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Publisher: Elsevier   (Total: 3162 journals)

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Showing 1201 - 1400 of 3162 Journals sorted alphabetically
Growth Factors and Cytokines in Health and Disease     Full-text available via subscription   (Followers: 1)
Growth Hormone & IGF Research     Hybrid Journal   (Followers: 16, SJR: 1.059, CiteScore: 2)
Gynecologic Oncology     Hybrid Journal   (Followers: 25, SJR: 2.339, CiteScore: 4)
Gynecologic Oncology Reports     Open Access   (Followers: 10, 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: 1, SJR: 4.16, CiteScore: 2)
Handbook of Defense Economics     Full-text available via subscription   (Followers: 1)
Handbook of Development Economics     Full-text available via subscription   (Followers: 7)
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: 8)
Handbook of Economic Forecasting     Full-text available via subscription   (Followers: 1)
Handbook of Economic Growth     Full-text available via subscription   (Followers: 2)
Handbook of Environmental Economics     Full-text available via subscription   (Followers: 2)
Handbook of Experimental Economics Results     Full-text available via subscription   (Followers: 4)
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: 10)
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: 2)
Handbook of Industrial Organization     Full-text available via subscription   (Followers: 3)
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: 6, 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: 5)
Handbook of Natural Resource and Energy Economics     Full-text available via subscription   (Followers: 3)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 4)
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: 3)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 6)
Handbook of Public Economics     Full-text available via subscription  
Handbook of Regional and Urban Economics     Full-text available via subscription   (Followers: 1)
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: 1)
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: 6)
Handbook of the Economics of Giving, Altruism and Reciprocity     Full-text available via subscription  
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: 5, 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: 42, 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: 10, 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: 10, 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: 1, 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: 42, SJR: 0.237, CiteScore: 1)
Hong Kong Physiotherapy J.     Open Access   (Followers: 12, SJR: 0.183, CiteScore: 0)
Hormigón y Acero     Full-text available via subscription  
Hormones and Behavior     Hybrid Journal   (Followers: 12, 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: 17, SJR: 0.856, CiteScore: 2)
Human Movement Science     Hybrid Journal   (Followers: 15, SJR: 0.756, CiteScore: 2)
Human Pathology     Hybrid Journal   (Followers: 26, SJR: 1.304, CiteScore: 3)
Human Pathology : Case Reports     Open Access   (Followers: 1, SJR: 0.136, CiteScore: 0)
Human Resource Management Review     Hybrid Journal   (Followers: 50, 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: 73, 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: 53, 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: 12, 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: 4, 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   (SJR: 0.273, CiteScore: 0)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 3)
Industrial Crops and Products     Hybrid Journal   (Followers: 5, 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: 16)
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: 5, 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: 4, SJR: 0.63, CiteScore: 1)
Information Fusion     Hybrid Journal   (Followers: 2, SJR: 1.832, CiteScore: 7)
Information Processing & Management     Hybrid Journal   (Followers: 282, 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: 335, 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: 5, 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: 5)
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: 28, 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: 6, SJR: 1.051, CiteScore: 2)
Intl. Economics     Hybrid Journal   (Followers: 4, 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)
Intl. J. of Accounting Information Systems     Hybrid Journal   (Followers: 5, SJR: 0.399, CiteScore: 2)

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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  [3162 journals]
  • Transition towards sustainability in agriculture and food systems: role of
           information and communication technologies

    • Abstract: Publication date: Available online 2 July 2018Source: Information Processing in AgricultureAuthor(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.
       
  • Forecasting soil temperature at multiple-depth with a hybrid artificial
           neural network model coupled-hybrid firefly optimizer algorithm

    • Abstract: Publication date: Available online 27 June 2018Source: Information Processing in AgricultureAuthor(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.
       
  • Decomposition of influencing factors and its spatial-temporal
           characteristics of vegetable production: A case study of China

    • Abstract: Publication date: Available online 19 June 2018Source: Information Processing in AgricultureAuthor(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: Available online 15 June 2018Source: Information Processing in AgricultureAuthor(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.
       
  • Comparison of Sick and Hokuyo UTM-30LX Laser Sensors in Canopy Detection
           for Variable-rate Sprayer

    • Abstract: Publication date: Available online 8 June 2018Source: Information Processing in AgricultureAuthor(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: Available online 2 June 2018Source: Information Processing in AgricultureAuthor(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).
       
  • On the neurocomputing based intelligent simulation of tractor fuel
           efficiency parameters

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): S.M. Shafaei, M. Loghavi, S. Kamgar Tractor fuel efficiency parameters (TFEPs) (fuel consumption per working hour (FCWH), fuel consumption per tilled area (FCTA) and specific volumetric fuel consumption (SVFC)) were intelligently simulated. A neurocomputing based simulation strategy (adaptive neuro-fuzzy inference system (ANFIS)) was used to simulate the TFEPs. A comparison was also made between results of the best ANFIS environment and those of another neurocomputing based simulation strategy, artificial neural network (ANN). Field experiments were conducted at plowing depths of 10, 20 and 30 (cm) and forward speeds of 2, 4 and 6 (km/h) using a disk plow implement. Statistical descriptor parameters applied to evaluate simulation environments indicated that the best simulation environment of both ANFIS and ANN were able to perfectly predict the TFEPs. However, the best comprehensive ANN simulation environment with a simple architecture of 2-6-3 was easier to use than three individual ANFIS simulation environments. The ANN results revealed that simultaneous increase of forward speed from 2 to 6 (km/h) and plowing depth from 10 to 30 (cm) led to nonlinear increment of the FCWH from 5.29 to 14.89 (L/h) and nonlinear decrement of the SVFC from 2.95 to 0.67 (L/h kW). Meanwhile, forward speed increment along with plowing depth decrement resulted in nonlinear decrement of the FCTA from 28.13 to 12.24 (L/ha). Interaction of forward speed and plowing depth on the FCWH and SVFC was congruent, while it was incongruent for the FCTA. It is suggested to employ the ANN environment in developing future fuel planning schemes of tractor during tillage operations.Graphical abstractGraphical abstract for this article
       
  • Use of ultrasound to modify the pyrolyzed biomass of Pinus spp. and the
           implications for biological models

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Aislan R. Guimarães, Marcelo E. Cordeiro, Jaqueline Nicolini, Keller P. Nicolini Pine (Pinus ssp.) needle biomass (PNB) was pyrolyzed at 400 °C for 3 h and then subjected to hydrothermal treatment at the same temperature for 10 min, with and without the addition of potassium chloride (KCl). The suspensions of the materials treated hydrothermally were submitted to ultrasound for 5, 10, 20, 30 and 60 min. Diffuse reflectance UV–Vis (DRUV) spectroscopy results for the materials with variations in sonication times were obtained and the band gap energy (E) was calculated. A culture medium containing Saccharomyces cerevisiae was monitored during 30 min of exposure to different materials for the calculation of the 10% (IC10), 30% (IC30) and 50% (IC50) inhibitory concentrations. Of the samples that underwent ultrasonic treatment, the material pyrolyzed at 400 °C without the addition of potassium ions (PNB4003H60) presented the greatest inhibition of 10% of the Saccharomyces cerevisiae cultures. Of the materials without the addition of potassium, the material pyrolyzed and sonicated for 10 min (PNB4003H10) showed the best characteristics for use as a support for Saccharomyces cerevisiae organisms.Graphical abstractGraphical abstract for this article
       
  • Sunflower petals: Some physical properties and modeling distribution of
           their number, dimensions, and mass

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Amir Hossein Mirzabe, Golam Reza Chegini, Javad Khazaei Sunflower petal is one of the parts of the sunflower which has drawn attention and has several applications these days. These applications justify getting information about physical properties, mechanical properties, drying trends, etc. in order to design new machines and use new methods to harvest or dry the sunflower petals. For three varieties of sunflower, picking force of petals was measured; number of petals of each head was counted; unit mass and 1000-unit mass of fresh petals were measured and length, width, and projected area of fresh petals were calculated based on image processing technique; frequency distributions of these parameters were modeled using statistical distribution models namely Gamma, Generalized Extreme Value (G. E. V), Lognormal, and Weibull. Results of picking force showed that with increasing number of days after appearing the first petal on each head from 5 to 14 and decreasing loading rate from 150 g min−1 to 50 g min−1 values of picking force were decreased for three varieties, but diameter of sunflower head had different effects on picking force for each variety. Length, width, and number of petals of Dorsefid variety ranged from 38.52 to 95.44 mm, 3.80 to 9.28 mm and 29 to 89, respectively. The corresponding values ranged from 34.19 to 88.18 mm, 4.28 to 10.60 mm and 21 to 89, respectively for Shamshiri variety and ranged from 44.47 to 114.63 mm, 7.03 to 20.31 mm and 29 to 89 for Sirena variety. Results of frequency distribution modeling indicated that in most cases, G. E. V and Weibull distributions had better performance than other distributions.
       
  • Reproductive development of two groundnut cultivars as influenced by boron
           and light

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Md. Quamruzzaman, Md. Jafar Ullah, Md. Fazlul Karim, Nazrul Islam, Md. Jahedur Rahman, Md. Dulal Sarkar Boron is an important micronutrient that enhances reproductive growth of crops such as groundnut. Light also plays an important role for pegging of groundnut. There has been little information on the application of boron and light in groundnut in Bangladesh. A field experiment was conducted to study the effects of boron and light on the reproductive development of two groundnut cultivars. Treatments considered as two groundnut cultivars, viz., ‘Dhaka-1 (C1)’ and ‘BARI Chinabadam-8 (C2)’, three levels of boron (B), viz., 0-kg B ha−1 (B0), 1-kg B ha−1 (B1) and 2-kg B ha−1 (B2), and two levels of light, viz., normal day light (≈12-h light) and normal day light + 6-h extended red light at night (≈18-h light). Result revealed that the reproductive development, yield and shelling percentage of groundnut were markedly increased with the application of B. Due to imposition of light all of reproductive units increased but decrease number of pods and pod yield. The highest reproductive unit and yield were observed from ‘BARI Chinabadam-8’. Therefore, reproductive development could be improved by application of boron in improved cultivars (BARI Chinabadam-8) but not for under extended light.
       
  • Applied machine learning in greenhouse simulation; new application and
           analysis

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Morteza Taki, Saman Abdanan Mehdizadeh, Abbas Rohani, Majid Rahnama, Mostafa Rahmati-Joneidabad Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot regions. The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it. So the main goal of this study is to select the best method between Artificial Neural Network (ANN) and Support Vector Machine (SVM) to estimate three different variables include inside air, soil and plant temperatures (Ta, Ts, Tp) and also energy exchange in a polyethylene greenhouse in Shahreza city, Isfahan province, Iran. The environmental factors which influencing all the inside temperatures such as outside air temperature, wind speed and outside solar radiation were collected as data samples. In this research, 13 different training algorithms were used for ANN models (MLP-RBF). Based on K-fold cross validation and Randomized Complete Block (RCB) methodology, the best model was selected. The results showed that the type of training algorithm and kernel function are very important factors in ANN (RBF and MLP) and SVM models performance, respectively. Comparing RBF, MLP and SVM models showed that the performance of RBF to predict Ta, Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 indices. The range of RMSE and MAPE factors for RBF model to predict Ta, Tp and Ts were between 0.07 and 0.12 °C and 0.28–0.50%, respectively. Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups. The performance of best model (RBF) to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy.
       
  • Down image recognition based on deep convolutional neural network

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Wenzhu Yang, Qing Liu, Sile Wang, Zhenchao Cui, Xiangyang Chen, Liping Chen, Ningyu Zhang Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN). To deal with the above problems, a Deep Convolutional Neural Network (DCNN) for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN.
       
  • Manage system for internet of things of greenhouse based on GWT

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Jizhang Wang, Jinsheng Zhou, Rongrong Gu, Meizheng Chen, Pingping Li In order to fit the different demands for the internet of things system of greenhouse environment monitoring and control, the greenhouse environment monitoring and control management system based on Google Web Toolkit (GWT) was developed. Using remote method call (RPC) AJAX as the communication method between browser and web server, the system realized the functions such as: configuration of acquisition and control parameters, the adaptive match of database between gateway and server, the adaptive diagnosis of monitoring parameters, the warning of monitoring parameters, the adaptive generation of interface, and so on. The functions of the system was tested the results shows that the WEB browser application and Android App can adaptively realize the greenhouse environment monitoring and control according to the information configuration.
       
  • Feature extraction of overlapping hevea leaves: A comparative study

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Sule T. Anjomshoae, Mohd Shafry Bin Mohd Rahim Automation of rubber tree clone classification has inspired research into new methods of leaf feature extraction. In current practice, rubber clone inspectors has been using several leaf features to identify clone types. One of the unique features of rubber tree leaf is palmate leaflets. This characteristic generates different leaflet positions, where the leaves are overlapping or separated. In this research, we propose keypoint extraction and line detection methods to extract shape and axil (angle between petioles) features of leaflet positions. The results of keypoint extraction methods, namely, SIFT, Harris, and FAST, were compared and discussed for shape feature extraction. Next, Hough transformation and boundary-tracing methods were compared to identify the suitable axil detection method. The evaluation result demonstrates the proper keypoint extraction method for shape context and the clear advantages of Hough Transformation in accuracy of angle detection.
       
  • Application of dynamic model to predict some inside environment variables
           in a semi-solar greenhouse

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Behzad Mohammadi, Seyed Faramarz Ranjbar, Yahya Ajabshirchi Greenhouses are one of the most effective cultivation methods with a yield per cultivated area up to 10 times more than free land cultivation but the use of fossil fuels in this production field is very high. The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it. There are many control methods, such as adaptive, feedback and intelligent control and they require a precise model. Therefore, many modeling methods have been proposed for this purpose; including physical, transfer function and black-box modeling. The objective of this paper is to modeling and experimental validation of some inside environment variables in an innovative greenhouse structure (semi-solar greenhouse). For this propose, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (38°10′N and 46°18′E with elevation of 1364 m above the sea level). The main inside environment factors include inside air temperature (Ta) and inside soil temperature (Ts) were collected as the experimental data samples. The dynamic heat transfer model used to estimate the temperature in two different points of semi-solar greenhouse with initial values. The results showed that dynamic model can predict the inside temperatures in two different points (Ta and Ts) with RMSE, MAPE and EF about 5.3 °C, 10.2% and 0.78% and 3.45 °C, 7.7% and 0.86%, respectively.
       
  • A promising trend for field information collection: An air-ground
           multi-sensor monitoring system

    • Abstract: Publication date: June 2018Source: Information Processing in Agriculture, Volume 5, Issue 2Author(s): Yawei Zhang, Du Chen, Shumao Wang, Lei Tian Timely identifying and quantifying significant spatial and temporal variability in agricultural field has been a crucial factor for improving agricultural production and management. This paper focuses on the mainstream techniques and applications can be adopted to improve the field information collection method. In this paper, the development of wireless sensor networks (WSNs) and remote sensing (RS) technology were reviewed, especially the micro unmanned aerial vehicle (mUAV)-based WSNs and mUAV-based RS by analyzing its applications in field information collection, and pointed out its existing benefits and limitations. A system encompassed multiple technique approaches was proposed in this paper which is called air-ground multi-sensor monitoring system. With the diversification methods of in-field information collection and the improvement of detection precision, an in-field information collection system will play an important role in controlling the farming operations of mechanized agriculture and optimizing the management of agricultural machinery group. In the future, the combination of mUAV, WSNs and RS for crop and soil monitoring will become a powerful tool to obtain field information, increase production, optimize the overall farming practices and input of resources and provide comprehensive reference for the study of soil-crops-machine relationships.
       
  • Quality control of the agricultural products supply chain based on
           “Internet +”

    • Abstract: Publication date: Available online 26 May 2018Source: Information Processing in AgricultureAuthor(s): Qiang Shen, Jian Zhang, Yun-xian Hou, Jia-hui Yu, Jin-you Hu This paper describes a quality-control supply-chain model using the “Internet +” paradigm. The model is based on principal-agent theory, which considers the reputational loss due to inferior products and external responsibility identification. After model analysis and simulation verification, the results show that the optimal quality-control level and market price of agricultural products can be achieved in the agricultural supply chain based on “Internet +” if and only if the information platform’s claim to the agricultural producer is less than the agricultural producer’s claim to the delivery service provider. Also, a rise in consumers’ claims or the agricultural producer’s reputational loss due to inferior products will motivate the quality control of an agricultural procedure. Meanwhile, the market price of agricultural products will also increase with enhanced quality control procedures. The quality-control level of a delivery service provider is inversely proportional to the information platform or its own reputational loss. Thus, the key to promoting quality control along the supply chain is to strengthen the responsibility confirmation of an inferior product between the agricultural producer and the delivery service provider.
       
  • ANFIS and ANNs model for prediction of moisture diffusivity and specific
           energy consumption potato, garlic and cantaloupe drying under convective
           hot air dryer

    • Abstract: Publication date: Available online 23 May 2018Source: Information Processing in AgricultureAuthor(s): Mohammad Kaveh, Vali Rasooli Sharabiani, Reza Amiri Chayjan, Ebrahim Taghinezhad, Yousef Abbaspour-Gilandeh, Iman Golpour The main purpose of this study was to develop and apply an adaptive neuro-fuzzy inference system (ANFIS) and Artificial Neural Networks (ANNs) model for predicting the drying characteristics of potato, garlic and cantaloupe at convective hot air dryer. Drying experiments were conducted at the air temperatures of 40, 50, 60 and 70 °C and the air speeds of 0.5, 1 and l.5 m/s. Drying properties were including kinetic drying, effective moisture diffusivity (Deff) and specific energy consumption (SEC). The highest value of Deff obtained 9.76 × 10−9, 0.13 × 10−9 and 9.97 × 10−10 m2/s for potato, garlic, and cantaloupe, respectively. The lowest value of SEC for potato, garlic, and cantaloupe were calculated 1.94 × 105, 4.52 × 105 and 2.12 × 105 kJ/kg, respectively. Results revealed that the ANFIS model had the high ability to predict the Deff (R2 = 0.9900), SEC (R2 = 0.9917), moisture ratio (R2 = 0.9974) and drying rate (R2 = 0.9901) during drying. So ANFIS method had the high ability to evaluate all output as compared to ANNs method.
       
  • Environmental assessment of date (Phoenix doctylifera) production in Iran
           by life cycle assessment

    • Abstract: Publication date: Available online 15 May 2018Source: Information Processing in AgricultureAuthor(s): Reza Hesampour, Aboubakr Bastani, Kobra Heidarbeigi Environmental indicators have been considered as a main element of environmental impact assessments because of their role on decision and policy making in different fields of special environmental management. The present study was conducted in order to evaluate date (Phoenix doctylifera) production in viewpoint of environmental indicators using life cycle assessment method. The required information was collected by questionnaire and interview with farmers and experts in Khuzestan Province, Iran. The SimaPro Software was used to calculate 11 different environmental indicators for producing one Tonne of the produced date. Diesel fuel, pesticides and nitrogen fertilizer had the highest shares in the studied environmental indicators.
       
  • A technical efficiency evaluation system for vegetable production in China

    • Abstract: Publication date: Available online 9 May 2018Source: Information Processing in AgricultureAuthor(s): Ying Xu, Biao Zhang, Lingxian Zhang With the increasing demand for food worldwide, it has attracted increasing attention how to improve the agricultural production efficiency. This paper aims to develop a technical efficiency evaluation system for vegetable production to provided decisions for the practice of precision agriculture. The paper analyses the system-needs and business processes, and proposes a system framework which has three tiers architectures, based on B/S model. The stochastic frontier analysis (SFA) algorithm model which is the incorporated into the system is established. The system was tested and evaluated by real business data, which were from Beijing from 2003 to 2011 to test system performance based on the temporal perspective and China during 2011 and 2012 to test system performance based on the spatial characteristics. The results shows that the system achieves the business requirements with an intelligent tool for data management and technical efficiency evaluation for vegetable production to improve automation, efficiency and convenience.
       
  • A review of neural networks in plant disease detection using hyperspectral
           data

    • Abstract: Publication date: Available online 9 May 2018Source: Information Processing in AgricultureAuthor(s): Kamlesh Golhani, Siva K. Balasundram, Ganesan Vadamalai, Biswajeet Pradhan This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data.Graphical abstractGraphical abstract for this article
       
  • Effect of selenium application on phenylalanine ammonia-lyase (PAL)
           activity, phenol leakage and total phenolic content in garlic (Allium
           sativum L.) under NaCl stress

    • Abstract: Publication date: Available online 5 May 2018Source: Information Processing in AgricultureAuthor(s): Rozita Khademi Astaneh, Sahebali Bolandnazar, Fariborz Zaare Nahandi, Shahin Oustan It is well known that salinity has badly effect on plant growth all over the world and greatly reduces crop production in the affected regions. Selenium can function as an antioxidant in plants and also in low concentration can promotes plant growth and produce tolerance against stress. This study was conducted in order to determine the effects of selenium (Se) application (0, 4, 8 and 16 mg L-1) on phenylalanine ammonia-lyase (PAL) activity, phenol leakage and total phenolic content of garlic under salt stress (0, 30, 60 and 90 mM NaCl). The highest PAL activity was recorded at 60 and 90 mM NaCl salinity with application of 8 mg Se L-1. Also, when Se was added to the salt-stress garlic, the level of phenol leakage was decreased significantly at two levels of NaCl concentration (by 52% and 40% at 30 mM NaCl with application of 4 and 16 mg Se L-1, and by 50% at 90 mM NaCl with application of 4 mg Se L-1, respectively) in comparison to the salt-stressed garlic without Se. The results showed that Se can increase the salt tolerance of garlic by protecting the cell membrane against lipid peroxidation. The highest concentration of phenols was recorded at 90 mM NaCl salinity level with application of 4 and 8 mg Se L-1, that respectively produced 59% and 51% higher phenols than control treatment without Se. So, application of optimal Se level can increase the potential of garlic in a medium with relatively high level of NaCl.
       
  • Efficient detection method for foreign fibers in cotton

    • Abstract: Publication date: Available online 3 May 2018Source: Information Processing in AgricultureAuthor(s): Xuehua Zhao, Xiangyun Guo, Jie Luo, Xu Tan Since foreign fibers in cotton seriously affect the quality of the final cotton textile products, machine-vision-based detection systems for foreign fibers in cotton are receiving extensive attention in industrial equipment. As one of the key components in detection systems, the suitable and good classifier is significantly important for machine-vision-based on detection systems for foreign fibers in cotton due to it improving the system’s performance. In the study, we test five classifiers in the dataset of foreign fibers in cotton, and for finding the best feature set corresponding to the classifiers, we use the four filter feature selection approaches to find the best feature sets of foreign fibers in cotton corresponding to specific classifiers. The experimental results show that the extreme learning machine and kernel support vector machines have the excellent performance for foreign fiber detection and the classification accuracy are respectively 93.61% and 93.17% using the selected corresponding feature set with 42 and 52 features.
       
  • Robust model predictive control for greenhouse temperature based on
           particle swarm optimization

    • Abstract: Publication date: Available online 27 April 2018Source: Information Processing in AgricultureAuthor(s): Lijun Chen, Shangfeng Du, Yaofeng He, Meihui Liang, Dan Xu Application of model predictive control (MPC) in horticultural practice requires detailed models. However, even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded uncertainties. To enforce robustness during the controller design stage, this paper proposes a particle swarm optimization (PSO)-based robust MPC strategy for greenhouse temperature systems. The strategy is based on a nonlinear physical temperature affine model. The robust MPC technique requires online solution of a minimax optimal control problem, which optimizes the tradeoff between set point tracking and cost requirements reduction. The minimax optimization problem is reformulated to a nonlinear programming problem with constraints. PSO is used to solve the reformulated problem and priority ranking of constraint fitness is proposed to guarantee that the constraints are satisfied. The results of simulations performed using the proposed control system show that the controller can effectively achieve the set point in the presence of disturbances and that it offers more suitable control variables, higher control precision, and stronger robustness than the conventional MPC.
       
  • A raw milk service platform using BP Neural Network and Fuzzy Inference

    • Abstract: Publication date: Available online 14 April 2018Source: Information Processing in AgricultureAuthor(s): Weihong Ma, Jinwei Fan, Qifeng Li, Yuhang Tang An important index for raw milk storage in a dairy farm is the raw milk storage temperature which directly reflects the raw milk quality. Meanwhile, it’s hard to centrally manage numerous dairy farms which are not distributed in the same place. We aim to build a kind of raw milk monitoring and warning equipment, gateway and cloud service platform to solve these problems. The raw milk monitoring and warning equipment and service platform were designed to monitor the raw milk temperature in the refrigerated storage tank and provide a warning alarm if an exception occurred. Data-driven modeling was used for acquiring, cleaning, and utilizing data to solve the raw milk storage problems. The raw milk monitoring and warning management system provided a way of predicting and warning for raw milk storage using BP Neural Network and Fuzzy Inference. The test showed that the BP Neural Network and Fuzzy Inference model built in this paper had a good performance in predicting the raw milk storage temperature and reflecting the variation of raw milk temperature in raw milk storage process. The platform and models provided a method to manage the raw milk in dairies and prevent the raw milk from deteriorating caused by the rising temperature.
       
  • Evaluation of the toxicity and repellency of tropical plant extract
           against subterranean termites, Globitermes sulphureus and Coptotermes
           gestroi

    • Abstract: Publication date: Available online 31 March 2018Source: Information Processing in AgricultureAuthor(s): Noor Hazwani Bakaruddin, Hamady Dieng, Shaida Fariza Sulaiman, Abdul Hafiz Ab Majid The harmful effects of chemical-based termiticides and the increased incidence of termite resistance have resulted in the need for safer and more effective termiticides. Therefore, the screening of antitermiticidal activity of naturally-occurring products could possibly hamper an alternative means in termite control strategies. The aims of this study were to determine the toxicity and repellency of L. leucocephala, A. paniculata, Az. indica and P. niruri crude extracts against two subterranean termites, G. sulphureus and C. gestroi. Bioassays were conducted by applying varying concentrations of the plant extracts (10,000 ppm, 5000 ppm and 500 ppm) on both termite species under laboratory conditions. All extracts exhibited a significant antitermiticidal activity in time- and concentration-dependent manners after 14 days of exposure. The highest mortality of G. sulpureus and C. gestroi were noted in all methanolic extracts of P. niruri, L. leucocephala, A. paniculata, Az. indica at 10,000 ppm. High repellent activity was also noted in the choice bioassay when both termites were treated with all methanolic extracts at 10,000 ppm.Graphical abstractGraphical abstract for this article
       
  • Application of remote sensing for sustainable agriculture and forest
           management

    • Abstract: Publication date: Available online 28 March 2018Source: Information Processing in AgricultureAuthor(s): Ram Swaroop Meena, Tarik Mitran, Sandeep Kumar, Gulab Singh Yadav, Jitendra Singh Bohra, Rahul Datta
       
  • Sensitivity analysis of energy inputs and economic evaluation of
           pomegranate production in Iran

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Mehdi Esmailpour Troujeni, Mehdi Khojastehpour, Adel Vahedi, Bagher Emadi The aim of this research was to investigate the energy use and costs of pomegranate production in Behshahr city (Mazandaran province) of Iran. The required data were gathered by questionnaire and face to face interviews with 83 pomegranate producers. Cobb-Douglas model and sensitivity analysis were employed for energy flows modeling of the production system. The total energy inputs and energy output of production were determined to be 11195.06 and 13276.56 MJ ha−1, and two inputs of diesel fuel and chemical fertilizers with the shares of 45.81 and 23.47%, were the highest energy consumers for pomegranate production. Energy use efficiency, energy productivity and net energy were 1.18, 2081.50 MJ ha−1 and 0.62 kg MJ−1, respectively. The results of Cobb-Douglas model showed that the effect of the energy inputs including human labor, biocides, chemical fertilizers, farmyard manure, electricity and water for irrigation on pomegranates yield were positive, while the effects of diesel fuel and agricultural machinery were negative on the pomegranate yield. The sensitivity analysis results of energy inputs showed that with the increase of one MJ in the energy input of water for irrigation and chemical fertilizers, the yield was increased to 3.12 and 1.42 kg, respectively. Also with the increase of one MJ in diesel fuel and agricultural machinery inputs, the yield was decreased to 0.67 and 0.47 kg, respectively. Diesel fuel as the most used energy input in the production accounted for 0.85% of variable costs and the benefit to cost ratio was determined to be 5.57.
       
  • A hybrid model for dissolved oxygen prediction in aquaculture based on
           multi-scale features

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Chen Li, Zhenbo Li, Jing Wu, Ling Zhu, Jun Yue To increase prediction accuracy of dissolved oxygen (DO) in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR) is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP). Then, the trend term is predicted using grey model (GM) because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD), EEMD-BP and MEC-BP models based on the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE). Our hybrid model is proven to be an effective approach to predict aquaculture DO.
       
  • Scientific development of smart farming technologies and their application
           in Brazil

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Dieisson Pivoto, Paulo Dabdab Waquil, Edson Talamini, Caroline Pauletto Spanhol Finocchio, Vitor Francisco Dalla Corte, Giana de Vargas Mores Smart farming (SF) involves the incorporation of information and communication technologies into machinery, equipment, and sensors for use in agricultural production systems. New technologies such as the internet of things and cloud computing are expected to advance this development, introducing more robots and artificial intelligence into farming. Therefore, the aims of this paper are twofold: (i) to characterize the scientific knowledge about SF that is available in the worldwide scientific literature based on the main factors of development by country and over time and (ii) to describe current SF prospects in Brazil from the perspective of experts in this field. The research involved conducting semi-structured interviews with market and researcher experts in Brazil and using a bibliometric survey by means of data mining software. Integration between the different available systems on the market was identified as one of the main limiting factors to SF evolution. Another limiting factor is the education, ability, and skills of farmers to understand and handle SF tools. These limitations revealed a market opportunity for enterprises to explore and help solve these problems, and science can contribute to this process. China, the United States, South Korea, Germany, and Japan contribute the largest number of scientific studies to the field. Countries that invest more in R&D generate the most publications; this could indicate which countries will be leaders in smart farming. The use of both research methods in a complementary manner allowed to understand how science frame the SF and the mains barriers to adopt it in Brazil.
       
  • The risk management of perishable supply chain based on coloured Petri Net
           modeling

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Lu Liu, Xinlei Liu, Guangchen Liu The supply chain of perishable products is a combination of information organization, sharing and integration. The information modeling of supply chain is constructed to abstract key quality information including environment information, processing procedures and product quality assessments based on principle of quality safety factors and property of decay rate. The coloured Petri Net is applied for integrated description of independent information classification, aiming at risk identification and risk management framework. Well, according to the quality deterioration tendency, risk grades management and decision-making system are established. Practically, the circulation system of aquatic products is studied in this paper for full processing description. The simulation experiments are manipulated on environmental information, processing information and product quality information by the coloured Petri Net. Eventually, the conclusion turns out precisely as such that the coloured Petri Net conclusive for information classification and information transmission while integrated information management is available of efficient risk identification and decision-making system in supply chain of perishable products. Meanwhile, the validity of evaluating management and shelf-life estimation of perishable products are technically feasible.
       
  • Automatic relationship extraction from agricultural text for ontology
           construction

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Neha Kaushik, Niladri Chatterjee In the present era of Big Data the demand for developing efficient information processing techniques for different applications is expanding steadily. One such possible application is automatic creation of ontology. Such an ontology is often found to be helpful for answering queries for the underlying domain. The present work proposes a scheme for designing an ontology for agriculture domain. The proposed scheme works in two steps. In the first step it uses domain-dependent regular expressions and natural language processing techniques for automatic extraction of vocabulary pertaining to agriculture domain. In the second step semantic relationships between the extracted terms and phrases are identified. A rule-based reasoning algorithm RelExOnt has been proposed for the said task. Human evaluation of the term extraction output yields precision and recall of 75.7% and 60%, respectively. The relation extraction algorithm, RelExOnt performs well with an average precision of 86.89%.
       
  • Detecting sugarcane borer diseases using support vector machine

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Tisen Huang, Rui Yang, Wenshan Huang, Yiqi Huang, Xi Qiao Based on the fact that great labor of artificial selection was needed after the sugarcane seeds were cut by the sugarcane cutting machine, and there was a misjudgment of the sugarcane borer diseases. SVM (support vector machine) method was proposed in this study to detect the sugarcane borer diseases. With the machine vision technology, together with threshold segmentation, filling and corrosion operation to process the three images of the same sugarcane whose interval is 120°. The classification features, minimum average gray value and the corresponding minimum gray value were selected by adaptive threshold segmentation algorithm, and removed the region which area of 1. The study used radial basis function as the kernel function of SVM, and roughly selected the range of regularization parameters of C and kernel function parameter σ. Finally, it selected the optimal parameters by the grid search and the cross validation method to identify sugarcane with diseases. The test showed that correct rate of diseases and disease-free sugarcane is 96%, 95.83% for the test set, so the method can effectively complete the sugarcane borer diseases detection.
       
  • Solar thermal simulation and applications in greenhouse

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Morteza Taki, Abbas Rohani, Mostafa Rahmati-Joneidabad In this study, a comprehensive review focusing on key strategies of energy saving technologies based on simulation of heat and mass transfer and also artificial intelligent for climate controlling is presented. Following the brief and concise assessment of existing greenhouse systems in terms of their role in total energy consumption; effective shape and structure, energy-efficient and new technologies are analyzed in detail for potential utilization in greenhouses for notable reductions in energy consumption and also go toward the sustainability. The technologies considered within the scope of this research are mainly renewable and sustainable based solutions such as photovoltaic (PV) modules, solar thermal (T) collectors, hybrid PV/T collectors and systems, phase change material (PCM) and underground based heat storage techniques, energy-efficient heat pumps, alternative facade materials for better thermal insulation and power generation. The findings from the research clearly reveal that up to 70% energy saving can be achieved through appropriate retrofit of conventional greenhouses. Using of solar greenhouses in Europe is more popular than others. In some countries in Asia such as Iran, it is very restrict to invest on renewable projects because of cheap fossil fuels. So it is recommended beside of investments by private investors, the Iranian government should also invest in the extension of solar energy in greenhouse by setting up a specialized agency or contracting firms. Those should target the modeling and design the best shape of solar greenhouse for all agricultural areas to receive the maximum solar radiation and decrease the need of fossil fuels.
       
  • Image segmentation of overlapping leaves based on Chan–Vese model
           and Sobel operator

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Zhibin Wang, Kaiyi Wang, Feng Yang, Shouhui Pan, Yanyun Han To improve the segmentation precision of overlapping crop leaves, this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator. The approach consists of three stages. First, a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background. Second, the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges, respectively. Third, a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator. To verify the effectiveness of the proposed algorithm, a segmentation experiment was performed on 30 images of cucumber leaf. The mean error rate of the proposed method is 0.0428, which is a decrease of 6.54% compared with the mean error rate of the level set method. Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.
       
  • Cattle behaviour classification from collar, halter, and ear tag sensors

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): A. Rahman, D.V. Smith, B. Little, A.B. Ingham, P.L. Greenwood, G.J. Bishop-Hurley In this paper, we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data. Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head, on a halter positioned at the side of the head behind the mouth, or on the ear using a tag. The purpose of the study was to determine how sensor data from different placement can classify a range of typical cattle behaviours. Data were collected and animal behaviours (grazing, standing or ruminating) were observed over a common time frame. Statistical features were computed from the sensor data and machine learning algorithms were trained to classify each behaviour. Classification accuracies were computed on separate independent test sets. The analysis based on behaviour classification experiments revealed that different sensor placement can achieve good classification accuracy if the feature space (representing motion patterns) between the training and test animal is similar. The paper will discuss these analyses in detail and can act as a guide for future studies.
       
  • Climatic variables: Evaporation, sunshine, relative humidity, soil and air
           temperature and its adverse effects on cotton production

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Zakaria M. Sawan Cotton yield is a function of growth rates, flower production rates, and flower and boll retention during the fruiting period. Information on the relationship between climatic factors and the cotton plant's ability to produce and sustain flower buds, flowers, and bolls will allow one to model plant responses to conditions that frequently occur in the field and to predict developmental rate or the formation of these organs. This study investigates the statistical relationship between various climatic factors and overall flower and boll production. Also, the relationship between climatic factors and production of flowers and bolls obtained during the development periods of the flowering and boll stage. Further, predicting effects of climatic factors during different convenient intervals (in days) on cotton flower and boll production compared with daily observations. Evaporation, sunshine duration, relative humidity, surface soil temperature at 1800 h, and maximum air temperature, are the important climatic factors that significantly affect flower and boll production. The five-day interval was found to be more adequately and sensibly related to yield parameters. Evaporation; minimum humidity and sunshine duration were the most effective climatic factors during preceding and succeeding periods on boll production and retention. There was a negative correlation between flower and boll production and either evaporation or sunshine duration, while that correlation with minimum relative humidity was positive.
       
  • Temperature based generalized wavelet-neural network models to estimate
           evapotranspiration in India

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Sirisha Adamala In this paper, generalized wavelet-neural network (WNN) based models were developed for estimating reference evapotranspiration (ETo) corresponding to Hargreaves (HG) method for different agro-ecological regions (AERs): semi-arid, arid, sub-humid, and humid in India. The input and target to the WNN models are climate data (minimum and maximum air temperature) and ETo (estimated from FAO-56 Penman Monteith method), respectively. The developed WNN models were compared with the various generalized conventional models such as artificial neural networks (ANN), linear regression (LR), wavelet regression (WR), and HG method to test the best performed model. The performance indices used for the comparison include root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), the ratio of average output to the average target ETo values (Rratio), and relative percentage (RP). The WNN and ANN models were performed better as compared to LR, WR and HG methods. Further, the best performed WNN and ANN models were tested on locations, which were not included in training to test their generalizing capability. It is concluded that the WNN and ANN models were shown good generalizing capability for the tested locations as compared to HG method.
       
  • The effects of selenium on some physiological traits and K, Na
           concentration of garlic (Allium sativum L.) under NaCl stress

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Rozita Khademi Astaneh, Sahebali Bolandnazar, Fariborz Zaare Nahandi, Shahin Oustan Selenium, which is an essential microelement for animals, positively affects growth and development of some plants by enhancing the activities of antioxidant enzyme. The study was conducted in order to determine the effects of selenium (0, 4, 8 and 16 mg L−1) on salt stress (0, 30, 60 and 90 mM NaCl) subjected garlic plants grown in hydroponics. According to results, salinity reduced chlorophyll index and carotenoid contents. Se application 8 mg L−1 in 30 mM NaCl salinity and 4 mg L−1 in 60 mM NaCl salinity significantly improved chlorophyll index and carotenoid contents, RWC was increased by application of 16 mg L−1 Se under 90 mM NaCl, in garlic leaves. Also, with further increases in Se to the medium containing 30 mM NaCl, K concentration gradually increased in leaves. Analysis of Na content in leaves revealed that Na concentration increases with increasing salinity. Moreover, it is concluded that Se increase in K uptake and decrease in Na uptake under salt stress.
       
  • A new approach for visual identification of orange varieties using neural
           networks and metaheuristic algorithms

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Sajad Sabzi, Yousef Abbaspour-Gilandeh, Ginés García-Mateos Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied. This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges (Citrus sinensis L.), namely Bam, Payvandi and Thomson. A total of 300 color images were used for the experiments, 100 samples for each orange variety, which are publicly available. After segmentation, 263 parameters, including texture, color and shape features, were extracted from each sample using image processing. Among them, the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm (ANN-PSO). Then, three different classifiers were applied and compared: hybrid artificial neural network – artificial bee colony (ANN-ABC); hybrid artificial neural network – harmony search (ANN-HS); and k-nearest neighbors (kNN). The experimental results show that the hybrid approaches outperform the results of kNN. The average correct classification rate of ANN-HS was 94.28%, while ANN-ABS achieved 96.70% accuracy with the available data, contrasting with the 70.9% baseline accuracy of kNN. Thus, this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties, which can be easily implemented in processing factories. The main contribution of this work is that the method can be directly adapted to other use cases, since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.
       
  • Impacts of the precision agricultural technologies in Iran: An analysis
           experts' perception & their determinants

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Somayeh Tohidyan Far, Kurosh Rezaei-Moghaddam Nowadays agricultural methods developments that are productively, economically, environmentally and socially sustainable are required immediately. The concept of precision agriculture is becoming an attractive idea for managing natural resources and realizing modern sustainable agricultural development. The purpose of this study was to investigate factors influencing impacts of precision agriculture from the viewpoints of Boushehr Province experts. The research method was a cross sectional survey and multi-stage random sampling was used to collect data from 115 experts in Boushehr province. According to the results, experts found underground and surface waters conservation, rural areas development, increase of productivity and increasing income as the most important impacts of precision agricultural technologies. Experts’ attitudes indicate their positive view toward these kinds of impacts. Also behavioral attitude has the most effect on impacts.
       
  • A quasi-steady state model for predicting the heating requirements of
           conventional greenhouses in cold regions

    • Abstract: Publication date: March 2018Source: Information Processing in Agriculture, Volume 5, Issue 1Author(s): Md Shamim Ahamed, Huiqing Guo, Karen Tanino A time-dependent, quasi-steady state thermal model (GREENHEAT) based on the lumped estimation of heat transfer parameters of greenhouses has been developed to predict the hourly heating requirements of conventional greenhouses. The model was designed to predict the hourly heating requirements based on the input of greenhouse indoor environmental control parameters, physical and thermal properties of crops and construction materials, and hourly weather data including temperature, relative humidity, wind speed, and cloud cover. The model includes all of the heat transfer parameters in greenhouses including the heat loss for plant evapotranspiration, and the heat gain from environmental control systems. Results show that the predicted solar radiation data from the solar radiation sub-model are a reasonable fit with the data from the National Solar Radiation Database (NSRDB). Thermal analysis indicates environmental control systems could reduce 13–56% of the total heating requirements over the course of a year in the study greenhouse. During the winter season, the highest amount of greenhouse heat is lost due to conduction and convection, and the heat used for evapotranspiration is dominant in the summer. Finally, the model was validated with actual heating data collected from a commercial greenhouse located in Saskatoon, and the results show that the model satisfactorily predicts the greenhouse heating requirements.
       
 
 
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