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ENGINEERING (1213 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: 7)
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: 254)
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: 9)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 7)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 18)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 6)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 11)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 22)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 27)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 10)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 30)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 12)
Advances in Polymer Science     Hybrid Journal   (Followers: 41)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 40)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aerobiologia     Hybrid Journal   (Followers: 2)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 6)
AIChE Journal     Hybrid Journal   (Followers: 32)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access   (Followers: 1)
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: 17)
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: 8)
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: 18)
Applied Clay Science     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Network Science     Open Access   (Followers: 1)
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: 5)
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: 8)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 3)
Asia-Pacific Journal of Science and Technology     Open Access  
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: 9)
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: 5)
Batteries     Open Access   (Followers: 6)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 24)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 4)
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: 4)
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 10)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 18)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 34)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 4)
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   (Followers: 1)
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: 14)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 30)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 44)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 8)
Case Studies in Thermal Engineering     Open Access   (Followers: 4)
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: 8)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 7)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
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: 24)
Clay Minerals     Full-text available via subscription   (Followers: 10)
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: 5)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 2)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
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: 27)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Composite Structures     Hybrid Journal   (Followers: 271)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 199)
Composites Part B : Engineering     Hybrid Journal   (Followers: 256)
Composites Science and Technology     Hybrid Journal   (Followers: 194)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 15)
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: 8)
Computer Science and Engineering     Open Access   (Followers: 19)
Computers & Geosciences     Hybrid Journal   (Followers: 30)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 7)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 5)
Computers and Geotechnics     Hybrid Journal   (Followers: 11)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 33)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 8)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 9)
Control Engineering Practice     Hybrid Journal   (Followers: 43)
Control Theory and Informatics     Open Access   (Followers: 8)
Corrosion Science     Hybrid Journal   (Followers: 25)
Corrosion Series     Full-text available via subscription   (Followers: 6)
CT&F Ciencia, Tecnologia y Futuro     Open Access   (Followers: 1)

        1 2 3 4 5 6 7 | Last

Journal Cover Computers and Electronics in Agriculture
  [SJR: 0.823]   [H-I: 73]   [5 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0168-1699
   Published by Elsevier Homepage  [3123 journals]
  • Nondestructive assessments of carotenoids content of broadleaved plant
           species using hyperspectral indices
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Rei Sonobe, Quan Wang
      Carotenoids play important roles regarding photoprotection as well as light harvesting during the process of photosynthesis, resulting in the opportunity of quantifying carotenoids content to evaluate the productivity of vegetation. The traditional approaches such as ultraviolet and visible (UV–vis) spectroscopy are destructive and hence do not allow to determine the temporal dynamics of carotenoids content over time. As a promising alternative, hyperspectral remote sensing provides a way to evaluate carotenoid content changes over time and at multiple scales. Furthermore, it is easier to expand such approaches for large scale monitoring. However, to identify a generally applicable hyperspectral index sensitive to carotenoids remains a big challenge. In this study, we have evaluated thirteen available hyperspectral indices to quantify carotenoids, based on four independent datasets including two field datasets from Japan and two publicly available datasets (LOPEX and ANGERS). We attempted to develop a new generally applicable hyperspectral index for broadleaved plant species using the original and first derivative reflected spectra of the four datasets. We found that dND (516,744), a normalized differences type index using reflectance derivatives at 516 and 744 nm ( ( D 516 - D 744 ) / ( D 516 + D 744 ) ), had the highest robustness among all datasets and also was the best index when all data were combined (R2 = 0.475, WAIC = 2430.1, and RPD = 1.45 for all datasets), suggesting its potential for general applications. Further extensive evaluations of the proposed index in other types of plants is required to test whether it can also be applied in other than broadleaved species.

      PubDate: 2018-02-03T03:23:58Z
  • Visible-near infrared spectrum-based classification of apple chilling
           injury on cloud computing platform
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Ji'An Xia, YuWang Yang, HongXin Cao, Chen Han, DaoKuo Ge, WenYu Zhang
      This paper evaluates the feasibility of applying cloud computing technology for spectrum-based classification of apple chilling injury. The reflectance spectra of Fuji apples with four different levels of chilling injury (none, slight, medium, and severe) were collected. During data processing, the spectra at 400–1000 nm were selected, and first- and second-order-derivative spectral data sets were obtained through integral transformations. Five optimal wavebands were chosen as inputs for the classification models. A cloud computing framework based on Spark and the MLlib machine learning library was used to realize multivariate classification models based on an artificial neural network (ANN) and support vector machine (SVM). The ANN and SVM classification models were used for multivariate classification and analysis of the spectral data sets (raw, first derivative, second derivative) and corresponding optimal wavebands. Of the total data samples, 70% were used for training, while the remaining 30% were used for prediction. The experimental results showed that, by using the cloud computing platform, we could establish an efficient spectrum classification model of apple chilling injury; the ANN model had slightly higher accuracy than the SVM model (not including the second-derivative spectra), but the SVM model was more efficient. Moreover, the classification accuracy using full-waveband spectral data sets was higher than that of data sets using five optimal wavebands. Furthermore, the Spark framework and MLlib were used to implement binary classification models (decision tree and random forest), and these were compared with the multivariate classification model; the binary classification method had better performance in near-infrared spectrum-based classification of apple chilling injury. Finally, we extended the existing spectrum data set to verify the efficiency of the cloud computing platform and desktop PC for handling larger data sets. The results showed that the efficiency of the cloud computing platform was significantly improved by increasing the spectral data set capacity or number of working nodes. Owing to processor and memory limitations, the classification algorithm and model of abundant spectral data sets cannot complete all of the tasks on a desktop PC.

      PubDate: 2018-02-03T03:23:58Z
  • Automatic classification of plant electrophysiological responses to
           environmental stimuli using machine learning and interval arithmetic
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Danillo Roberto Pereira, João Paulo Papa, Gustavo Francisco Rosalin Saraiva, Gustavo Maia Souza
      In plants, there are different types of electrical signals involving changes in membrane potentials that could encode electrical information related to physiological states when plants are stimulated by different environmental conditions. A previous study analyzing traits of the dynamics of whole plant low-voltage electrical showed, for instance, that some specific frequencies that can be observed on plants growing under undisturbed conditions disappear after stress-like environments, such as cold, low light and osmotic stimuli. In this paper, we propose to test different methods of automatic classification in order to identify when different environmental cues cause specific changes in the electrical signals of plants. In order to verify such hypothesis, we used machine learning algorithms (Artificial Neural Networks, Convolutional Neural Network, Optimum-Path Forest, k-Nearest Neighbors and Support Vector Machine) together Interval Arithmetic. The results indicated that Interval Arithmetic and supervised classifiers are more suitable than deep learning techniques, showing promising results towards such research area.

      PubDate: 2018-02-03T03:23:58Z
  • Development and evaluation of key ambient factors online monitoring system
           in live Urechis unicinctus transportation strategies
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Yongjun Zhang, Xiaoshuan Zhang, Mai Thi Tuyet Nga, Liufeng, Hairui Yu
      This paper puts forward the reasonable choice of live transportation strategy to guarantee survival rate and quality of Urechis unicinctus by designing its packaging mode and carrying out real-time monitoring technologies. Ambient data sensing devices are designed and deployed in water and waterless transportation facilities by which the simulation of real live transportation is tested. During the delivery process of live Urechis unicinctus, the key ambient factors – temperature, dissolved oxygen/oxygen, carbon dioxide, PH, salinity–are dynamically sampling by on-line electronic monitoring equipment. Urechis unicinctus transport ambient data and their correlations are statistically calculated and analyzed by transportation monitoring and analysis system for the two transportation strategies. Through five control groups test for water and waterless shipment, it is found that waterless transportation is more suitable for over 30 h live transportation by studying transportation facilities management, surface characteristics and survival rate, which can provide a sound statistical basis of reasonable transportation mode to guarantee consumers to eat healthier and cheaper seafood with more convenient and economical way.

      PubDate: 2018-02-03T03:23:58Z
  • Development and implementation of a fish counter by using an embedded
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): J.M. Hernández-Ontiveros, E. Inzunza-González, E.E. García-Guerrero, O.R. López-Bonilla, S.O. Infante-Prieto, J.R. Cárdenas-Valdez, E. Tlelo-Cuautle
      The development and implementation of an instrument for the automatic counting of ornamental fish by using an embedded system, is introduced herein. The proposed instrument is tested with two marine species, the Guppies (Poecilia Reticulata) and Mollies (Poecilia Sphenops), under conditions of controlled lighting and specimens whose sizes vary from 0.5 to 2.3 cm. The counting is done by digital image processing obtaining an average accuracy up to 96.64% using different species of fishes and different sizes. The main contributions are the theoretical and experimental study to determine the aquarium background color and the algorithm of the proposed method implemented in a low cost and high performance embedded system, specifically in a Raspberry Pi 2 executing the free GNU Octave Scientific Programming Language, thus, allowing the counting instrument to be reliable, portable and easily migratory to different operating systems. The obtained results demonstrate that the proposed method is competitive with state-of-the-art ones.

      PubDate: 2018-02-03T03:23:58Z
  • Simulating peanut (Arachis hypogaea L.) growth and yield with the use of
           the Simple Simulation Model (SSM)
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Seyyed Ali Noorhosseini, Afshin Soltani, Hossein Ajamnoroozi
      The use of crop simulation models for interpreting experiments and analyzing production systems in different management and environmental conditions is common in the literature. In this study, parameterization and evaluation of the Simple Simulation Model (SSM) for the prediction of peanut (Arachis hypogaea L.) growth and yield was conducted for the first time. Data from different field experiments from Astaneh Ashrafieh of northern Iran were used for coefficient estimation and model evaluation for the Virginia-type peanut variety North Carolina 2 (cv. NC2). After estimation of genetic parameters, the model was tested using independent data. The SSM simulated peanut growth and yield with reasonable accuracy, using data of more than 10 field experiments from different environmental conditions (11 experiments in the parameterization stage and 15 experiments in the evaluation stage). Based on data of independent experiments that were not used for parameterization, the model predicted an acceptable percentage of the observed results concerning days to harvest maturity (r = 0.46, CV = 5%), accumulated dry matter (r = 0.66, CV = 15%), grain yield (r = 0.55, CV = 21%), and pod yield (r = 0.45, CV = 18%). Local sensitivity analysis with 23 parameters indicated that two parameters related to leaf development and a parameter related to yield formation were the most sensitive cultivar-specific parameters; thus, estimation of the parameters need to be done with care for new cultivars. The SSM provided an adequate level of peanut growth simulation and based both on its transparency and easiness-to-use can be used as a valid tool for simulating growth of peanut Virginia-type varieties.

      PubDate: 2018-02-03T03:23:58Z
  • Predicting the ripening of papaya fruit with digital imaging and random
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Luiz Fernando Santos Pereira, Sylvio Barbon, Nektarios A. Valous, Douglas Fernandes Barbin
      Papaya grading is performed manually which may lead to misclassifications, resulting in fruit boxes with different maturity stages. The objective is to predict the ripening of the papaya fruit using digital imaging and random forests. A series of physical/chemical analyses are carried out and true maturity stage is derived from pulp firmness measurements. Imaging and image analysis provides hand-crafted color features computed from the peel and random decision forests are implemented to predict ripening stage. More specifically, a total of 114 samples from 57 fruits are used for the experiments, and classified into three stages of maturity. After image acquisition and analysis, twenty-one hand-crafted color features (comprising seven groups) that have low computational cost are extracted and evaluated. Random forests with two datasets (cross-validation and prediction set) are employed for the experiments. Concerning all image features, 94.3% classification performance is obtained over the cross-validation set. The prediction set obtained 94.7% misclassifying only a single sample. For the group comparisons, the normalized mean of the RGB (red, green, blue) color space achieved better performance (78.1%). Essentially, the technique can mature into an industrial application with the right integration framework.

      PubDate: 2018-02-03T03:23:58Z
  • A pattern recognition approach for detecting and classifying jaw movements
           in grazing cattle
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): José O. Chelotti, Sebastián R. Vanrell, Julio R. Galli, Leonardo L. Giovanini, H. Leonardo Rufiner
      Precision livestock farming is a multidisciplinary science that aims to manage individual animals by continuous real-time monitoring their health and welfare. Estimation of forage intake and monitoring the feeding behavior are key activities to evaluate the health and welfare state of animals. Acoustic monitoring is a practical way of performing these tasks, however it is a difficult task because masticatory events (bite, chew and chew-bite) must be detected and classified in real-time from signals acquired in noisy environments. Acoustic-based algorithms have shown promising results, however they were limited by the effects of noises, the simplicity of classification rules, or the computational cost. In this work, a new algorithm called Chew-Bite Intelligent Algorithm (CBIA) is proposed using concepts and tools derived from pattern recognition and machine learning areas. It includes (i) a signal conditioning stage to attenuate the effects of noises and trends, (ii) a pre-processing stage to reduce the overall computational cost, (iii) an improved set of features to characterize jaw-movements, and (iv) a machine learning model to improve the discrimination capabilities of the algorithm. Three signal conditioning techniques and six machine learning models are evaluated. The overall performance is assessed on two independent data sets, using metrics like recognition rate, recall, precision and computational cost. The results demonstrate that CBIA achieves a 90% recognition rate with a marginal increment of computational cost. Compared with state-of-the-art algorithms, CBIA improves the recognition rate by 10%, even in difficult scenarios.

      PubDate: 2018-02-03T03:23:58Z
  • Distributed monitoring system for precision enology of the Tawny Port wine
           aging process
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Raul Morais, Emanuel Peres, J. Boaventura-Cunha, Jorge Mendes, Fernanda Cosme, Fernando M. Nunes
      Aging of Tawny Port wine is a multifactorial process critical for attaining the desired quality. Real time monitoring of important intrinsic and extrinsic factors that are known to affect the time and quality of the aging process are important to optimize and to manage the natural variability between wines aged in different long-used wood barrels. For this study, a distributed monitoring system was installed in sixteen oak barrels, placed in two adjacent wineries – one of them with controlled temperature – in the Douro Demarcated Region, Portugal. The monitoring process was performed using a RS-485 industrial network, which interconnects sensors that continuously measure wine temperature, pH, redox potential and wine’s dissolved oxygen, as well as other sensors that measure parameters related to the barrels’ environmental context, such as room temperature and relative humidity. This work presents the design, development and implementation of a remote distributed system to monitor such parameters, aiming to determine the existence of behaviour models for Port Tawny wine during aging in long-used oak barrels, depending on their storage history and to understand the evolution of wine pH, dissolved oxygen and redox potential in real winery conditions as well as their dependence on the wine’s storage temperature. This approach is based on easy-to-use embedded systems, with the aim of giving a relevant contribution to other projects in the area of precision enology.

      PubDate: 2018-02-03T03:23:58Z
  • Distributed network for measuring climatic parameters in heterogeneous
           environments: Application in a greenhouse
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Javier López-Martínez, José L. Blanco-Claraco, José Pérez-Alonso, Ángel J. Callejón-Ferre
      In Mediterranean countries of Southern Europe, the climatic conditions are usually favourable to cultivate greenhouse vegetables but not always for workers. The aim of this study was to design a network of weather stations capable of gathering data of environmental parameters related to the wellbeing of workers in greenhouses in south-eastern Spain. The unevenness of the thermal environment was studied both vertically as well as horizontally following guideline ISO 7726. The results indicate that the greenhouse should be considered a heterogeneous environment, implying that, for an evaluation of the environmental conditions related to thermal stress of the workers inside the greenhouse, measurements should be taken at different points within the greenhouse at three heights (ankle, abdomen, and head).
      Graphical abstract image

      PubDate: 2018-02-03T03:23:58Z
  • Wine grape cultivar influence on the performance of models that predict
           the lower threshold canopy temperature of a water stress index
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): B.A. King, K.C. Shellie
      The calculation of a thermal based Crop Water Stress Index (CWSI) requires an estimate of canopy temperature under non-water stressed conditions (Tnws ). The objective of this study was to assess the influence of different wine grape cultivars on the performance of models that predict T nws . Stationary infrared sensors were used to measure the canopy temperature of the wine grape cultivars Malbec, Syrah, Chardonnay and Cabernet franc under well-watered conditions over multiple years and modeled as a function of climatic parameters – solar radiation, air temperature, relative humidity and wind speed using multiple linear regression and neural network modeling. Despite differences among cultivars in Tnws , both models provided good prediction results when all cultivars were collectively modeled. For all cultivars, prediction error variance was lower in neural network models developed from cultivar-specific datasets than regression models developed from multi-cultivar datasets. Overall, the cultivar-specific models had less prediction error variance than multi-cultivar models. Multi-cultivar models generally resulted in prediction bias whereas cultivar-specific models eliminated the prediction bias. All predictive models had an uncertainty of ±0.1 in calculation of the CWSI despite significantly different prediction error variance between models.

      PubDate: 2018-02-03T03:23:58Z
  • Open geospatial infrastructure for data management and analytics in
           interdisciplinary research
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Jacob Høxbroe Jeppesen, Emad Ebeid, Rune Hylsberg Jacobsen, Thomas Skjødeberg Toftegaard
      The terms Internet of Things and Big Data are currently subject to much attention, though the specific impact of these terms in our practical lives are difficult to apprehend. Data-driven approaches do lead to new possibilities, and significant improvements within a broad range of domains can be achieved through a cloud-based infrastructure. In the agricultural sector, data-driven precision agriculture shows great potential in facilitating the increase in food production demanded by the increasing world population. However, the adoption rate of precision agriculture technology has been slow, and information and communications technology needed to promote the implementation of precision agriculture is limited by proprietary integrations and non-standardized data formats and connections. In this paper, an open geospatial data infrastructure is presented, based on standards defined by the Open Geospatial Consortium (OGC). The emphasis in the design was on improved interoperability, with the capability of using sensors, performing cloud processing, carrying out regional statistics, and provide seamless connectivity to machine terminals. The infrastructure was implemented through open source software, and was complemented by open data from governmental offices along with ESA satellite imagery. Four use cases are presented, covering analysis of nearly 50 000 crop fields and providing seamless interaction with an emulated machine terminal. They act to showcase both for how the infrastructure enables modularity and interoperability, and for the new possibilities which arise from this new approach to data within the agricultural domain.

      PubDate: 2018-02-03T03:23:58Z
  • Fusion of dielectric spectroscopy and computer vision for quality
           characterization of olive oil during storage
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Alireza Sanaeifar, Abdolabbas Jafari, Mohammad-Taghi Golmakani
      Oxidation level and quality characteristics of olive oil require monitoring during storage to ensure that their amounts are maintained in the lawful thresholds. It is especially important for licensing their commercialization as high-value virgin olive oils. The present research proposes a novel approach based on the fusion of dielectric spectroscopy and computer vision for the characterization of olive oil quality indices during storage in order to reduce the time of analysis, reagent consumption, manpower and high-cost equipment. Colour features in RGB, HSV and L∗a∗b∗ spaces were extracted as well as dielectric features in the frequency range of 40 kHz to 20 MHz for each olive oil sample. After data pre-processing, classification and prediction models were developed and compared. Several machine learning techniques were investigated for storage time classification and quality indices prediction including artificial neural network (ANN), support vector machine (SVM), Bayesian network (BN) and multiple linear regression (MLR). The best result in the classification of olive oils during the storage period was obtained by BN technique with 100% accuracy. Among predictive models, the SVM with RBF kernel had the best results (R = 0.969, 0.988 and 0.976) for prediction of peroxide value (PV), UV absorbance at 232 nm (K232) and chlorophyll, respectively. Also, the SVM with normalized polynomial kernel had the best results (R = 0.989, 0.976, 0.969 and 0.969) for prediction of p-Anisidine value (AV), total oxidation value (TOTOX), UV absorbance at 268 nm (K268) and carotenoid, respectively. The ANN with 40-2-1 topology gave the best result (R = 0.977) for modelling free acidity (FA). Results of this research can be utilized for developing an efficient and reliable system for olive oil quality evaluation and monitoring by industry.

      PubDate: 2018-02-03T03:23:58Z
  • Evaluation of support vector machine and artificial neural networks in
           weed detection using shape features
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Adel Bakhshipour, Abdolabbas Jafari
      Weed detection is still a challenging problem for robotic weed removal. Small tolerance between the cutting tine and main crop position requires highly precise discrimination of the weed against the main crop. Close similarities between the shape features of sugar beet and common weeds make it impossible to define an exclusive feature to be able to efficiently detect all the weeds with acceptable accuracy. Therefore in this study, it was tried to integrate several shape features to establish a pattern for each variety of the plants. To enable the vision system in the detection of the weeds based on their pattern, support vector machine and artificial neural networks were employed. Four species of common weeds in sugar beet fields were studied. Shape feature sets included Fourier descriptors and moment invariant features. Results showed that the overall classification accuracy of ANN was 92.92%, where 92.50% of weeds were correctly classified. Higher accuracies were obtained when the SVM was used as the classifier with an overall accuracy of 95.00% whereas 93.33% of weeds were correctly classified. Also, 93.33% and 96.67% of sugar beet plants were correctly classified by ANN and SVM respectively.

      PubDate: 2018-02-03T03:23:58Z
  • Comparison of voltammetry and digital bridge methods for electrical
           resistance measurements in wood
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Shan Gao, Zhenyu Bao, Lihai Wang, Xiaoquan Yue
      The comparison of accuracy and ease of operation was made between voltammetry and digital bridge method for electrical resistance measurement in Populus davidiana wood specimens and the factors influencing voltammetry were examined. The results showed that current types, waveforms, voltages and frequency had different effects on the resistance values of voltammetry. The measured DC resistance decreased with the increasing voltage. DC resistance presented a turning point at the voltage of 8 V, while AC impedance remained constant over the entire voltage range. The effects of waveform on resistance was minor. No remarkable difference in resistances was found between the two methods above fiber saturated point (FSP) and voltammetry was relatively stable below FSP. The relationship between MC and resistances confirmed the previous findings from other scholars. Compared to the digital bridge, the voltammetry of AC with 1000 Hz sine waves was found to be the superior method for wood resistance measurement.

      PubDate: 2018-02-03T03:23:58Z
  • Ultrasound, microwave and Box-Behnken Design amalgamation offered superior
           yield of gum from Abelmoschus esculentus: Electrical, chemical and
           functional peculiarity
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Meenu Nagpal, Geeta Aggarwal, Manish Jindal, Ashish Baldi, Upendra Kumar Jain, Ramesh Chandra, Jitender Madan
      Background and objective In present investigation, ultrasonic assisted followed by microwave irradiation involving extraction process was developed under the umbrella of Box-Behnken design for gaining superior yield of gum from okra fruit, Abelmoschus esculentus. Methods and results Stationed on single factor layout, Box-Behnken design was employed to calculate the optimized conditions for isolating the okra fruit gum (OFG) using ultrasonic waves and microwave radiations. The extracted gum was further characterized for particle size, zeta-potential, surface morphology, thermal stability, functional groups, and polymorphism. The optimized conditions like water to raw material ratio of 44.98 ml/g, extraction time of 40 min and ultrasonic power of 60 W provided the uppermost extraction yield of 31.52% ± 0.22% that was analogous to the predicted value. The average mean diameter of OFG was measured to be 256.3 ± 18.4 nm in addition to the zeta potential of −9.85 ± 0.12 mV. SEM image of OFG powder revealed irregular, rough surfaced and amorphous structure of OFG powder. The degree of esterification was measured to be 7.8 with high thermal stability, as exposed by DSC. The FT-IR spectrum of OFG displayed a broad peak at 3405.20 cm−1 announcing presence of OH group and hydrophilicity attribute. The spectrum also presented the small peak at 1605.20 cm−1 (CO) owing to the presence of galacturonic acid besides galactose and rhamnose. Conclusion In conclusion, ultrasound and microwave irradiation assisted extraction process under the shed of Box-Behnken design offered superior yield of OFG that may be used as a pharmaceutical excipient for designing medicated or health products.
      Graphical abstract image

      PubDate: 2018-02-03T03:23:58Z
  • A novel compressed sensing based quantity measurement method for grain
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Enes Yigit
      The quantity of grain in silos has commercial and crucial importance. That’s why many researches have been implemented to detect the quantity of the grain. Although the traditional methods can measure the level of the grain from one measurement point, there has not yet been an effective method regarding to 3 dimensional (3D) volume measurement. Available thru-air radar (TAR) based systems can be adapted to 3D perception by increasing the beamwidth of the illumination. But, to achieve pure grain reflections from cluttered noisy signal (containing multi-path (MP) scatterings and mirror scatterings that suppressed the grain reflections) is a challenging problem. In this study, a new wide-beamwidth radar based level measurement method is firstly proposed to determine the amount of grain in silos. Based on the proposed CS-based method, the back-scattering information of the grain surface is obtained accurately. In this way, Cartesian coordinates of the powerful scattering points of grain surfaces, illuminated electromagnetically by three antennas, are identified and 3D height information belongs to the surface are obtained. According to the dominant scattering point’s coordinates and the probable smooth conical stack of the grain, a heuristic volume expression is derived and the volume of the stack grain is estimated with high accuracy. The success of the developed measurement method is confirmed through a real data of a commercial test silo.

      PubDate: 2018-02-03T03:23:58Z
  • Abnormal shapes of production function: Model interpretations
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): A. Topaj, W. Mirschel
      An abnormal non-monotonic shape of production function (response of obtained yield to increasing rates of mineral nitrogen fertilizers) has been observed in experimental field trials. Often, the observed effect (an inflection point, or intermediate plateau or even local undershoot of the “yield-fertilization” curve) is treated as a test distortion and will be ignored or sorted out. This article presents the authors’ efforts to interpret and to explain similar phenomenon by means of investigating two mechanistic crop simulation models – AGROSIM and AGROTOOL. It is demonstrated that an imitation model can be used as a valuable tool of scientific research, allowing for the hypothesising of alternative understandings of non-trivial natural phenomena.

      PubDate: 2018-02-03T03:23:58Z
  • Uncertainty of weight measuring systems applied to weighing lysimeters
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Alisson M. Amaral, Fernando R. Cabral Filho, Lucas M. Vellame, Marconi B. Teixeira, Frederico A.L. Soares, Leonardo N.S. dos Santos
      The determination of measurement reliability in weighing lysimeters via error analysis is essential for scientific research and irrigation management. The objective of this study was to evaluate four different weight measuring systems (MSs) applied to load cell weighing lysimeters and compare the results with the expected uncertainty values obtained from data provided by manufacturers. A weighing lysimeter with an area of 0.385 m2 and a volume of 0.289 m3 was used, installed on three load cells. In MS1, the load cells were connected to a junction box and the box to a weighing indicator module in a six-wire configuration. In MS2, a four-wire connection was used between the junction box and a datalogger, whereas in MS3, there was a six-wire connection. For MS4, the connection between the load cells and datalogger was direct. The uncertainties of the measurement systems were determined from the calibration results. MS1 presented the lowest measurement errors and uncertainties, resulting in performance superior to those of the other MSs. After MS1, the best performances were obtained by MS2 and MS3, and MS4 presented the worst performance. The effect of the signal measurement uncertainties and the excitation by the datalogger had the greatest effects on the overall uncertainty of the system compared with the influence of temperature on the load cells. The measurement system may be selected according to the technical data supplied by the manufacturer; however, periodic calibration of the effective measuring range is necessary to verify and compensate for systematic errors, which are accentuated during the operation time.

      PubDate: 2018-02-03T03:23:58Z
  • Mapping forests using an unmanned ground vehicle with 3D LiDAR and
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Marek Pierzchała, Philippe Giguère, Rasmus Astrup
      Enabling automated 3D mapping in forests is an important component of the future development of forest technology, and has been garnering interest in the scientific community, as can be seen from the many recent publications. Accordingly, the authors of the present paper propose the use of a Simultaneous Localisation and Mapping algorithm, called graph-SLAM, to generate local maps of forests. In their study, the 3D data required for the mapping process were collected using a custom-made, mobile platform equipped with a number of sensors, including Velodyne VLP-16 LiDAR, a stereo camera, an IMU, and a GPS. The 3D map was generated solely from laser scans, first by relying on laser odometry and then by improving it with robust graph optimisation after loop closures, which is the core of the graph-SLAM algorithm. The resulting map, in the form of a 3D point cloud, was then evaluated in terms of its accuracy and precision. Specifically, the accuracy of the fitted diameter at breast height (DBH) and the relative distance between the trees were evaluated. The results show that the DBH estimates using the Pratt circle fit method could enable a mean estimation error of approximately 2 cm (7–12%) and an RMSE of 2.38 cm (9%), whereas for tree positioning accuracy, the mean error was 0.0476 m. The authors conclude that robust SLAM algorithms can support the development of forestry by providing cost-effective and acceptable quality methods for forest mapping. Moreover, such maps open up the possibility for precision localisation for forestry vehicles.

      PubDate: 2018-02-03T03:23:58Z
  • Determination of egg storage time at room temperature using a low-cost NIR
           spectrometer and machine learning techniques
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Julian Coronel-Reyes, Ivan Ramirez-Morales, Enrique Fernandez-Blanco, Daniel Rivero, Alejandro Pazos
      Currently, consumers are more concerned about freshness and quality of food. Poultry egg storage time is a freshness and quality indicator in industrial and consumer applications, even though egg marking is not always required outside the European Union. Other authors have already published works using expensive laboratory equipment in order to determine the storage time and freshness of eggs. This paper presents a novel alternative method based on low-cost devices for the rapid and non-destructive prediction of egg storage time at room temperature (23 ± 1 °C). H&N brown flock with 49-week-old hens were used as a source for the sampled eggs. Samples were scanned for a period of 22 days beginning from the time the egg was laid. The spectral acquisition was performed using a low-cost near-infrared reflectance (NIR) spectrometer which has a wavelength range between 740 nm and 1070 nm. The resulting dataset of 660 samples was randomly split according to a 10-fold cross-validation in order to be used in a contrast and optimization process of two machine learning algorithms. During the optimization, several models were tested to develop a robust calibration model. The best model used a Savitzky Golay pre-processing technique with a third derivative order and an artificial neural network with ten neurons in one hidden layer. Regressing the storage time of the eggs, tests achieved a coefficient of determination (R-squared) of 0.8319 ± 0.0377 and a root mean squared error in cross-validation test set (RMSECV) of 1.97 days. Although further work is needed, this technique shows industrial potential and consumer utility to determine an egg's freshness using a low-cost spectrometer connected to a smartphone.

      PubDate: 2017-12-27T09:55:02Z
  • Development of an electro-mechanic control system for seed-metering unit
           of single seed corn planters Part II: Field performance
    • Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Anil Cay, Habib Kocabiyik, Sahin May
      Using single seed planters is important for a uniform distribution of plant growing area. Seed metering units of planters receive their motion from the drive wheel pass through various transmission members such as the chains, gears, shafts and belts. While the planter is being operated, the transmission system of the machine and drive system of the seed metering units naturally push the driving wheel. Because of this effect, the wheel experiences a loss of mobility or some sort of slipping. Consequently, all seed metering units are being affected due to the common mobility transmission system and changes in the desired plant spacing occur. In order to overcome these negativities, an electro-mechanic drive system (EMDS) alternative to classic driving system (CDS) was developed. Detailed information regarding the system design and laboratory simulation results of EMDS were provided in Part I of this study. In this part, it was aimed to investigate the effect of EMDS on the planting quality (plant spacing uniformity, variation among rows) and operational parameters (fuel consumption and negative slippage) in the field and compared with the CDS. While the quality of feed index (Iqf) 90.63%, multiple index (Imult) 0.94%, missing index (Imiss) 8.44% and precision index (Ip) 17.63% were obtained in trials performed by the EMDS, Iqf 88.13%, Imult 2.50%, Imiss 9.38% and Ip 17.81% were found in trials performed by the CDS. Plant spacing uniformity in the EMDS was found as “good” while it was “moderate” in the CDS, according to related criteria. Plant distribution uniformity in the EMDS were better than the CDS. Furthermore, the experimental plant spacing values obtained by the EMDS were closer to the theoretical (set) value than the values obtained by the CDS. The negative slipping in the planter’s drive wheel was found as 1.33% at trials with the EMDS while it was 6.79% with the CDS. When the EMDS used in the field operations, it provided approximately 22% fuel saving compared with the CDS. The results promise that the developed system can be used as an alternative to the CDS for single seed planters. However, in order to provide a complete mechanical rapport between the EMDS and the planter, future studies, various structural improvements in the seed metering unit designs and optimization of the seed plate thickness, number of holes and connection methods may be required.

      PubDate: 2017-12-27T09:55:02Z
  • The gamma shape mixture model and influence of sample-unit size on
           estimation of tree diameter distributions: Forest modelling
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Rafał Podlaski, Dariusz Wojdan, Monika Żelezik
      The gamma shape mixture (GSM) model is based on a mixture of gamma distributions. A general Bayesian approach for estimating the unknown parameters of the GSM model was employed. Tree diameter at breast height (DBH) sets of complex and stratified forests are ideal data sources for testing the usefulness of theoretical distributions applied to modelling and simulating strongly differentiated data sets. The GSM model was useful in fitting these DBH structures. During the identification of homogenous forest patches of the similar DBH structures one should select sample plots of at least 0.15 ha in area, with a minimum of 30 trees. The use of GSM model has the potential to facilitate the presentation of not only DBH structures, but also other empirical distributions, describing e.g. various biological stages and processes. The GSM model can be employed especially for modelling multimodal, asymmetrical and heavy-tailed survey data.

      PubDate: 2017-12-27T09:55:02Z
  • Virtual cold chain method to model the postharvest temperature history and
           quality evolution of fresh fruit – A case study for citrus fruit packed
           in a single carton
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Wentao Wu, Paul Cronjé, Bart Nicolai, Pieter Verboven, Umezuruike Linus Opara, Thijs Defraeye
      Fruit quality loss is dependent on the temperature control throughout the postharvest cold chain. Previous research mainly focused on optimizing the cooling performance of single unit operations. However, assessing fruit temperature history throughout the entire cold chain is crucial to determine the end quality. This study proposes a virtual cold chain (VCC) method to predict the temperature history and quality loss of packaged fresh fruit, down to each individual fruit, throughout the entire cold chain. The VCC method is based on computational fluid dynamics and kinetic quality modelling. Results show that the difference in quality loss among individual fruit in a carton could reach 11% for a specific cold chain. The maximum difference in the remaining quality at the end of the cold chain between different cold chain scenarios is 23%. The VCC method has a potential to track temperature history and to estimate quality loss of individual fruit in the cargo throughout a cold chain.

      PubDate: 2017-12-27T09:55:02Z
  • Optimization of vacuum cooling treatment of postharvest broccoli using
           response surface methodology combined with genetic algorithm technique
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): José Carlos C. Santana, Sidnei A. Araújo, Wonder A.L. Alves, Peterson A. Belan, Ling Jiangang, Chen Jianchu, Liu Dong-Hong
      In this paper, the effects of vacuum cooling factors on the weight loss of postharvest broccoli were initially investigated. In sequence, the vacuum cooling treatment conditions were optimized using the response surface methodology (RSM) combined with the genetic algorithm (GA) technique. Fresh broccoli samples were harvested from a Chinese farm, and the green heads of selected samples were cut into smaller pieces, with diameters approximately 3–4 cm, and sequentially equilibrated to room temperature. Pressure (200–600 Pa), broccoli weight (200–500 g), water volume (2–6%, v/v) and time (20–40 min) were used as factors, and weight loss and end temperature were recorded as responses. The GA was employed to find the optimal condition for processing broccoli, and its initial solution was obtained from the RSM. The results demonstrate a good performance of the GA for the optimization of the broccoli cooling process. The best conditions of vacuum cooling process were as follows: a weight between 273.5 g and 278.0 g, a water volume of 3.0% v/v, a processing time of 40 min, a pressure of 200 Pa, and a weight loss and end temperature of 0.34 ± 0.01% and 2.0 ± 0.0 °C, respectively, leading to a percentage of profit of 99.66 ± 0.01%.

      PubDate: 2017-12-27T09:55:02Z
  • An algorithm for selecting groups of factors for prioritization of land
           consolidation in rural areas
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Przemysław Leń
      Large defects in the spatial structure of agricultural land are an issue in many countries. The main cause of these defects are hundreds of years of social, economic and historical transformations. The spatial structure of farmland can be improved using procedures such as land consolidation and exchange. However, given the fact that the funds for these activities are limited, it is necessary to select those areas in which land consolidation and exchange are a priority. To develop a prioritization plan for land consolidation and exchange interventions, it is necessary to carry out a series of studies and analyses which will provide a set of factors characterizing the region under investigation. Many years of research have shown that different regions in Poland are characterized by different parameters. Therefore, in the present study, an attempt was made to develop a universal algorithm for selecting groups of factors for purposes of prioritization of land consolidation which takes into account the geographic location of an investigated area. To test the proposed algorithm, analyses were carried out in two communes located in central and south-eastern Poland which differed significantly in terms of the spatial structure of farm holdings. A first test object was the commune of Paradyż, occupying an area of 8125.73 ha and divided into 15,941 cadastral plots. A second test object was the commune of Frysztak, located in south-eastern Poland, with an area of 9066.86 ha, divided into 21,506 cadastral plots.

      PubDate: 2017-12-27T09:55:02Z
  • A taste sensor device for unmasking admixing of rancid or winey-vinegary
           olive oil to extra virgin olive oil
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Ussama Harzalli, Nuno Rodrigues, Ana C.A. Veloso, Luís G. Dias, José A. Pereira, Souheib Oueslati, António M. Peres
      Electrochemical sensor devices have gathered great attention in food analysis namely for olive oil evaluation. The adulteration of extra-virgin olive oil with lower-grade olive oil is a common worldwide fraudulent practice, which detection is a challenging task. The potentiometric fingerprints recorded by lipid polymeric sensor membranes of an electronic tongue, together with linear discriminant analysis and simulated annealing meta-heuristic algorithm, enabled the detection of extra-virgin olive oil adulterated with olive oil for which an intense sensory defect could be perceived, specifically rancid or winey-vinegary negative sensations. The homemade designed taste device allowed the identification of admixing of extra-virgin olive oil with more than 2.5% or 5% of rancid or winey-vinegary olive oil, respectively. Predictive mean sensitivities of 84 ± 4% or 92 ± 4% and specificities of 79 ± 6% or 93 ± 3% were obtained for rancid or winey-vinegary adulterations, respectively, regarding an internal-validation procedure based on a repeated K-fold cross-validation variant (4 folds × 10 repeats, ensuring that the dataset was forty times randomly split into 4 folds, leaving 25% of the data for validation purposes). This performance was satisfactory since, according to the legal physicochemical and sensory analysis, the intentionally adulterated olive oil with percentages of 2.5–10%, could still be commercialized as virgin olive oil. It could also be concluded that at a 5% significance level, the trained panelists could not distinguish extra-virgin olive oil samples from those adulterated with 2.5% of rancid olive oil or up to 5% of winey-vinegary olive oil. Thus, the electronic tongue proposed in this study can be foreseen as a practical and powerful tool to detect this kind of worldwide common fraudulent practice of high quality olive oil.

      PubDate: 2017-12-27T09:55:02Z
  • Generalizability of gene expression programming and random forest
           methodologies in estimating cropland and grassland leaf area index
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Sepideh Karimi, Ali Ashraf Sadraddini, Amir Hossein Nazemi, Tongren Xu, Ahmad Fakheri Fard
      Leaf Area Index (LAI) is a very important structural attribute of ecosystems which affects the energy, water and carbon exchanges between the land surface and atmosphere. Direct measurement of LAI is costly and time consuming so indirect measurement approaches have been developed for determining its magnitude. The present paper aimed at modeling LAI in cropland and grassland sites using the available meteorological data through two heuristic data driven techniques, namely, gene expression programming (GEP) and random forest (RF). Different data set organizations were designed using local (temporal) and external (spatial) norms to provide a thoroughgoing data scanning strategy. The results showed that the external GEP and RF models (EGEP and ERF) might be suitable approaches for modeling LAI by average scatter index (SI) values of 0.275 and 0.270 (for cropland) and 0.273 and 0.279 (for grassland) when compared to the local GEP and RF models with average SI values of 0.207 and 0.204 (cropland), and 0.249 and 0.204 (grassland), respectively. The presented methodology allowed the evaluation in each site of models developed (trained) using local patterns and the models developed using the exogenous data (patterns from ancillary sites).

      PubDate: 2017-12-27T09:55:02Z
  • Wood species recognition through multidimensional texture analysis
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Panagiotis Barmpoutis, Kosmas Dimitropoulos, Ioannis Barboutis, Nikos Grammalidis, Panagiotis Lefakis
      Wood recognition is a crucial task for wood sciences and industries, since it leads to the identification of the anatomical features and physical properties of wood. Traditionally, the recognition process relies almost exclusively on human experts, who are based on various characteristics of wood, such as color, structure and texture. However, there are numerous types of wood species in the nature that are difficult to be identified even by experienced scientists. Towards this end, in this paper we propose a novel approach for automated wood species recognition through multidimensional texture analysis. By taking advantage of the fact that static wood images contain periodic spatially-evolving characteristics, we introduce a new spatial descriptor considering each wood image as a collection of multidimensional signals. More specifically, the proposed methodology enables the representation of wood images as concatenated histograms of higher order linear dynamical systems produced by vertical and horizontal image patches. The final classification of images, i.e., histogram representations, into wood species, is performed using a Support Vector Machines (SVM) classifier. For the evaluation of the proposed method, a dataset, namely “WOOD-AUTH”, consisting of more than 4200 wood images (from cross, radial and tangential sections of normal wood structure) of twelve common wood species existing in Greek territory, was created. Experimental results presented in this paper show the great potential of the proposed methodology, which, despite a small number of misclassification cases with regards to both anatomically similar and different species, outperforms a number of state of the art approaches, yielding a classification rate of 91.47% in wood cross sections.

      PubDate: 2017-12-27T09:55:02Z
  • Numerical simulation of spreading performance and distribution pattern of
           centrifugal variable-rate fertilizer applicator based on DEM software
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Shi Yinyan, Chen Man, Wang Xiaochan, Morice Oluoch Odhiambo, Ding Weimin
      Farmers in China have been concerned with the efficiency and utilization rate of fertilizers, because of the rigorous implementation of China’s “dual-reduction” plan, which calls for reducing fertilizer and pesticide usage. This study was aimed to improve the spreading performance and fertilizer distribution uniformity of an independently developed centrifugal variable-rate fertilizer applicator. The spreading performance was evaluated by conducting discrete-element-simulation tests, and the relationship between the variations in the fertilizer particle distribution and the working parameters of the fertilizer spreader was analyzed. The quality of the particles was evaluated using a two-dimensional matrix, and the coefficient of variation of the transverse distribution of the fertilizer particles was determined. The results show that the shape of the distribution varies irregularly with the increase in the vane pitch angle, and the coefficient of variation decreases with the increase in the spreader disc height. Further, when the application flow rate is increased gradually, the coefficient of variation decreases rapidly first but gradually thereafter. In addition, with an increase in the rotational speed of the disc, the distribution gradually changes from a triangular shape to a W shape and ultimately to an M shape. The average coefficient of variation was the lowest (14.39%) for a single-row application flow rate of 300 g/s, a vane pitch angle of 15°, a spreader disc height of 95 cm, and a rotational speed of 600 r/min, with a good spreading uniformity. Field validation tests show that the average coefficient of variation with respect to the effective spreading swath width of the applicator was 16.74%. The relative error was 10.66% with respect to the simulation results, thus validating the simulation model and confirming its accuracy. The results show that the coefficient of variation for the developed variable-rate spreader is reduced, exhibiting a high spreading performance. The results serve as a theoretical basis for farmers for altering their traditional empirical fertilization techniques and should aid design and optimization of centrifugal variable-rate fertilizer applicators.

      PubDate: 2017-12-27T09:55:02Z
  • Assessment of important soil properties related to Chinese Soil Taxonomy
           based on vis–NIR reflectance spectroscopy
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Dongyun Xu, Wanzhu Ma, Songchao Chen, Qingsong Jiang, Kang He, Zhou Shi
      As a rapid, inexpensive and accurate analysis technique, vis–NIR spectra has shown great advantages for determining a wide variety of soil properties, such as soil organic matter content, mineral composition, water content, particle size and color. Thus, this technique is becoming increasingly popular in soil science. We aim to assess the applicability of using vis–NIR spectra to estimate eighteen different soil properties that are important for Chinese Soil Taxonomy (CST). In this study, vis–NIR reflectance spectra were measured under laboratory conditions. First, partial least-squares regression (PLSR) was used to predict the following soil properties related to soil classification: coarse crumb, sand, silt, and clay contents, bulk density (BD), pH (H2O), pH (KCl), soil organic matter (SOM), total nitrogen (TN), total potassium (TK), and total phosphorus (TP) contents, cation exchange capacity (CEC), free iron (Fe2O3), soluble salts (salt), available phosphorus (AP), exchangeable aluminum (ExAl), aluminum saturation (AS) and base saturation (BS). Then, the important bands for modeling these soil properties were selected based on the selectivity ratio (SR). Finally, the spectral chromophores of the soil and the correlations of soil properties were analyzed. The results showed that (1) the prediction accuracy based on the PLSR algorithm was good for pH, SOM, TN, Fe2O3, salt, AS and BS (RPD > 2.0, R 2 between 0.70 and 0.90). For sand, silt, clay, BD, TP, TK, CEC, AP and ExAl, the PLSR model could achieve acceptable estimates (1.4 < RPD < 2.0, R 2 between 0.56 and 0.72), while for coarse crumb, the PLSR model was unable to make reliable predictions (RPD < 1.4, R 2 below 0.50). (2) As chromophore properties, SOM, TN, Fe2O3, clay and salt are and can be predicted by spectroscopy. Besides, BD, pH, TK, TP, CEC, AP, ExAl, AS and BS have significant correlations with chromophore properties and can also be predicted by vis–NIR spectroscopy. Therefore, except for coarse crumb, the soil properties important to CST can be quantitatively predicted by PLSR based on vis–NIR reflectance spectroscopy. This study is significant to CST, and it provides a fast and efficient method for soil classification.

      PubDate: 2017-12-12T19:30:24Z
  • Spatial analysis of the accuracy of the cadastral parcel boundaries
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Paweł Hanus, Agnieszka Pęska-Siwik, Robert Szewczyk
      Contemporary advancements in technology and the widespread availability of GIS (Geographic Information System) tools offer many possibilities in performing computer-assisted geospatial data analysis. For this type of analysis, the quality of the data is an extremely important factor. In the case of analysis related to parcel boundaries, the quality of the data is mainly determined by the mean errors of the location of the breakpoints of the boundary lines for particular parcels and the origin of the information about the boundary point coordinates. This paper presents new opportunities for analyzing the boundary point position errors and their impact on the course of the boundary lines based on these points. The above-mentioned analysis of boundary errors was conducted in the area of the village of Chocznia. The results of the analysis are presented graphically and analytically to illustrate the distribution of these errors. Clear visualization of the distribution of the errors can be a valuable source of information. The information obtained in such a manner can be used in management processes and for planning agricultural land management work. A rapid assessment of parcel boundary data accuracy with GIS tools could also be used for the selection and classification of areas requiring cadastre modernization or for verification of the data contained within it.

      PubDate: 2017-12-12T19:30:24Z
  • Droplet impingement behavior analysis on the leaf surface of Shu-ChaZao
           under different pesticide formulations
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Lin Zhu, Jia-Ru Ge, Yin-Yin Qi, Qi Chen, Ri-Mao Hua, Feng Luo, Pei-Rong Chen
      Spray deposition in agriculture is of particular importance to apply pesticides to plant because poor efficiency leads to reduced biological efficacy, environmental contamination and even economic losses. Spray deposition on leave surfaces is associated with impingement dynamics behaviors of pesticide droplets. But how this impact affects the foregoing deposition is an intriguing subject of research, notably for the deposition on tea leave-surfaces. In this study a tea leaf-surface of a real tea tree, i.e. Shu-Chazao was selected as an impingement target of the pesticide droplet. A Couple Level Set & Volume of Fluids (CLSVOF) interface tracking method was proposed to characterize the impingement dynamics behaviors of the three commonly used pesticide droplets (such as chlorothalonil, dimethoate and malathion) on the leaf-surface and thus assess the effects of the different pesticide formulations on the spray deposition. Four key factors, including liquid phase pattern, surface wettability, pressure and velocity distributions were investigated, respectively, along the transverse and longitudinal directions of the leaf-surface. The calculated predictions provide a reasonable match with the published data. With our study, the CLSVOF interface trace modeling is demonstrated to have great potential for in-depth study of the impingement dynamics behaviors of the pesticide droplets on the tea leaf-surface. The simulation results can contribute to spray efficiency improvement of the tea plants in China.

      PubDate: 2017-12-12T19:30:24Z
  • Automated early yield prediction in vineyards from on-the-go image
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Arturo Aquino, Borja Millan, Maria-Paz Diago, Javier Tardaguila
      Early grapevine yield assessment provides information to viticulturists to help taking management decisions to achieve the desired grape quality and yield amount. In previous works, image analysis has been explored to this effect, but with systems performing either manually, on a single variety or close to harvest-time, when there are few rectifiable agronomic aspects. This study presents a solution based on image analysis for the non-invasive and in-field yield prediction in vines of several varieties, at phenological stages previous to veraison, around 100 days from harvest. To this end, an all-terrain vehicle (ATV) was modified with equipment to autonomously capture images of 30 vine segments of five different varieties at night-time. The images were analysed with a new image analysis algorithm based on mathematical morphology and pixel classification, which yielded overall average Recall and Precision values of 0.8764 and 0.9582, respectively. Finally, a model was calibrated to produce yield predictions from the number of detected berries in images with a Root-Mean-Square-Error per vine of 0.16 kg. This accuracy makes the proposed methodology ideal for early yield prediction as a very helpful tool for the grape and wine industry.

      PubDate: 2017-12-12T19:30:24Z
  • Predictive model based on artificial neural network for assessing beef
           cattle thermal stress using weather and physiological variables
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Rafael Vieira de Sousa, Alex Vinicius da Silva Rodrigues, Mariana Gomes de Abreu, Rubens Andre Tabile, Luciane Silva Martello
      The performance of feedlot cattle is adversely affected by thermal stress but the approach to assess the status of animal stress can be laborious, invasive, and/or stressful. To overcome these constraints, the present study proposes a model based on an artificial neural network (neural model), for individual assessment of the level of thermal stress in feedlot finishing cattle considering both weather and animal factors. An experiment was performed using two different groups of Nellore cattle. Physiological and weather data were collected during both experiments including surface temperatures for four selected spots, using infrared thermography (IRT). The data were analyzed (in terms of Pearson’s correlation) to determine the best correlation between the weather and physiological measurements and the IRT measurements for defining the best body location and physiological variable to support the neural model. The neural model had a feed-forward and multi-layered architecture, was trained by supervised learning, and accepted IRT, dry bulb temperature, and wet bulb temperature as inputs to estimate the rectal temperature (RT). A regression model was built for comparison, and the predicted and measured RTs were classified on levels of thermal stress for comparing with the classification based on the traditional temperature–humidity index (THI). The results suggested that the neural model has a good predictive ability, with an R2 of 0.72, while the regression model yielded R2 of 0.57. The thermal stress predicted by the neural model was strongly correlated with the measured RT (94.35%), and this performance was much better than that of the THI method. In addition, the neural model demonstrated good performance on previously unseen data (ability to generalize), and allowed the individual assessment of the animal thermal stress conditions during the same period of day.

      PubDate: 2017-12-12T19:30:24Z
  • Multi-vehicle refill scheduling with queueing
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Giovanni D’Urso, Stephen L. Smith, Ramgopal Mettu, Timo Oksanen, Robert Fitch
      We consider the problem of refill scheduling for a team of vehicles or robots that must contend for access to a single physical location for refilling. The objective is to minimise time spent in travelling to/from the refill station, and also time lost to queuing (waiting for access). In this paper, we present principled results for this problem in the context of agricultural operations. We first establish that the problem is NP-hard and prove that the maximum number of vehicles that can usefully work together is bounded. We then focus on the design of practical algorithms and present two solutions. The first is an exact algorithm based on dynamic programming that is suitable for small problem instances. The second is an approximate anytime algorithm based on the branch and bound approach that is suitable for large problem instances with many robots. We present simulated results of our algorithms for three classes of agricultural work that cover a range of operations: spot spraying, broadcast spraying and slurry application. We show that the algorithm is reasonably robust to inaccurate prediction of resource utilisation rate, which is difficult to estimate in cases such as spot application of herbicide for weed control, and validate its performance in simulation using realistic scenarios with up to 30 robots.

      PubDate: 2017-12-12T19:30:24Z
  • Multi-step spray modelling of a flat fan atomizer
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): A. Kashani, H. Parizi, K.H. Mertins
      The present study combines Eulerian CFD modeling of the liquid flow inside an atomizer, instability analysis of liquid sheet, statistical spray analysis, and Lagrangian modeling for spray transport of a flat fan atomizer widely used in agricultural spraying. High-speed spray imaging and spray size measurements carried out for validation of numerical models and the experimental results agreed reasonably with the modeling results. Although the subject of the study is a flat fan atomizer, the methodology presented could be employed to other types of spray nozzles that rely on liquid sheet disintegration mechanism. The systematic modeling of spray systems with the methodology explained in the paper not only reduces the design and prototyping time and costs but also gives engineers a better understanding of the design parameters for improved spray system performance.

      PubDate: 2017-12-12T19:30:24Z
  • Development of an electro-mechanic control system for seed-metering unit
           of single seed corn planters Part I: Design and laboratory simulation
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Anil Cay, Habib Kocabiyik, Sahin May
      The performance of precision planters is very important for attaining uniform seed spacing. While a planter is on work, undesired situations such as spinning and slipping on ground wheel, vibration, seizing and jamming on the chain-sprocket systems may occur during the transfer of the motion from the ground wheel to the seed-metering unit especially at high operating speeds. In order to overcome these problems, it was aimed to develop an electro-mechanic drive system (EMDS) for seed metering units of a classic single seed planter. The performances of the EMDS and the classic drive system (CDS) were tested at three different operating speeds (vf) (5, 7.5, 10 km/h) and ten different seed spacing (zt) from 6 to 29.3 cm at laboratory. Both systems were compared regarding to the seed spacing uniformity. When the EMDS was used, the quality of feed index (Iqf), multiple index (Imult), miss index (Imiss) and precision index (Ip) were ranged as such: 2.91–95.36%, 0–1.73%, 4.45–97.09% and 8.79–22.14%, respectively. At the test of the CDS, the performance indices varied as such: Iqf 2.09–98.55%, Imult 0–0.36% and Imiss 1.09–97.91%, Ip 5.79–20.92%. Seed spacing uniformities were found as “good” and “moderate” for both systems. Average seed spacing values obtained from the EMDS were found to be closer to the theoretical seed spacing values compared with that obtained from the CDS. EMDS enabled the suggested optimum seeding rate, a quick and simple setting possibility, synchronize and real-time control, the ability to work under higher speeds, individual movement and control for each metering unit. However, EMDS should be tested to determine the success of the system in practice. Therefore, the field performance of EMDS with respect to plant spacing uniformity and operational parameters (variation among rows, fuel consumption and negative slippage) were examined in the following part of this study (Part II: Field Performance).

      PubDate: 2017-12-12T19:30:24Z
  • A comparison of support vector machines, artificial neural network and
           classification tree for identifying soil texture classes in southwest
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv
      The variability of soil properties plays a critical role in soil and water conversation engineering. In this study, different machine learning techniques were applied to identify the soil texture classes based on a set of terrain parameters in a small mountainous watershed located in the core areas of Three Gorges of Yangtze River, southwest China. For this, the support vector machines (SVMs) with polynomial and Gaussian radius basis functions, artificial neural network, and classification tree methods were compared. The most commonly used performance measures including overall accuracy, kappa index, receiver operating characteristics (ROC), and area under the ROC curve (AUC) were employed to evaluate the accuracy of the models for classification. The observed results showed a better performance under SVMs than under artificial neural network and classification tree algorithms. Moreover, SVM with polynomial function (SVM-poly) worked slightly better than SVM with Gaussian radius basis function. The overall accuracy, kappa statistic, and AUC of SVM-poly were 0.943, 0.79, and 0.944, respectively. Meanwhile, the classification accuracy was 0.794 for clay, 0.992 for loam, and 0.661 for sand under SVM-poly. Elevation, terrain classification index for lowlands, and flow path length were the most important terrain indicators affecting the variation in the soil texture class in the study area. These results showed that the support vector machines are feasible and reliable in the identification of soil texture classes.

      PubDate: 2017-12-12T19:30:24Z
  • Simulation-based modeling of wild blueberry pollination
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Hongchun Qu, Frank Drummond
      The high variability of wild (lowbush) blueberry plants in spatial and genetic structure, in combination with bee foraging behavior varying between species, and the complexity of these factors interacting over time and space, are major obstacles to understanding of pollination dynamics subject to environmental change. The bottom-up modeling paradigm provides an ideal approach to bridging the gap between known mechanisms of individual organisms and unknown spatial–temporal dynamics of pollination at the field scale. By linking empirical data to stochastically-based ecological process modeling, we present a spatially-explicit agent-based simulation model that enables exploration of how various factors, including plant spatial arrangements, outcrossing and self-pollination, bee species compositions and weather conditions, in isolation and combination, affect pollination efficiency throughout a blueberry bloom season. The firmly validated open-source model is a useful tool for hypothesis testing and theory development for wild blueberry pollination researches. Sensitivity analysis suggested that fruit set and resulting measures of productivity such as fruit mass and viable seeds per fruit were sensitive to parameterization of blueberry genotype or clone size and the amount of blueberry plant cover in a field. Fruit set due to pollen compatibility was sensitive to ovule number per flower and foraging bee density. Simulation experiments allowed us to compare bee pollination efficiencies at the bee taxon population level (honey bees, bumble bees, and native solitary bees), the effect of foraging distance from bee nest or colony site on fruit set, and test whether the mechanism of gametophytic self-incompatibility (pre- vs. post-zygotic decision making by the plant) in wild blueberry pollination at the field level matters in estimating yield.

      PubDate: 2017-12-12T19:30:24Z
  • Development of an early warning algorithm to detect sick broilers
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Xiaolin Zhuang, Minna Bi, Jilei Guo, Siyu Wu, Tiemin Zhang
      The frequent occurrence of poultry diseases, such as bird flu, not only causes huge economic losses to farmers but also seriously threatens the health of human beings. Providing early warnings of new poultry disease outbreaks is essential in poultry breeding. With the rise of digital image processing technology and machine learning algorithms, real-time monitoring of poultry health status through cameras is an effective way to prevent large-scale outbreaks of disease. To analyze the postures of healthy and sick broilers, bird flu virus was inoculated intranasally into healthy broilers manually. The broilers were then placed in isolator cages for comparative experiments. The methods of observing the posture changes of broilers and extracting the key features are used to realize the automatic classification of healthy and sick broilers. In this research, broiler images are obtained, and two kinds of segmentation algorithms are proposed to separate the broilers from the background to obtain the outlines and skeleton information of the broilers. According to the preset feature extraction algorithm, the posture features of healthy and sick chickens are extracted, the eigenvectors are established, the postures of the broilers are analyzed by machine learning algorithms, and the diseased broilers are predicted. A series of experiments have been done. Data for each feature acquired by the algorithms are analyzed, and the effect of each feature on the recognition accuracy is obtained. Using some of the features proposed in this research, accuracy rates of 84.248%, 60.531% and 91.504% are obtained, but using all the features can yield an accuracy rate of 99.469%. Then, the recognition effects of several commonly used machine learning algorithms are compared. The Support Vector Machine (SVM) model obtains an accuracy rate of 99.469% on the test samples, which is superior to those of the other machine learning algorithms. The experimental results show that the algorithms proposed in this research can effectively separate broilers from the background, extract the posture information of broilers, and accurately and quickly identify the health status of broilers by means of SVM. The algorithms for digital image processing and machine learning are evaluated in the diagnosis of broiler health status and show high accuracy, good stability and good generalization performance, and can give early warning signals. This research can provide a reference for the intelligent identification of broiler health status in the future.

      PubDate: 2017-12-12T19:30:24Z
  • Assessing the potential of data-driven models for estimation of long-term
           monthly temperatures
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Saeid Mehdizadeh
      Having information on air temperature components consisting minimum (Tmin), maximum (Tmax) and mean (T) temperatures plays a crucial role in various aspects of agriculture such as agricultural meteorology, soil science, agronomy, etc. The present study explores the performance of four data-driven models including artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and multivariate adaptive regression splines (MARS) for estimation of long-term monthly Tmin, Tmax and T. For this purpose, the long-term monthly temperatures of 50 stations all over Iran were used. The data of 35 and 15 stations were utilized to train and test the models, respectively. To feed the models, the geographical information (latitude, longitude, altitude) and periodicity component (the number of months) were employed as input parameters. The obtained results demonstrated that the long-term monthly temperatures of the studied regions can be estimated as a function of geographical information and periodicity component. Comparing the overall performance of the models at training stage revealed that the ANN outperformed the other models for estimating the long-term monthly Tmin, Tmax and T. That's while the SVM, ANN and ANFIS had superiority over the others at testing stage for estimation of the long-term monthly Tmin, Tmax and T, respectively. Furthermore, the MARS model presented the weakest performance for estimating the long-term monthly temperatures at both training and testing stages.

      PubDate: 2017-12-12T19:30:24Z
  • AgroPortal: A vocabulary and ontology repository for agronomy
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Clément Jonquet, Anne Toulet, Elizabeth Arnaud, Sophie Aubin, Esther Dzalé Yeumo, Vincent Emonet, John Graybeal, Marie-Angélique Laporte, Mark A. Musen, Valeria Pesce, Pierre Larmande
      Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain’s semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform’s content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain.

      PubDate: 2017-12-12T19:30:24Z
  • Monitoring trough visits of growing-finishing pigs with UHF-RFID
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Felix Adrion, Anita Kapun, Florian Eckert, Eva-Maria Holland, Max Staiger, Sven Götz, Eva Gallmann
      Automatic monitoring of animal feeding behaviour in commercial farms is desirable as it is an important indicator for the well-being and health of animals. Low-frequency and high-frequency radio frequency identification (RFID) systems have been tested for the detection of feeding visits of growing-finishing pigs, but the suitability of ultra-high frequency (UHF) RFID for this application has not yet been shown. Therefore, the objective of this study was the validation of a UHF-RFID system, consisting of a reader, antennas and passive transponder ear tags, for the monitoring of visits of growing-finishing pigs at a short trough for liquid feeding. Consequently, (1) two antenna variants (free-form and patch antennas) were tested at different levels of antenna output power, (2) two methods to determine a bout criterion for the creation of trough visits from the RFID registrations were compared, and (3) a comparison of the RFID data with reference data from video observation was carried out. The analysis showed that the reading area exceeded the trough especially in the variants with high output power. Thus, the evaluation of the first and second rate of change of the total daily number and total daily duration of visits led to higher values for the bout criterion (50 s for both antenna variants) than the evaluation of the mean absolute deviation between video and RFID data (20 s for the free-form antenna and 30 s for the patch antennas). The trough visits observed were detected best with the patch antennas at 25 dBm output power and a bout criterion of 30 s with regard to average precision (61.1%), and correlation between the video data and RFID visits (R2 = 0.87 for total number and 0.80 for total duration of visits). The average sensitivity of this variant was 49.7%, specificity 99.0% and accuracy 97.9%. The highest average sensitivity (79.7%) and a good correlation between video data and RFID visits (R2 = 0.78 for total number, 0.56 for total duration of visits with a bout criterion of 50 s) were measured with the free-form antenna at 26 dBm. In conclusion, UHF-RFID can be suitable for the monitoring of trough visits of growing-finishing pigs, but the effect of ear tissue on the performance of the UHF-RFID ear tags should be reduced by further development. In addition, further research should be carried out to evaluate the potential of this technology completely for animal behaviour monitoring.

      PubDate: 2017-12-12T19:30:24Z
  • Using ground-based spectral reflectance sensors and photography to
           estimate shoot N concentration and dry matter of potato
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Zhenjiang Zhou, Mohamed Jabloun, Finn Plauborg, Mathias Neumann Andersen
      Two years experiments were set up to evaluate the performance of different vegetation indices (VI) to estimate shoot N concentration (Nc) and shoot dry matter (DM) for a potato crop grown under different nitrogen (N) treatments. Possibilities to improve the performance of VI using normalization by leaf area index (LAI) or camera-derived ground cover fraction (GC) were also investigated. Results indicated that Nc was significantly correlated to RRE (Near-infrared divided by red edge reflectance) and RRE/GC with a coefficient of determination (R2) of 0.62 and 0.78, respectively, indicating that inclusion of auxiliary parameter GC together with RRE substantially improved the correlation as compared to using only RRE. However, no significant correlation between Nc and RVI (Ratio Vegetation Index, near-infrared divided by red reflectance) or NDVI (Normalized Difference Vegetation Index) was found. However, DM was highly correlated to RVI and NDVI. Moreover, DM showed significant relationship (R2 = 0.86) with GC, highlighting its versatile usefulness in estimating agronomic variables DM and Nc, which are the core variables to assess N status of crops for a better N application.

      PubDate: 2017-12-12T19:30:24Z
  • Seed drill depth control system for precision seeding
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Søren Kirkegaard Nielsen, Lars Juhl Munkholm, Mathieu Lamandé, Michael Nørremark, Gareth T.C. Edwards, Ole Green
      An adequate and uniform seeding depth is crucial for the homogeneous development of a crop, as it affects time of emergence and germination rate. The considerable depth variations observed during seeding operations - even for modern seed drills - are mainly caused by variability in soil resistance acting on the drill coulters, which generates unwanted vibrations and, consequently, a non-uniform seed placement. Therefore, a proof-of-concept dynamic coulter depth control system for a low-cost seed drill was developed and studied in a field experiment. The performance of the active control system was evaluated for the working speeds of 4, 8 and 12 km h−1, by testing uniformity and accuracy of the coulter depth in relation to the target depth of −30 mm. The evaluation was based on coulter depth measurements, obtained by coulter position sensors combined with ultrasonic soil surface sensors. Mean coulter depth offsets of 3.5, 5.3 and 6.3 mm to the target were registered for the depth control system, compared to 8.0, 9.1 and 11.0 mm without the control system for 4, 8 and 12 km h−1, respectively. However, speed did not affect the coulter depth significantly. The control system optimised coulter depth accuracy by 15.2% and at 95% confidence interval it corresponded to an absolute reduction in the coulter depth confidence span of 10.4 mm. The spatial variability, due to variation in soil mechanical properties was found to be ±8 mm, across the blocks for the standard drill and when activating the coulter depth control system this variability was reduced to ±2 mm. The system with the active control system operated more accurately at an operational speed of 12 km h−1 than at 4 km h−1 without the activated control system.

      PubDate: 2017-12-12T19:30:24Z
  • CFD study of the influence of laying hen geometry, distribution and weight
           on airflow resistance
    • Abstract: Publication date: January 2018
      Source:Computers and Electronics in Agriculture, Volume 144
      Author(s): Qiongyi Cheng, Wentao Wu, Hao Li, Guoqiang Zhang, Baoming Li
      Proper indoor environment is essential to the production performance of laying hens, one effective approach to research it was Computational Fluid Dynamics (CFD). However, the modelling is a great issue as modelling individual hen would generate large number of mesh, an alternative is to simplify caged laying hen occupied zone (CZ) into porous media zone. As for it, the flow resistance of CZ requires firstly to calculate the resistance coefficients. In this study, CFD simulation was applied to calculate the resistance in three directions perpendicular to each other. The effect of the hen model geometry (full-geometry, ellipsoidal and body only model in which hen’s head, neck and legs were neglected), spatial distribution (four, three and two hens stand near the feeding through, respectively) and body weight (1.5 kg, 1.8 kg and 2.0 kg) were investigated on flow resistance. Finally, the resistance coefficients were obtained under different situations. The numerical model was firstly validated against wind tunnel experiment with five spheres representing hens. Different turbulence models were evaluated and the RNG k-ε model showed the superior performance than others. Hen model geometry, the spatial distribution and body weight of hens showed significant effect on flow resistance of CZ, the resistance increased with hens’ body weight while its variation decreased. The resistance coefficients determined in this study can be directly applied to other related simulation studies using porous media to represent CZ.

      PubDate: 2017-12-12T19:30:24Z
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