results show that the laser sensors have different detection accuracies under the same experimental conditions, and has different detection accuracy under different experimental conditions. The LMS151-10100 laser sensor is more accurate and more suitable for detection in the case of fast detection speed. However, both sensors have the capability to measure the targets accurately and can be applied for the detection of trees in the area of variable-rate precision spraying.
       
  • Modeling of moisture loss kinetics and color changes in the surface of
           lemon slice during the combined infrared-vacuum drying

    • Abstract: Publication date: December 2018Source: Information Processing in Agriculture, Volume 5, Issue 4Author(s): Fakhreddin Salehi, Mahdi Kashaninejad Color is one of the most important appearance attributes of food materials, since it influences consumer acceptability. In this study the effects of combined infrared-vacuum drying on the drying kinetics, moisture diffusivity, surface changes (shrinking) and color changes kinetics of lemon slices were investigated. Both the infrared lamp power and vacuum pressure influenced the drying time of lemon slices. The regression results showed that the quadratic model satisfactorily described the drying behavior of lemon slices with highest R value and lowest SE values. The effective moisture diffusivity increased from 2.92 × 10−10 and 1.58 × 10−9 m2/s when the infrared lamp power was increased from 300 to 400 W. The colour parameters L∗ (lightness), a∗ (redness/greenness), b∗ (yellowness/blueness), and ΔE (total colour difference) were used to estimate colour changes during drying. L∗, a∗ and b∗ values of dried lemon increased during drying. The rise in infrared power has a negative effect on the ΔE and with the increase of infrared radiation power it was increased. Different kinetic models were used to fit the experimental data and the results revealed that the power model was the most suitable to describe the color change intensity (ΔE).
       
 
 
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