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  Subjects -> ENGINEERING (Total: 2284 journals)
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ENGINEERING (1208 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 7)
3D Research     Hybrid Journal   (Followers: 19)
AAPG Bulletin     Hybrid Journal   (Followers: 5)
AASRI Procedia     Open Access   (Followers: 15)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 227)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 2)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Active and Passive Electronic Components     Open Access   (Followers: 7)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 6)
Advanced Science     Open Access   (Followers: 5)
Advanced Science Focus     Free   (Followers: 3)
Advanced Science Letters     Full-text available via subscription   (Followers: 6)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 7)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 17)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 14)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 20)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 25)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 28)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 12)
Advances in Polymer Science     Hybrid Journal   (Followers: 40)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aerobiologia     Hybrid Journal   (Followers: 1)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 4)
AIChE Journal     Hybrid Journal   (Followers: 29)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access  
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 28)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 11)
American Journal of Engineering Education     Open Access   (Followers: 9)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 7)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 6)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 9)
Applied Clay Science     Hybrid Journal   (Followers: 4)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 3)
Applied Nanoscience     Open Access   (Followers: 7)
Applied Network Science     Open Access  
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 3)
Applied Sciences     Open Access   (Followers: 2)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 7)
Arid Zone Journal of Engineering, Technology and Environment     Open Access  
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 3)
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Avances en Ciencias e Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 3)
Batteries     Open Access   (Followers: 4)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 23)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access  
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 9)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
Biomedical Science and Engineering     Open Access   (Followers: 3)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 2)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription  
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 14)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 3)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 14)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 41)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 7)
Case Studies in Thermal Engineering     Open Access   (Followers: 3)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 2)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 8)
Catalysis Science and Technology     Free   (Followers: 6)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 5)
CEAS Space Journal     Hybrid Journal  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
Central European Journal of Engineering     Hybrid Journal   (Followers: 1)
CFD Letters     Open Access   (Followers: 6)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencias Holguin     Open Access   (Followers: 1)
CienciaUAT     Open Access  
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Full-text available via subscription   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 21)
Clay Minerals     Full-text available via subscription   (Followers: 9)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Coal Science and Technology     Full-text available via subscription   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 11)
Coastal Engineering Journal     Hybrid Journal   (Followers: 4)
Coatings     Open Access   (Followers: 3)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 1)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 13)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 26)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Composite Structures     Hybrid Journal   (Followers: 256)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 179)
Composites Part B : Engineering     Hybrid Journal   (Followers: 227)
Composites Science and Technology     Hybrid Journal   (Followers: 197)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 13)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 17)
Computers & Geosciences     Hybrid Journal   (Followers: 28)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 4)
Computers and Geotechnics     Hybrid Journal   (Followers: 10)
Computing and Visualization in Science     Hybrid Journal   (Followers: 5)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 29)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 6)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 8)
Control Engineering Practice     Hybrid Journal   (Followers: 42)
Control Theory and Informatics     Open Access   (Followers: 7)
Corrosion Science     Hybrid Journal   (Followers: 25)
CT&F Ciencia, Tecnologia y Futuro     Open Access  

        1 2 3 4 5 6 7 | Last

Journal Cover Computers and Electronics in Agriculture
  [SJR: 0.823]   [H-I: 73]   [4 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0168-1699
   Published by Elsevier Homepage  [3042 journals]
  • Practical modeling and optimization of ultrasound-assisted bleaching of
           olive oil using hybrid artificial neural network-genetic algorithm
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Sara Asgari, Mohammad Ali Sahari, Mohsen Barzegar
      Multi-objective modeling and optimization of ultrasound-assisted bleaching of olive oil were accomplished by a hybrid artificial neural network (ANN) and genetic algorithm (GA) method using an ultrasonic bath with a frequency of 25kHz. The influence of process parameters including ultrasonic power, bleaching clay dosage, process temperature and time (inputs) on final Lovibond red (Lr) and peroxide value (PV) (outputs) was modeled by a multilayer feed-forward back propagation ANN. The accurate 2-hidden layer model with 20 neurons in each, high R2 (up to 90%) and minimum mean square error (MSE) obtained by ANN was introduced to GA to find the best operation conditions to achieve minimum Lr and PV. The optimum treatment was found with ultrasonic power of 30%, bleaching clay of 1.2%, bleaching time of 13min and temperature of 65°C. Under optimal conditions, Lr and PV were 2.47 and 6.49 (meqO2/kg), respectively, that were consistent with predicted values. Optimally ultrasonic bleached olive oil and an industrially bleached olive oil were compared. In most cases, the results indicated no detrimental effects of ultrasound on oil structure. Thus, 40% reduction in bleaching clay dosage, 35% reduction in process temperature and 57% reduction in time over ultrasound-assisted bleaching which not only provided economic and environmental benefits, but also retained edible oil nutritional value in comparison to common bleaching procedure. The results of this study confirm the applicability of ultrasound-assisted bleaching by ultrasonic bath as an economic and feasible approach for bleaching of olive oil to reduce high bleaching costs.

      PubDate: 2017-07-09T22:40:07Z
  • A generic ontological network for Agri-food experiment integration –
           Application to viticulture and winemaking
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Aunur Rofiq Muljarto, Jean-Michel Salmon, Brigitte Charnomordic, Patrice Buche, Anne Tireau, Pascal Neveu
      This paper presents an ontological approach of scientific experimental data integration across complementary sub-domains, i.e., agricultural production and food processing, with an application to viticulture and winemaking. The two main steps in this approach are (i) to integrate preexisting ontologies to create a so-called ontology network and (ii) to populate the ontology network with experimental data from various sources. The Agri-Food Experiment Ontology (AFEO), a new ontology network, was developed, based on two ontological resources, i.e., AEO (Ontology for Agricultural Experiments) and OFPE (Ontology for Food Processing Experiments). It contains 136 concepts which cover various viticulture practices, as well as winemaking products and operations. AFEO was used to guide the data integration of two different data sources, i.e., viticulture experimental data stored in a relational database, and winemaking experimental data stored in Microsoft Excel files. Two applications illustrate the approach. The first one is on wine traceability and the second one is related to the influence of irrigation practices and winemaking methods on GSH concentration in wine. These examples show that data integration guided by an ontology network can provide researchers with the information necessary to address extended research questions.

      PubDate: 2017-07-09T22:40:07Z
  • Particle Swarm Optimization based incremental classifier design for rice
           disease prediction
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Shampa Sengupta, Asit K. Das
      Increase of huge amount of data in every application demands an incremental learning technique for data analysis. One of such data analysis task in dynamic environment is to design an incremental classifier for decision making and consequently updating the knowledge base of the overall system. Classifier construction depicts extraction of interesting patterns from the large repository of data and predicts the future trends based on the existing patterns. The time complexity of the classification system increases gradually and the system becomes inefficient while it is learned repeatedly for adding new group of data with the existing one in a certain interval of time. Without learning the same classifier for the whole data, if the knowledge of old data extracted by the classifier is used together with the new group of data to design the updated classifier, called incremental classifier, then time complexity reduces drastically. In the paper, the concepts of Particle Swarm Optimization technique and Association Rule Mining are used to design an incremental rule based classification system. The incremental classifier is suitable to apply on rice disease dataset for disease prediction as the characteristics of rice diseases change in time due to change of climate, biological, and geographical factors. The proposed method has been applied on both simulated rice disease dataset and benchmark datasets and the classification accuracy is measured and compared with various state of the art classification algorithms. The method is also evaluated based on some statistical measures and statistical test is done to establish its significance and effectiveness.

      PubDate: 2017-07-09T22:40:07Z
  • Machine vision based soybean quality evaluation
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Md Abdul Momin, Kazuya Yamamoto, Munenori Miyamoto, Naoshi Kondo, Tony Grift
      A novel proof of concept was developed targeted at the detection of Materials Other than Grain (MOGs) in soybean harvesting. Front lit and back lit images were acquired, and image processing algorithms were applied to detect various forms of MOG, also known as dockage fractions, such as split beans, contaminated beans, defect beans, and stem/pods. The HSI (hue, saturation and intensity) colour model was used to segment the image background and subsequently, dockage fractions were detected using median blurring, morphological operators, watershed transformation, and component labelling based on projected area and circularity. The algorithms successfully identified the dockage fractions with an accuracy of 96% for split beans, 75% for contaminated beans, and 98% for both defect beans and stem/pods.

      PubDate: 2017-07-09T22:40:07Z
  • Early detection of water stress in maize based on digital images
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Shuo Zhuang, Ping Wang, Boran Jiang, Maosong Li, Zhihong Gong
      Early water stress detection is of great significance in precision plant breeding and agricultural production. In the field, outdoor cameras would be an applicable tool for early drought stress detection with high-resolution images. Based on image analysis, we presented a model to detect water stress of maize in the early stage. In the red-green-blue (RGB) color space, a simple linear classifier was proposed to extract green vegetation from maize images. After color image segmentation, fourteen-dimensional color and texture features were extracted from each image. Three water treatment levels (well-watered, reduced watered and drought stressed) were applied to maize plants. We adopted a two-stage detection model trained with different feature subsets to evaluate the water stress. The water stress detection model was based on a supervised learning algorithm, gradient boosting decision tree (GBDT). The recognition accuracy of three water treatments (ATWT) was 80.95% and the accuracy of water stress (AWS) reached 90.39%. Results showed that the proposed method had an effective detection performance between water suitability and water stress conditions in the maize fields.

      PubDate: 2017-07-09T22:40:07Z
  • Development of a CFD crop submodel for simulating microclimate and
           transpiration of ornamental plants grown in a greenhouse under water
    • Abstract: Publication date: Available online 4 July 2017
      Source:Computers and Electronics in Agriculture
      Author(s): Hacene Bouhoun Ali, Pierre-Emmanuel Bournet, Patrice Cannavo, Etienne Chantoiseau
      Predictive models of soil-plant-atmosphere water transfers may be helpful to better manage water inputs to plants in greenhouses. In particular, Computational Fluid Dynamics appears to be a powerful tool to describe the greenhouse microclimate and plant behavior. Up until now, most models for potted plants grown in greenhouses were established for well-watered conditions. In this context, the aim of this work is to develop a specific submodel to simulate the distributed transpiration and microclimate during plants grown in pots inside greenhouses under water restriction conditions. A 2D transient CFD (Computational Fluid Dynamics) model was implemented and user-defined functions were adapted to take account of the crop interactions with the climate inside the greenhouse. The crop was considered as a porous medium and specific source terms for transpiration and sensible heat transfers were added. A specific submodel was also implemented to calculate the substrate water content based on the water balance between irrigation and transpiration. Particular care was paid to the modeling of stomatal resistance. In order to obtain the input data and to validate the CFD simulations, an experiment was conducted over 16weeks inside a greenhouse equipped with New Guinea impatiens ornamental plants grown in containers on shelves. Both well-watered and restriction conditions were analyzed. The results of the CFD simulations showed the ability of the model to correctly predict transpiration, air and leaf temperatures as well as air humidity inside the greenhouse for both water regimes. Different irrigation scenarios were then tested, progressively reducing the water supply by providing a lesser amount of water than the growing media water capacity. The simulations made it possible to assess the model response to different irrigation regimes on plant transpiration, usual growing media water potential and climate distribution inside the greenhouse. The tests also showed that the water supply could be reduced by 20% without significantly impacting the transpiration rate and, therefore, potential plant growth. The CFD model could thus be useful to test different irrigation scenarios and better manage water inputs.

      PubDate: 2017-07-09T22:40:07Z
  • Temperature-stabilized laser-based sensors for accurate plant
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): S. Askraba, A. Paap, K. Alameh, J. Rowe, C. Miller
      We propose the use of temperature-stabilized lasers to improve the accuracy of a spectral-reflectance-based plant discrimination sensor for use in selective herbicide spraying systems. The discrimination of Canola from Wild-radish is based on Normalized Difference Vegetation Index (NDVI) measurements at two different laser wavelengths. Indoor experimental results show that the relative discrimination accuracy for a temperature non-stabilized sensor drops to 12.5% when the laser temperature varies between 16°C and 34°C. Experimental results also show that by controlling the temperature of the laser diodes, canola crops can be discriminated from wild radish weeds with accuracy as high as 90%.

      PubDate: 2017-06-28T12:35:16Z
  • Estimation of leaf nitrogen concentration in wheat using the MK-SVR
           algorithm and satellite remote sensing data
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Liai Wang, Xudong Zhou, Xinkai Zhu, Wenshan Guo
      The appropriate spectral vegetation indices can be used to rapidly and non-destructively estimate the leaf nitrogen concentration (LNC) in wheat for on-farm wheat management. However, the accuracy of estimation should be further improved. Previous studies focused on developing vegetation indices, but research about modeling algorithms were limited. In this study, multiple-kernel support vector regression (MK-SVR) was used to assess the LNC in wheat based on satellite remote sensing data. The objectives of this study were to (1) investigate the applicability of the MK-SVR algorithm for remotely estimating the LNC in wheat, (2) test the performance of the MK-SVR regression model, and (3) compare the performance of the MK-SVR algorithm with multiple linear regression (MLR), partial least squares (PLS), artificial neural networks (ANNs), and single-kernel SVR (SK-SVR) algorithms for wheat LNC estimation. In-situ LNC data over four years at different sites in Jiangsu Province of China were measured during the jointing, booting, and anthesis stages; one HJ-CCD image of wheat was obtained during each stage. Vegetation indices were calculated based on these images, and correlations between vegetation indices and LNC data were measured. Finally, a MK-SVR model whose inputs were vegetation indices was established to estimate the LNC during each stage. The results showed that the MK-SVR model performed well in estimating LNC. The coefficients of determination (R2 ) of the estimated-versus-measured LNC values for the three stages were respectively 0.73, 0.82, and 0.75, meanwhile, the corresponding root mean square errors (RMSE) and the relative RMSE were respectively 0.13 and 6.6%, 0.21 and 7.7%, and 0.20 and 6.5%. Thus, the MK-SVR algorithm provides an effective way to improve the prediction accuracy of LNC in wheat on a large scale.

      PubDate: 2017-06-28T12:35:16Z
  • Fusion of superpixel, expectation maximization and PHOG for recognizing
           cucumber diseases
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Shanwen Zhang, Yihai Zhu, Zhuhong You, Xiaowei Wu
      Cucumber diseases can be detected and recognized automatically based on diseased leaf symptoms. In this paper, we propose a new method, combining superpixels, expectation maximization (EM) algorithm, and logarithmic frequency pyramid of histograms of orientation gradients (PHOG), to recognize cucumber diseases. The proposed method is composed of following steps. First, the superpixel operation is used to divide a diseased leaf image into a number of compact regions, which can dramatically accelerate the convergence speed of the EM algorithm that is adopted to segment the diseased leaf regions and obtain the lesion image. Second, the logarithmic frequency PHOG features are extracted from the segmented lesion image. Finally, Support Vector Machines (SVMs) are performed to classify and recognize different cucumber diseases. Conducted on a database of cucumber diseased leaf images, experimental results show the proposed method is effective and feasible for recognizing cucumber diseases.

      PubDate: 2017-06-28T12:35:16Z
  • Near infrared spectroscopy and element concentration analysis for
           assessing yerba mate (Ilex paraguariensis) samples according to the
           country of origin
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Alessandro Kahmann, Michel J. Anzanello, Marcelo Caetano Alexandre Marcelo, Dirce Pozebon
      Yerba mate (Ilex paraguariensis) is used to produce a beverage typically consumed in South America countries, and presents peculiar land-based characteristics due to geographical origin. Such characteristics have recently become a matter of interest for many producers as specific features of yerba mate tend to influence product acceptance in new markets, prices and commercial advantages. This scenario justifies the developing of frameworks tailored to correctly classify products according to their authenticity. This paper uses Near Infrared (NIR) spectroscopy and data describing concentration of chemical elements to classify commercial yerba mate samples according to their place of origin. Aimed at enhancing data interpretability, we propose a novel variable selection method that applies quadratic programming to reduce redundant information among the retained variables and maximize their relationship regarding the sample place of origin; sample categorization is then performed using alternative classification techniques. When applied to the NIR dataset, the proposed method retained average 8.79% of the original wavenumbers, while leading to 1.9% more accurate classifications when compared to categorization using the full spectra. As for the elements dataset, we increased average classification accuracy by 3.5% and retained 47.22% of the original elements. The proposed method also outperformed two other approaches for variable selection from the literature. Our findings suggest that variable selection frameworks help to correctly identify the origin and authenticity of yerba mate samples, making model construction and interpretation easier.

      PubDate: 2017-06-28T12:35:16Z
  • Chemical imaging for measuring the time series variations of tuber dry
           matter and starch concentration
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Wen-Hao Su, Da-Wen Sun
      The potential of chemical imaging for rapid measurement of dry matter concentration (DMC) and starch concentration (SC) in both potato and sweet potato tubers was investigated. The time series images of tuber samples were acquired, then the resulting reflectance spectra (RS) were corrected and transformed into absorbance spectra (AS), and exponent spectra (ES). Full wavelength regression models including multiple linear regression (MLR), partial least squares regression (PLSR) and locally weighted partial least squares regression (LWPLSR) were established based on spectral profiles with measured DMC and SC values. The best calibration model for measuring DMC and SC was LWPLSR based on ES and RS where the coefficients of determination in cross-validation (R 2 CV) were 0.987 and 0.985, and the root mean squared errors in cross-validation (RMSECV) were 0.015 and 0.014, respectively. After, six groups of eight feature wavelengths were chosen from RS, AS and ES based on wavelength selection methods including β-coefficient (βC) of PLSR and the first derivative and mean centering iteration algorithm (FMCIA), and were successively used to build simplified models. The acquired FMCIA-RS-LWPLSR and βC-RS-LWPLSR models showed better accuracy than other simplified models, with R 2 P of 0.985 and RMSEP of 0.016 for DMC prediction, and R 2 P of 0.983 and RMSEP of 0.015 for SC prediction, respectively. Besides, the optimal models for MLR and PLSR were obtained using FMCIA on the basis of the ES. After further reducing the number of feature wavelengths, only six wavelengths (1028, 1068, 1135, 1208, 1262 and 1460nm) were selected and utilized to develop the simplest FMCIA-Es-MLR model for determining DMC and FMCIA-Es-PLSR model for detecting SC, yielding a reasonable level of accuracy with R 2 P of 0.962 and 0.963 as well as RMSEP of 0.025 and 0.023, respectively. Furthermore, the time series variations of DMC and SC on tuber samples were visualized based on an equation to apply the simplest models to the spectral images.

      PubDate: 2017-06-28T12:35:16Z
  • PVC membrane-based portable ion analyzer for hydroponic and water
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Hak-Jin Kim, Dong-Wook Kim, Won Kyung Kim, Woo-Jae Cho, Chang Ik Kang
      Rapid on-site measurement of chemical ions in hydroponic and water solutions would allow efficient management of nutrients used for plant production as well as early monitoring of chemical pollutants. This paper reports the development of an embedded portable analyzer incorporated with a sensor array of polyvinyl chloride (PVC) membrane-based ion-selective electrodes (ISEs) to directly measure the concentrations of NO3, K, and H ions in hydroponic solutions and water. The developed ion analyzer consisted of an AVR microcontroller, five channels of sensor inputs, a 16-bit analog-to-digital converter, a 7-inch LCD touch panel, and SD memory card-based data storage. The use of a two-point normalization method consisting of sensitivity adjustment followed by offset compensation was effective in minimizing signal drifts resulting from a series of sample measurements while reducing variability in response among multiple ISEs during replicate measurements in practical manners. The sensitivity and predictive capability of the PVC membrane-based H ISEs, in conjunction with a three-point calibration method, were satisfactory, showing sub-Nernstian slopes of 51.9–56.1mV/decade over a pH range of 4–10 (R2 >0.96) and providing results in close agreement with the results of a conventional pH meter (a nearly 1:1 regression slope and a y-intercept near 0). A response time of 50s and a wide sensitivity range of 11–884mgL−1 measured with the prototype portable ion analyzer equipped with PVC membrane-based NO3 and K ISEs, each with a lifetime of 60days, enabled direct analysis of hydroponic and water samples without the need to dilute samples. Strong linear relationships between the prototype ion analyzer and the standard instrument methods (R2 >0.97, slopes ranging from 0.89 to 1.24) were exhibited.

      PubDate: 2017-06-28T12:35:16Z
  • Calibrating cameras in an industrial produce inspection system
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Andrew Wilson, Gadi Ben-Tal, Jamie Heather, Richard Oliver, Robert Valkenburg
      We describe a multi-camera calibration method for a produce inspection system with color and monochrome cameras. The method uses a novel spheroidal calibration target that is similar in size to the produce being graded, and features a pattern of large and small dots. This enables us to calibrate the camera system for the localized volume through which the produce moves, where human access is impractical. We describe the detection and localization of the dot centres, and the process for putting dot images into correspondence with 3D points on the target. The calibration parameters are estimated via standard bundle adjustment techniques. The method reliably gives a reprojection error RMS of approximately 0.35px, and is fully automated. We further validate the method by measuring error in sparse reconstructions of chessboard targets and the spheroid. These objects are reconstructed with approximately 0.2mm RMS error. Finally, we use the calibrations to build 3D models of fruit and vegetables, achieving volume estimates within 7.3mL (2.6%) of the true volumes.

      PubDate: 2017-06-28T12:35:16Z
  • Prediction of water temperature in prawn cultures based on a mechanism
           model optimized by an improved artificial bee colony
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Longqin Xu, Shuangyin Liu, Daoliang Li
      To reduce aquaculture risk and optimize water quality management in prawn culture ponds, this paper uses mechanistic and statistical analytic methods to propose a hybrid water temperature forecasting model based on the water temperature mechanism model (WTMM) with optimal parameters selected by an improved artificial bee colony (IABC) algorithm. Because of existing problems with using an artificial bee colony algorithm in modeling, an improved ABC with a dynamically adjusted inertia weight based on the fitness function value was implemented to improve local and global search abilities. Then, IABC was employed to adaptively search for the optimal combinatorial parameters needed in the WTMM model, which overcomes the blindness of and limits to parameter selection for the traditional WTMM model. We adopted an IABC-WTMM algorithm to construct a non-linear mechanical prediction model. The IABC-WTMM was tested and compared to other algorithms by applying it to the prediction of water temperature in prawn culture ponds. Experimental results show that the proposed IABC-WTMM could increase prediction accuracy and execute generalization performance better than the original water temperature mechanism model (O-WTMM) and back-propagation neural network (BP-NN), but was inferior to the standard LSSVR model. Overall, it is a suitable and effective method for predicting water temperature in intensive aquacultures.

      PubDate: 2017-06-28T12:35:16Z
  • Comparison of artificial neural network and multivariate regression models
           for prediction of Azotobacteria population in soil under different land
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Mitra Ebrahimi, Ali Akbar Safari Sinegani, Mohammad Reza Sarikhani, Seyed Abolghasem Mohammadi
      Azotobacteria are one of the most important and beneficial soil bacteria which their number and distribution are affected by physicochemical and biological properties of soil and land usage. The aim of this study was to evaluate the population of Azotobacter in soils with different land uses and relationship between population size and some physicochemical and biological properties of soil by using artificial neural network (ANN) and multivariate linear regression (MLR) methods. In total, 50 soil samples were collected from depth (0–25cm) under different land uses located in East Azerbaijan, Ardabil and Gilan provinces, Iran. Population of Azotobacter was separately counted in Winogradsky and LG media by preparation of serial dilution and plate counts. In addition, soil texture, pH, electrical conductivity (EC), carbonate calcium equivalent (CCE), organic carbon (TOC), cold water extractable OC (CWEOC), hot water extractable OC (HWEOC), light fraction OC (LFOC), heavy fraction OC (HFOC), basal respiration (BR) and substrate induced respiration (SIR), the number of bacteria, fungi and actinomycete were measured in three replicates in each soil sample. To predict Azotobacteria population based on easily measurable characteristics of soil properties, MLR analysis and ANN model (feed-forward back propagation network) were used. In order to assess the models, root mean square error (RMSE) and R2 were used. The R2 and RMSE values for population of Azotobacter in Winogradsky medium obtained by ANN model with SIR, EC, CCE, sand and silt as entered variables were 0.76 and 0.36, respectively, and for population of Azotobacter in LG medium, were 0.45 and 0.50, respectively. Using MLR the R2 value for population of Azotobacter in WG and LG media was 0.63 and 0.39, respectively. Results showed that ANN with eight neurons in hidden layer had better performance in predicting population of Azotobacter in WG than MLR.

      PubDate: 2017-06-28T12:35:16Z
  • Use of a digital camera as alternative method for non-destructive
           detection of the leaf chlorophyll content and the nitrogen nutrition
           status in wheat
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Jörg Peter Baresel, Pablo Rischbeck, Yuncai Hu, Sebastian Kipp, Yuncai Hu, Gero Barmeier, Bodo Mistele
      In this paper, the use of a digital consumer camera for the non-destructive detection of the N nutritional status is compared with two alternative methods, namely SPAD and reflectance spectrometry in three field experiments. The image analysis method consisted of segmentation and successive analysis of the foreground color, i.e. only green plant parts. Thus, also analysis of canopies with small degree of ground cover is possible. All methods gave comparable results, while the effort necessary was considerably higher when using the chlorophyll meter. With spectral measurements, the biomass and leaf nitrogen content could not be clearly differentiated; chlorophyll measurements do not reflect biomass, whereas the described procedure of image analysis permits the consideration both. If used properly, digital image analysis is a valuable tool for the determination of the N nutrition status under field conditions, with low costs and labor requirements.

      PubDate: 2017-06-21T12:20:14Z
  • Optimal crop plans for a multi-reservoir system having intra-basin water
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): R. Arunkumar, V. Jothiprakash
      Optimizing a multi-reservoir system is complicated, since the operation of one reservoir depends on other reservoir and may also have conflicting multiple objectives. The conflicting purposes of a multi-reservoir system requires a systematic multi-objective study. Recently, multi-objective evolutionary algorithms (MOEAs) have been widely used for the multi-objective analysis of the reservoir systems. However, the simple MOEAs result in premature convergence and local optimal solution for complex non-linear multi-objective optimization problems. To improve the performance and maintain the diversity in the population, chaos is being combined with the evolutionary algorithms for optimizing complex problems. In the present study, the chaos algorithm is coupled with MOEAs such as non-dominated genetic algorithm-II (CNSGA-II) and multi-objective differential evolution algorithm (CMODE) to derive an optimal crop planning for a multi-reservoir system having intra-basin water transfer. The model is developed with the objective of maximizing the net benefits and maximizing the crop production, subject to various physical, land and water availability constraints. The resulted optimal policy is further assessed using a simulation model and its performance is evaluated using various statistical indices. It is found that CMODE has resulted in slightly higher net benefits of Rs. 1921.77 Million and 1201.55 thousand tons of crop production with an irrigation intensity of 106.29% compared to other techniques used in this study. It has also resulted in an optimal spatial and temporal intra-basin water transfer from the upstream reservoirs to the downstream reservoir. The simulation of optimal results showed that the optimal policies obtained from CMODE performed well for longer period with less irrigation deficits. All the reservoirs in the system achieved more than 95% reliability in meeting the irrigation demands and intra-basin water transfer.

      PubDate: 2017-06-21T12:20:14Z
  • Performance investigation of the dam intake physical hydraulic model using
           Support Vector Machine with a discrete wavelet transform algorithm
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Pezhman Taherei Ghazvinei, Shahaboddin Shamshirband, Shervin Motamedi, Hossein Hassanpour Darvishi, Ely Salwana
      In the present study hydraulic scaled model was conducted to evaluate an intake structure and checking its safety hydraulic performance. An investigation on the structural and mechanical equipment performance was performed by testing a scaled model to determine discharge capacity and head losses. In addition, the novel method established on Support Vector Machines (SVM) coupled through discrete wavelet transform was designed and adapted to estimate head loss at inlet and outlet section of the horizontal intake structure. Estimation and prediction results of SVM-WAVELET model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. The model test results of SVM WAVELET approach reveal more accuracy in prediction and also attain improved generalization capabilities than GP and ANN. Furthermore, results specified that advanced SVM-WAVELET model can be applied confidently for auxiliary research to formulate predictive model for head loss at inlet and outlet section. Consequently, it was found that using of SVM-WAVELET is principally encouraging as an alternate strategy to predict the head loss as a representative of inner pressure head at intake structure.

      PubDate: 2017-06-21T12:20:14Z
  • NIREUS: A new software for the analysis of on-demand pressurized
           collective irrigation networks
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): A. Stefopoulou, N. Dercas
      On-demand collective irrigation networks are hydraulic systems designed to deliver and distribute irrigation water from the water source to the irrigation perimeter, while providing users with the flexibility to decide on the time, the duration and the frequency with which they intend to use their hydrant. This paper presents the newly developed software NIREUS, which is developed for implementing the performance analysis of such networks. The performance analysis with NIREUS gives an overview of the operational status of the network under study by implementing the model of the indexed characteristic curves, while on the other hand, NIREUS determines the pipes/hydrants of the network that present, or are most likely to present, operational problems by implementing the performance analysis at hydrant level. This information is particularly useful for the management of existing networks as well as for the planning of future interventions and rehabilitation activities. The paper also describes the main functions of NIREUS and the characteristics that differentiate it from other existing software. Moreover, a new indicator which is incorporated in NIREUS is presented, in which the whole magnitude of the operational efficiency of a hydrant is depicted. The validation of the new software is made through a comparative application of NIREUS and COPAM to an existing on-demand pressurized collective irrigation network. Results proved the usefulness of NIREUS in highlighting both the weak and the strong parts of the network. The validation procedure gave a very close approximation of the respective values calculated with COPAM with the relative error ranging between 0.0% to 0.36% for the indexed characteristic curve of C50 and 0.01% to 0.22% for the indexed characteristic curves of C70.

      PubDate: 2017-06-21T12:20:14Z
  • Rotation invariant wavelet descriptors, a new set of features to enhance
           plant leaves classification
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Ehsan Yousefi, Yasser Baleghi, Sayed Mahmoud Sakhaei
      Automatic plant leaf recognition can play an important role in plant classification due to leaf’s availability, stable features and good potential to discriminate different kinds of species. Amongst many leaf features like leaf venation, margin, texture and lamina, leaf shape is the most important one due to its better discriminative power and ease of analysis. One of the most common leaf shape descriptors is Elliptic Fourier Descriptor (EFD). In this paper a new shape descriptor is introduced as “Rotation Invariant Wavelet Descriptor” (RIWD). The performance of RIWD is compared with IEFD using Flavia dataset. MLP neural network is used as the classifier in this work. Results analysis shows better performance of the proposed feature in classification accuracy. Furthermore, an optimum feature vector is constructed using a set of textural and morphological features and the RIWD that reached 97.5% classification accuracy with low computational cost in comparison with many reported results in Flavia dataset.

      PubDate: 2017-06-21T12:20:14Z
  • A geospatial decision support system for supporting quality viticulture at
           the landscape scale
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): F. Terribile, A. Bonfante, A. D'Antonio, R. De Mascellis, C. De Michele, G. Langella, P. Manna, F.A. Mileti, S. Vingiani, A. Basile
      The world of viticulture connected to wine making has become a very important activity in many inland areas permitting both the generation of important income and the sustaining of agriculture systems. Recent progress in both crop modeling and Decision Support Systems (DSS) applied to viticulture promises important changes that combine both high quality production and environmental sustainability. However, most of this progress is only addressed at the farm level and does not challenge the viticulture landscape, which is a key issue when facing DOC, DOCG areas, wine growers' cooperatives and consortiums and strategic viticulture planning. Thus, this paper aims to demonstrate that a new type of DSS, which is developed on a Geospatial Cyberinfrastructure (GCI) platform, may provide an important web-based operational tool for high quality viticulture as it connects farm and landscape levels better. The GCI platform supports acquisition, management, processing of both static and dynamic data (e.g. pedological, daily climatic, and vineyard distribution), data visualization, and on-the-fly computer applications in order to perform simulation modeling (e.g. grapevine water stress, evaluation of ecosystem services, etc.). These are all potentially accessible via the Web. This is possible thanks to the implementation of a set of modeling clusters that is strongly rooted in soil-plant-atmosphere and physically based simulation modeling. The DSS tool, applied to an area of 20,000ha in Southern Italy, is designed to address viticulture planning and management by providing operational support for farmers, farmer associations and decision makers involved in the viticulture landscape. Output of the system includes viticulture planning and management scenario analysis, maps and evaluation of potential and current plant water stress. The tool will also be demonstrated through a short selection of practical case studies.

      PubDate: 2017-06-21T12:20:14Z
  • An yield estimation in citrus orchards via fruit detection and counting
           using image processing
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Ulzii-Orshikh Dorj, Malrey Lee, Sang-seok Yun
      The overall goal of this study is to develop an effective, simple, aptly computer vision algorithm to detect and count citrus on the tree using image processing techniques, to estimate the yield, and to compare the yield estimation results obtained through several methods. This new citrus recognition and counting algorithm was utilized the color features (or schemes) to present an estimate of the citrus yield, and the corresponding models are developed to provide an early estimation of the citrus yield. Citrus images were taken from Jeju, South Korea during daylight and the citrus recognition and counting algorithm were tested on 84 images which were collected from 21 trees. The citrus counting algorithm consisted of the following steps: convert RGB image to HSV, thresholding, orange color detection, noise removal, watershed segmentation, and counting. Distance transform and marker-controlled watershed algorithms were evaluated for automated watershed segmentation in citrus fruits to obtain good result. A correlation coefficient R2 of 0.93 was obtained between the citrus counting algorithm and counting performed through human observation. The proposed algorithm showed great potential for early prediction of the yield of single citrus trees and the possibility of its uses for further fruit crops.

      PubDate: 2017-06-21T12:20:14Z
  • Novel approach to determine the influence of pig and cattle ears on the
           performance of passive UHF-RFID ear tags
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Felix Adrion, Anita Kapun, Eva-Maria Holland, Max Staiger, Paul Löb, Eva Gallmann
      The potential of passive ultra-high frequency radio frequency identification (UHF-RFID) as an electronic identification technology for precision livestock farming applications has been evaluated in different projects. Despite very promising advantages, such as a high read range and simultaneous identification of animals, the application of UHF transponders in ear tags still struggles with the strong influence that body tissue in the vicinity of the transponders has on the reading performance of the system. A detailed and precise investigation of the influence of ears on the transponder ear tags to support transponder development is hardly possible in on-farm tests with animals. Thus, the aim of this study was to develop an approach to measure the influence of pig and cattle ears on the received signal strength indicator (RSSI) and read range of UHF transponder ear tags on a test bench. In a second step, replacement of cattle and pig ears in the experiments with tissue models was tested to enhance the repeatability and comparability of results. Three sets of tests were performed with three different types of UHF transponders (1) to compare the influence on the read range and RSSI of the transponders at the front and back of pig and cattle ears, (2) to determine the repeatability of measurements with ears, and (3) to compare the influence of pig ears with that of two tissue models. Results showed significant differences between the front and back side of the ears for pig ears with better results at the back. The results for cattle ears were heterogeneous. The repeatability of measurements was low in all variants (front and back of pig and cattle ears) with repeatability coefficients of up to 10dBm (RSSI) and 217cm (read range). The tests generally demonstrated the strong and highly variable influence of ear tissue on the read range and RSSI of the transponders. Nevertheless, the results indicated that targeted detuning of UHF transponders can lower the influence of ear tissue on the reading performance, which is promising for the use of UHF-RFID in livestock farming. The method presented could be used in an optimised manner in the future to perform comprehensive tests and comparisons of different types of UHF transponder ear tags.

      PubDate: 2017-06-21T12:20:14Z
  • Pan evaporation modeling using four different heuristic approaches
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Lunche Wang, Zigeng Niu, Ozgur Kisi, Chang'an Li, Deqing Yu
      Evaporation plays important roles in regional water resources management, climate change and agricultural production. This study investigates the abilities of fuzzy genetic (FG), least square support vector regression (LSSVR), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree) and multiple linear regression (MLR) in estimating daily pan evaporation (Ep). Daily climatic data, air temperature (Ta), surface temperature (Ts), wind speed (Ws), relative humidity (RH) and sunshine hours (Hs) at eight stations in the Dongting Lake Basin, China are used for model development and validation. The first part of this study focuses on testing the model accuracies at each station using local input and output data. The results show that LSSVR and FG models with more input variables perform better than the MARS, M5Tree and MLR models in predicting daily Ep at most stations with respect to mean absolute errors (MAE), root mean square errors (RMSE) and determination coefficient (R2). In the second part of this study, the models are tested using cross-validation method in two different applications. The daily Ep of Yueyang station is estimated using the input and output data of Jingzhou and Changsha, respectively. Comparisons of the models indicate that the FG, LSSVR and MARS models outperform the M5Tree model, Ts, Hs and Ta are major influencing factors and adding Ws or RH into model inputs significantly improve the model performances. The overall results indicate that above models can be successfully used for estimating daily Ep using local input and output data while the FG and LSSVR generally perform better than the other models without local input and outputs.

      PubDate: 2017-06-21T12:20:14Z
  • Development of two dielectric sensors coupled with computational
           techniques for detecting milk adulteration
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Mahdi Ghasemi-Varnamkhasti, Nushin Ghatreh-Samani, Mojtaba Naderi-Boldaji, Michele Forina, Mojtaba Bonyadian
      Milk adulteration is a challenging concern which leads to economic losses, quality deterioration of the end products in dairy industry, and consequently public health risks. The demands for reliable and fast methodologies have been already increased in dairy industry. Therefore, developing simple, rapid, and reliable instrumental methods is a necessity for detecting the milk adulterations. This research effort was aimed to combine the sensor technology with computational techniques for the milk adulteration detection. For this purpose, two dielectric sensors, parallel-plate (PPC) and cylindrical stub resonator (CS), were developed and evaluated to examine a number of prevalent milk adulterations (sodium bicarbonate, water, sugar, urea, and gelatin) with a range of variation. Dielectric power and amplitude were recorded versus frequency (swept within 0–150MHz) for the PPC and CS sensors, respectively. The spectral data were then analyzed using some computational approaches consisted of SPLINE (moving cubic spline), NIPALS (principal component analysis), CLASS (classification and class modelling techniques), MRM (multivariate range modelling), TREE (classification trees), and PLS2 (correlation between blocks of variables)). Based on the results, the dielectric power spectra of the PPC sensor showed the frequency range of 15–60MHz as the most sensitive band with respect to different adulterations and the amplitude-frequency response of the CS sensor revealed remarkable changes in the amplitude at the resonance frequencies. According to the data analysis, for PPC, two matrices were studied, with different range of the frequencies and it was proved that the data matrix with 145 variables has more discriminant information. Also, in the case of PPC data, classification tree showed the best result with 80% prediction ability for the milk samples while full classification accuracy was found for CS data. As a consequence, it was concluded that the adulterations in milk can be screened by both sensors coupled with computational techniques. Therefore, the methodologies presented here could be considered as a candidate for potential use in dairy industry.

      PubDate: 2017-06-21T12:20:14Z
  • Towards automatic tree rings detection in images of scanned wood samples
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Anna Fabijańska, Małgorzata Danek, Joanna Barniak, Adam Piórkowski
      In this paper, the problem of automation of dendrochronological measurements is considered. In particular, a fully automatic, image-based approach for detecting tree-ring boundaries in wood core images is introduced. The method is based on image gradient peak detection and linking. Unlike other existing approaches, the proposed method performs well on a variety of wood types and corresponding tree species. The results of applying the proposed approach to scanned images of wood cores representing 12 tree species (4 conifer and 8 angiosperm) are presented and discussed. Analysis of the results shows that the method performs almost faultlessly for conifer species, detecting almost 100% of tree-ring boundaries. In the case of diffuse-porous species, the results are not quite as good, but still at a level of 85% of properly detected tree-ring boundaries. For ring-porous species, the method has some problems, which are described in the paper.

      PubDate: 2017-06-21T12:20:14Z
  • A semi-automatic and an automatic segmentation algorithm to remove the
           internal organs from live pig CT images
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Pau Xiberta, Imma Boada, Anton Bardera, Maria Font-i-Furnols
      Removal of internal organs such as lungs, liver, and kidneys is a key step required to compute the lean meat percentage from Computed Tomography (CT) scans of live animals. In this paper, we propose two segmentation techniques to remove these organs focusing on pigs. The first method is semi-automatic, and it starts with the first CT slice and a manually defined mask with internal organs. Then, it applies a four-step iterative process that computes the masks of the next CT slices by using the information of the previous one. To find the best boundary it uses a Dynamic Programming-based approach. At each iteration the user can check the correctness of the new computed mask. The second method is fully automatic, and segments each slice individually by using distance maps and morphological operators, such as dilation. It is composed of three main steps which detect the pig’s torso, pre-classify the voxels in different tissues, and segment the internal organs using the information of such classification. Although it has some parameters, user interaction is not required to obtain the results. The proposed approaches have been tested on CT data sets from 9 pigs, and compared with a manual segmentation. To evaluate the results, the precision, recall, and F-score measures have been used. From our test, we can observe that the performance of both methods is very high according to their average F-score. We also analyse how the accuracy of the results in the semi-automatic approach increases when more user interaction is applied. For the automatic approach, we evaluate the dependence of the results on the algorithm’s parameters. If robustness is enough, and high accuracy is not required, the automatic algorithm can be used to segment a whole pig in less than 50s. However, if the user wants to control the level of accuracy, the semi-automatic algorithm is preferred. Both methods are useful to reduce the time needed to segment the internal organs of a pig from hours (manual segmentation) to minutes or seconds.

      PubDate: 2017-06-21T12:20:14Z
  • Contactless and non-destructive chlorophyll content prediction by random
           forest regression: A case study on fresh-cut rocket leaves
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Dario Pietro Cavallo, Maria Cefola, Bernardo Pace, Antonio Francesco Logrieco, Giovanni Attolico
      In green leafy vegetables, the retention of green colour is one of the most generally used index to evaluate the overall quality and freshness and it is associated to total chlorophyll content. Destructive chemical techniques and non-destructive chlorophyll meters represent the state-of-the-art methods to accomplish such critical task. The former are effective and robust but also expensive and time consuming. The latter are cheaper and faster but exhibit lower reliability, require the probe to touch the leaves and heavily depend on the positions chosen for sampling the leaf’s surface. In this paper, a new approach to non-destructively predict total chlorophyll content of fresh-cut rocket leaves without contact is proposed. Fresh-cut rocket leaves were analysed for total chlorophyll content by spectrophotometer and SPAD-502 (used as reference values) and acquired by a computer vision system using a machine-learning model (Random Forest Regression) to predict total chlorophyll content. Finally, the trained and validated model will be used for on-line prediction of total chlorophyll content of unseen fresh-cut rocket leaves. The proposed system can match the physical and timing constraints of a real industrial production line and its performance (R2 =0.90), measured on the case study of fresh-cut rocket leaves, outperformed the results of the SPAD chlorophyll meter (R2 =0.79).

      PubDate: 2017-06-21T12:20:14Z
  • Laser-induced backscattering imaging for classification of seeded and
           seedless watermelons
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Maimunah Mohd Ali, Norhashila Hashim, Siti Khairunniza Bejo, Rosnah Shamsudin
      This paper evaluates the feasibility of laser-induced backscattering imaging for the classification of seeded and seedless watermelons during storage. Backscattering images were obtained from seeded and seedless watermelon samples through a laser diode emitting at 658nm using a backscattering imaging system developed for the purpose. The pre-processed datasets extracted from the backscattering images were analysed using principal component analysis (PCA). The datasets were separated into training (75%) and testing (25%) datasets as the inputs in the classification algorithms. Three multivariate pattern recognition algorithms were used including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbour (kNN). The QDA-based algorithms obtained the highest overall average classification accuracies (100%) for both the seeded and seedless watermelons. The LDA and kNN-based algorithms also obtained quite high classification accuracies with all the accuracies above 90%. The laser-induced backscattering imaging technique is potentially useful for classification of seeded and seedless watermelons.

      PubDate: 2017-06-21T12:20:14Z
  • Validation of a low-cost 2D laser scanner in development of a
           more-affordable mobile terrestrial proximal sensing system for 3D plant
           structure phenotyping in indoor environment
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Huanhuan Wang, Yi Lin, Zeliang Wang, Yunjun Yao, Yuhu Zhang, Ling Wu
      Plant phenotyping plays a critical role in grasping plant architectures and understanding plant responses to environment changes. Hence, various plant phenotyping techniques have kept being developed for different scenarios, via introducing diverse remote sensing (RS) techniques such as light detection and ranging (LIDAR). Now, one important trend of this field is developing low-cost 3D systems that are affordable by common users, whereas the often-used LIDAR sensors with high costs cannot satisfy this demand. To handle this issue, this study attempted to develop a low-cost 2D laser scanner based mobile terrestrial proximal sensing system for 3D plant structure phenotyping in indoor environment. Specifically, two RPLIDAR laser scanners, as one kind of the lowest-cost 2D LIDAR sensors in the contemporary market, are installed at the two far-ends of the scanner-fixing frame on the mobile platform, with their scan profiles set in an oblique-crossing way. Then, the movement of the platform, after accurate data georeferencing and calibration, can render the two series of 2D scanning profiles in parallel into a full 3D representation of each row of plants of interest. Based on the resulting 3D point clouds, detailed plant structure features can be derived. Tests showed that the proposed solution has been basically validated, in terms of the specific plant structure variables such as leaf area (R2 =0.92). Overall, this work has pushed forward the development of LIDAR-based plant phenotyping techniques into the real-sense low-cost stage, and this suggests that more and more practical applications of LIDAR for plant phenotyping may occur in the communities such as plant cultivation and precision agriculture.

      PubDate: 2017-06-15T12:06:21Z
  • Simulation model for sour cherry product lines
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Z. Mézes, K. Szenteleki, M. Gaál
      Modelling and simulation are of high importance in agriculture and climate change research. This paper aims to simulate and analyse the sour cherry product lines in Hungary. The model presented below is suitable to analyse the market share, the cost and income relations as well as the relation structure of the sour cherry product lines. The vertical levels of the model (soil and climatic conditions, varieties, size of orchards, product type and trade channels) have individual values added from the aspect of the end product. Theoretically, all elements can be connected to any element of the next level, but there can be “prohibited contacts” because of professional, regulation or production practice reasons. The model makes possible to evaluate, compare and filter the different product lines, analyse the effects of the climate change or the use of different varieties, and thereby provides support for strategic planning and decision making.

      PubDate: 2017-06-15T12:06:21Z
  • Shape and size of parcels and transport costs as a mixed integer
           programming problem in optimization of land consolidation
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Stanisław Harasimowicz, Jarosław Janus, Stanisław Bacior, Jacek Gniadek
      This article presents a new approach to the problem of optimization of land arrangement. The new element is the use of binary variables in the calculation model using the principles of linear programming. For this reason the optimization model can be classified into the category of mixed integer programming problems (MIP). The proposed optimization model takes into account a number of very important factors in the process of land consolidation, including map of the land diversification or the actual shape of the transportation network. That also implies taking into account the real, not the straight-lined distances between different parts of the model. The solution of the model is an arrangement of plots minimizing the costs associated with the cultivation of these plots, depending on their shapes and distances from homesteads. The value of the land of individual farms before and after the consolidation is preserved. Optimization model has been saved and executed in the environment of the GLPK (GNU Linear Programming Kit) software package. The presented method has been used to carry out the optimization process on the test object of the area of 587 hectares. The outcome is a new layout of plots in a form which meets all the technical requirements required in the real project process.

      PubDate: 2017-06-11T11:25:13Z
  • Controlled comparison of machine vision algorithms for Rumex and Urtica
           detection in grassland
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): A. Binch, C.W. Fox
      Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision methods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standardised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machine. 1 This research was supported in part by the Innovate UK project IBEX2: Autonomous robot weed spraying for less favoured areas, Grant No. 131790. 1

      PubDate: 2017-06-11T11:25:13Z
  • Integrated network design of wheat supply chain: A real case of Iran
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Mohammad Reza Gholamian, Abdul Hakim Taghanzadeh
      This study proposes designing the integrated supply chain of wheat products that includes long-term decisions of supplier selection and locating new silos and mid-term decisions of assignment and distribution of the wheat and its products. The proposed model aims at selecting the suppliers, determining amount of the import, distribution of wheat, and production of its products. The model is developed by considering blending of different types of the wheat for producing different products, locating new silos and different modes of transportation in all levels of the chain, and also adding the export sector. Unlike the most of previous researches, the current research proposes an integrated planning model that is able to consider all effective levels and factors on the chain. The application of this model is surveyed in a case study for Iran that demonstrates considerable cost savings. By solving the model, the results illustrate the significant reduction in transportation costs. Meanwhile, the sensitivity analysis has been performed on changing of the most important parameter values of the model to show the effect of them on objective function. Finally, practical suggestions from the output results are brought in conclusion.

      PubDate: 2017-06-11T11:25:13Z
  • A WebGIS-based decision support system for locust prevention and control
           in China
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Xiaochuang Yao, Dehai Zhu, Wenju Yun, Fan Peng, Lin Li
      Locust swarms are destructive agricultural and biological disasters in China. The green prevention and control (GPC, such as ecological regulation and physical control) of locusts is a comprehensive and complex process, especially in information technology. In this study, a web-based decision support system (DSS) integrated with geographic information system (GIS) is developed to prevent and control locusts efficiently, accurately, and rapidly. The locust prevention and control DSS (LPCDSS) is developed to assist farmers and local government agencies in Chinese provinces with high incidence of locust by providing spatial decision-making information. LPCDSS offers online access to county, city, provincial, and national level data queries and is capable of storing, spatial analyzing, and displaying geographically referenced information of locust data. The system can also provide the real-time tracking of global positioning system (GPS) location, as well as goods scheduling of locust plagues prevention. Six types of web service, real-time data synchronization model, and locust population estimation model are developed and implemented to improve the decision-making usability and feasibility of LPCDSS by adopting a three-layer system architecture. The system is developed by using several programming languages, libraries, and software components. As a result, this system has been running successfully for several years and has improved efficiency of the locust prevention and control in China with high efficiency and great accuracy. The approaches and methodologies presented in this paper can serve as a reference for those who are interested in developing integrated pest control system applications.

      PubDate: 2017-06-11T11:25:13Z
  • A method of green litchi recognition in natural environment based on
           improved LDA classifier
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Zhi-Liang He, Jun-Tao Xiong, Rui Lin, Xiangjun Zou, Lin-Yue Tang, Zhen-Gang Yang, Zhen Liu, Ge Song
      Green litchi is always difficultly recognized by picking robot under the natural environment because of its similar color feature with background. A method of green litchi recognition based on improved LDA classifier is proposed by this paper. The color features of RGB components of litchi images were firstly analyzed. Then a linear discriminant analysis (LDA) method used for extracting convolutional features for classifying pixels of image was proposed to train the convolution kernel based on 1600 sample pixels. Simultaneously, an idea of ‘maximal margin’ from SVM to calculate the threshold of LDA classifier was introduced, and the corresponding threshold calculation method was put forward. The AdaBoost method was used in integration of a strong multiple LDA classifier. After classifying pixels, the Hough transform circle detection was used to locate the fruit of litchi by the sphere shape feature. Experiments with the proposed method show that green litchi recognition precision rate is 80.4% and the recall rate is 76.4%. This study provides technical support for the visual identification of green litchi and even other green fruits in natural environment.

      PubDate: 2017-06-11T11:25:13Z
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139

      PubDate: 2017-06-11T11:25:13Z
  • A wireless device for continuous frond elongation measurement
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): Jingbo Zhen, Effi Tripler, Xiangqi Peng, Naftali Lazarovitch
      Growth rate is one of the indicators for a plant’s physiological condition. Date palms are characterized by high frond elongation rates, which are mainly subjected to drought and salinity stresses. Thus, continuous measurement of these rates can provide real-time growth information, for assessing water status within the soil-plant-atmosphere continuum of cultivated date palms. This study introduces a novel device, the Palmeter, which continuously measures real-time date palm frond elongation. The Palmeter was calibrated in the laboratory and tested in a date palm orchard with a measurement resolution of 0.52mm. A field test indicated that the Palmeter could wirelessly transmit acquired data to a signal receiver over a distance of 100m with a success rate of more than 98%, facilitating the establishment of wireless sensor networks in date palm orchards. Neither temperature nor wind affected the Palmeter measurement within the orchard. The temporal patterns of the frond elongation measured by the Palmeter were found to be sensitive to various cultivation treatments, such as fruit load regimes, applied within a field study. Additionally, a six-volt power supply is recommended in order to reduce the Palmeter’s power consumption. The feasibility and robustness of the Palmeter system guaranteed the accurate measurement of the frond elongation under harsh field conditions. Therefore, the Palmeter can be potentially applied to measure the frond elongation of date palms and perhaps other palms, such as oil palms and coconut palms, for irrigation scheduling and cultivation management in large orchards.

      PubDate: 2017-06-06T11:16:41Z
  • Application of UAV imaging platform for vegetation analysis based on
           spectral-spatial methods
    • Abstract: Publication date: August 2017
      Source:Computers and Electronics in Agriculture, Volume 140
      Author(s): J. Senthilnath, Manasa Kandukuri, Akanksha Dokania, K.N. Ramesh
      This paper presents application of UAV imaging platforms for vegetation analysis. Remote sensing using a UAV, also known as low altitude remote sensing is performed to acquire RGB images for vegetation analysis. Two UAV platforms, a VTOL quadcopter and a fixed wing UAV, were used to obtain the images. Crop region classification was carried out on images acquired from VTOL quadcopter to demonstrate its use in applications such as inspections that require hovering of UAVs while tree crown classification was carried out on images acquired from the fixed wing UAV to demonstrate its use in applications that requires coverage over a relatively larger area. Classification was performed for crop region mapping and tree crown mapping using spectral-spatial method. In this proposed method, Bayesian information criterion was used to determine the constraint of optimal number of clusters for a given image. Keeping this constraint, divisive approach was performed using k-means and EM algorithm for clustering the dataset. On these clusters, the agglomerative approach was used to merge the dataset. The merging was done using percentage voting. Further, to improve the classification efficiency, spatial classification was applied. UAV images obtained using the two UAV platforms were used to demonstrate the performance of the proposed algorithm. A performance comparison of the proposed spectral-spatial classification with the other classification methods is presented. From the obtained results, it was concluded that the proposed spectral-spatial classification performs better and was more robust than the other algorithms in the literature.

      PubDate: 2017-06-06T11:16:41Z
  • Classification of foreign matter embedded inside cotton lint using short
           wave infrared (SWIR) hyperspectral transmittance imaging
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Mengyun Zhang, Changying Li, Fuzeng Yang
      Cotton is an important source of natural fiber around the world. Cotton lint, however, could be contaminated by various types of foreign matter (FM) during harvesting and processing, leading to reduced quality and potentially even defective textile products. Current sensing methods can detect the presence of foreign matter on the surface of cotton lint, but they are not able to efficiently detect and classify foreign matter that is mixed with or embedded inside cotton lint. This study focused on the detection and classification of common types of foreign matter hidden within the cotton lint by a short wave near infrared hyperspectral imaging (HSI) system using the transmittance mode. Fourteen common categories of foreign matter and cotton lint were collected from the field and the foreign matter particles were sandwiched between two thin cotton lint webs. Operation parameters were optimized through a series of experiments for the best performance of the transmittance mode. After acquiring transmittance images of the cotton lint and foreign matter mixture, minimum noise fraction (MNF) rotation was utilized to obtain component images to assist visual detection and mean spectraextractionfrom a total of 141 wavelength bands. The optimal spectral bands were identified by using the minimal-redundancy-maximal-relevance (mRMR)-based feature selection method. Linear discriminant analysis (LDA) and a support vector machine (SVM) were employed to classify foreign matter at the spectral and pixel level, respectively. Over 95% classification accuracies for the spectra and the images were achieved using the selected optimal wavelengths. This study indicated that it was feasible to detect botanical (e.g. seed coat, seed meat, stem, and leaf) and non-botanical (e.g. paper, and plastic package) types of foreign matter that were embedded inside cotton lint using short wave infrared hyperspectral transmittance imaging.

      PubDate: 2017-05-26T16:35:16Z
  • Embedded digital drive wheel torque indicator for agricultural 2WD
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): A. Ashok Kumar, V.K. Tewari, Brajesh Nare, C.R. Chetan, Prateek Srivastava, Satya Prakash Kumar
      A microcontroller based embedded system was developed to measure and display the dynamic wheel axle torque and drawbar power of agricultural tractor for tillage research. The device includes a special transducer to measure dynamic drive wheel torque of tractor, an embedded wireless digital system to receive process and display data digitally as well as record in SD card module near the dash board of the tractor. The embedded system mainly consists of an amplifier to amplify the transducer signal, a transmitter unit to process the amplifier data, and a receiver unit to receive and display the transmitter unit data. The developed system was rigorously tested under laboratory and actual field conditions. It was found that, a maximum variation of ±320Nm torque between the theoretically calculated and experimentally observed values under field conditions. With the developed devices the real time power requirement of various agricultural operations could be known to help the tillage researchers. The developed device and systems are simple and accurate and can be used for any range of agricultural tractors.

      PubDate: 2017-05-26T16:35:16Z
  • Using MARS, SVM, GEP and empirical equations for estimation of monthly
           mean reference evapotranspiration
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Saeid Mehdizadeh, Javad Behmanesh, Keivan Khalili
      Evapotranspiration is one of the most important components of hydrologic cycle for optimal management of water resources, especially in arid and semi-arid regions such as Iran. The main objective of the present research is to investigate the performance of empirical equations and soft computing approaches including gene expression programming (GEP), two types of support vector machine (SVM) namely SVM-polynomial (SVM-Poly) and SVM-radial basis function (SVM-RBF), as well as multivariate adaptive regression splines (MARS) in estimating monthly mean reference evapotranspiration (ETo) in Iran. In the present study, 16 empirical equations from temperature-based, mass transfer-based, radiation-based and meteorological parameters-based categories were utilized. Monthly mean data of 44 stations in the study region was used to estimate the monthly mean ETo. 50% of the data (22 stations) for the calibration/training step and the remaining 50% of the data (22 stations) were applied for the validation/testing stage of the empirical equations/soft computing methods. At first, 16 empirical equations were locally calibrated on the basis of FAO-56 Penman-Monteith method (as standard method). The results revealed that the calibration process improved the performance of equations in comparison with the original form of them. Then, the capability of the GEP, SVM-Poly, SVM-RBF and MARS models was evaluated for estimation of the monthly mean ETo. The selection of models’ inputs was conducted based on the used parameters in the empirical equations. It was found that the MARS and SVM-RBF methods generally performed better than GEP and SVM-Poly. At the end part of study, the accuracy of empirical equations and soft computing methods was compared. Overall, the performance of the MARS and SVM-RBF was better than used empirical equations.

      PubDate: 2017-05-26T16:35:16Z
  • Species – Transport CFD model for the gasification of rice husk (Oryza
           Sativa) using downdraft gasifier
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): P.C. Murugan, S. Joseph Sekhar
      The biomass as a source of energy is increasing globally due to environmental pollution and shortage of fossil fuels. The process of converting the biomass into useful energy is known as gasification. The implementation of gasification technology in remote areas needs more theoretical and experimental studies. Several models have been proposed to explain the geometrical parameters and the optimization of gasification process. Hence, the paper presents the numerical simulation of the gasifier using the species-transport model in CFD by incorporating all the zones of the gasifier and the experimental studies are conducted on a 40kW downdraft gasifier with rice husk (Oryza Sativa) available in remote villages situated in South India. The factors affecting the producer gas such as equivalence ratio, gas composition, higher heating value and the temperature distribution inside the gasifier have been studied. Both the studies established that the higher heating value and the producer gas composition are 5.19MJ/Nm3 with CO–22%, CH4–1.7%, CO2–8%, H2–13% and N2–40.1% respectively. Also, it is predicted that the zone temperatures are maximum when ER is 0.30.
      Graphical abstract image

      PubDate: 2017-05-21T16:27:35Z
  • Variation analysis in spectral indices of volatile chlorpyrifos and
           non-volatile imidacloprid in jujube (Ziziphus jujuba Mill.) using
           near-infrared hyperspectral imaging (NIR-HSI) and gas chromatograph-mass
           spectrometry (GC–MS)
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Wen-Hao Su, Da-Wen Sun, Jian-Guo He, Ling-Biao Zhang
      Two pesticides in terms of chlorpyrifos and imidacloprid contaminating edible jujube fruits were determined using hyperspectral imaging (900–1700nm) and gas chromatograph-mass spectrometry (GC–MS). Hyperspectral images of jujube samples contaminated by pesticides at different concentrations were collected. Their spectral data extracted in reflectance (RS), absorbance (AS), exponent (ES) and Kubelka–Munck (K-MS), were respectively used to develop partial least squares discriminant analysis (PLSDA)and locally weighted partial least square regression (LWPLSR) models. Based on these spectral parameters, corresponding models defined as AS-PLSDA and ES-PLSDA acquired optimal results, with correlation coefficients of cross-validation (R CV) of more than 0.900 for recognition of chlorpyrifos concentrations and R CV of over 0.713 for identification of concentrations of the imidacloprid. The ES-LWPLSR model obtained the best R CV of 0.864 for quantitative determination of chlorpyrifos residuals, and the best R CV of 0.885 for determination of imidacloprid residuals. The feature wavelengths were selected based on the automatic weighted least squares and gap segment derivative (AWLS-GSD) coupled with regression coefficient (RC) method. The better performance was obtained by the resulting simplified ES-AWLS-GSD-RC-LWPLSR model established using only eight characteristic wavelengths, with R CV of 0.757, RMSECV of 3.75×10−3 for chlorpyrifos residuals, and R CV of 0.898, RMSECV of 0.311×10−3 for imidacloprid residuals. To summarize, hyperspectral imaging technology shows a great potential to predict pesticide residuals of jujube fruit.

      PubDate: 2017-05-21T16:27:35Z
  • Optimization of QR code readability in movement state using response
           surface methodology for implementing continuous chain traceability
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Jianping Qian, Xiaowei Du, Baoyan Zhang, Beilei Fan, Xinting Yang
      Logistics and storage is the main processing for agro-food supply chain. Because of disconnection information between the two processing, it is difficult to trace continuously. An intelligent conveyer belt provides an effective method to associate storage and logistics by QR code scanning and information recording. Improving the QR code readability in movement state is the core of implementing continuous chain traceability with this belt. In this paper, a intelligent conveyer belt including automatic conveyer unit, barcode scanning unit, fault remove unit and control display unit was designed. Four factors affected QR readability were selected and the value range was confirmed, which was reading distance, code size, coded characters and belt moving speed. Based on the belt, an Central Composite Inscribed (CCI) experiment of four factors with five levels was designed using Response Surface Methodology (RSM) to obtain the optimal reading parameters. The result shows that the main factors of reading distance, belt moving speed and the interaction between reading distance and code size have the significant effect on QR code readability. Under the optimization condition of 141.45mm reading distance, 34.58mm code size, 100 bytes coded characters and 2.98m/min belt moving speed, the average value of QR code readability was 95%. With the optimization parameters, the intelligent conveyer belt was used in an apple marketing enterprise. The result shows that the continuous traceability between storage and logistic can be implemented with the extended breadth, deepened depth and improved precision.

      PubDate: 2017-05-21T16:27:35Z
  • Modeling pressure drop produced by different filtering media in
           microirrigation sand filters using the hybrid ABC-MARS-based approach, MLP
           neural network and M5 model tree
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): P.J. García Nieto, E. García-Gonzalo, J. Bové, G. Arbat, M. Duran-Ros, J. Puig-Bargués
      Granular media filters are commonly used to remove suspended solids and prevent emitter clogging in microirrigation systems. Silica sand is the standard filtering media but other granular materials can be used for this purpose. The characterization of the pressure drop produced by the clean filtering media is of practical interest for designing and managing these filters. Different models such as Ergun or Kozeny-Carman equations are usually used to predict pressure drop produced by the filtering media. However, as parameters of the media such as equivalent diameter and sphericity, that are difficult to determine, appear in these equations, the objective of this study was to construct a new model to estimate the pressure drop of different filtering materials of interest in granular filters with limited data of the physical parameters that characterize the filtering media. This paper, taking as starting point the multivariate adaptive regression splines (MARS), develops a new algorithm hybridizing it with the artificial bee colony (ABC) method, to estimate the pressure drop in granular filters broadly used in microirrigation systems for the first time with a data-driven model. Laboratory experiments were used to measure pressure drop across silica sand, crushed glass, modified glass and glass microspheres in a scaled filter at surface velocities ranging from 0.004 to 0.025ms−1. The ABC method allows the tuning of the MARS parameters during the training phase improving significantly the regression accuracy. Additionally, a multilayer perceptron network (MLP) and M5 model tree were fitted to the experimental data for comparison purposes. The results have shown that ABC-MARS-based model was the best estimation of the pressure drop with a coefficient of determination of 0.78. Therefore, ABC-MARS-based model could be easily implemented to predict pressure drop with minimal input parameters for other filtering materials used in microirrigation media filters.

      PubDate: 2017-05-21T16:27:35Z
  • Computer vision based method for quality and freshness check for fish from
           segmented gills
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Ashish Issac, Malay Kishore Dutta, Biplab Sarkar
      The quality and freshness of a fish sample is principally hampered in the post-harvested phase due to storage, handling and processing. The quality of the sample may degrade as the days pass till it finally reaches the consumers. The quality of a post harvested fish is determined mainly by two important factors namely climatic conditions and holding time. This paper presents a completely automated computer vision based segmentation of fish gills from digital images of fish samples. Post segmentation, a statistical relationship of the segmented gill region is established to design an assessment model for fish freshness identification. The fish gills are segmented using various strategic image processing techniques like contrast enhancement, adaptive intensity threshold and active contour based methods. The model for fish freshness testing is based on the image statistical features which are derived from the gills region of the saturation channel. The variation of the statistical distribution is observed to be decreasing monotonic which is basis for design of the framework for fish quality and freshness identification. This process being non-destructive provides an efficient fish quality assessment scheme in real time.

      PubDate: 2017-05-16T15:11:04Z
  • Fine crop mapping by combining high spectral and high spatial resolution
           remote sensing data in complex heterogeneous areas
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Mingquan Wu, Wenjiang Huang, Zheng Niu, Yu Wang, Changyao Wang, Wang Li, Pengyu Hao, Bo Yu
      In complex heterogeneous areas, it is difficult to map crops with high accuracy using only high spatial resolution or only high spectral resolution remote sensing data. Because the spectral resolution of high spatial resolution data is too low, the spectral differentiations of different vegetation types are very small in high spatial resolution data. It is hard to distinguish between different vegetation types using high spatial resolution data. For high spectral resolution remote sensing data, it is hard to exclude linear objects like roads, bridges and drains from crops due to the low spatial resolution of these data. To address this problem, a novel object-based fine crop mapping method by combining high spatial and high spectral resolution remote sensing data for heterogeneous areas was proposed and validated in Suzhou city, Jiangsu province, China. First, pure crop polygons were derived from a 0.5m aerial data. Due to the high spatial resolution, non-cultivated land could be easily isolated from arable land. Then, a Hyperion data was used to classify crops for each of the pure crop polygons. The results show that this method can map crops in complex heterogeneous areas with an overall accuracy higher than 95%, which is much higher than the accuracy of maps classified using only high spatial resolution data or only high spectral resolution data, which have an overall accuracy of 58.78% and 77.54%, respectively.

      PubDate: 2017-05-11T15:05:46Z
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