Computers and Electronics in Agriculture (COMPUT ELECTRON AGR )

Publisher: Elsevier

Description

Computers and Electronics in Agriculture provides international coverage of advances in the application of computer hardware, software and electronic instrumentation and control systems to agriculture, forestry and related industries. The latter include horticulture (in both its food and amenity aspects), forest products, aquaculture, animal/livestock science, veterinary medicine and food processing.The journal publishes original papers, reviews, applications notes and book reviews on topics including computerized decision-support aids (e.g., expert systems and simulation models) pertaining to any aspect of the aforementioned industries; electronic monitoring or control of any aspect of livestock/crop production (e.g. soil and water, environment, growth, health, waste products) and post-harvest operations (such as drying, storage, production assessment, trimming and dissection of plant and animal material). Relevant areas of technology include artificial intelligence, sensors, machine vision, robotics and simulation modelling.

  • Impact factor
    1.77
    Show impact factor history
     
    Impact factor
  • 5-year impact
    2.00
  • Cited half-life
    5.60
  • Immediacy index
    0.22
  • Eigenfactor
    0.01
  • Article influence
    0.50
  • Website
    Computers and Electronics in Agriculture website
  • Other titles
    Computers and electronics in agriculture (Online)
  • ISSN
    0168-1699
  • OCLC
    38840899
  • Material type
    Document, Periodical, Internet resource
  • Document type
    Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Elsevier

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Pre-print allowed on any website or open access repository
    • Voluntary deposit by author of authors post-print allowed on authors' personal website, arXiv.org or institutions open scholarly website including Institutional Repository, without embargo, where there is not a policy or mandate
    • Deposit due to Funding Body, Institutional and Governmental policy or mandate only allowed where separate agreement between repository and the publisher exists.
    • Permitted deposit due to Funding Body, Institutional and Governmental policy or mandate, may be required to comply with embargo periods of 12 months to 48 months .
    • Set statement to accompany deposit
    • Published source must be acknowledged
    • Must link to journal home page or articles' DOI
    • Publisher's version/PDF cannot be used
    • Articles in some journals can be made Open Access on payment of additional charge
    • NIH Authors articles will be submitted to PubMed Central after 12 months
    • Publisher last contacted on 18/10/2013
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: To reduce the amount of wasted reactive nitrogen (N) reaching the environment and to achieve high N fertilizer use efficiency, a site-specific N management strategy using GreenSeeker™ optical sensor (GS) was evaluated in dry direct-seeded rice (DDSR) in the north-western India. Four field experiments were conducted during 2011–2013 to develop an optical sensor algorithm for fine tuning in-season N fertilizer applications. It was demonstrated that panicle initiation of rice is the appropriate stage for applying GS guided N fertilizer dose. Application of a prescriptive dose of 60 kg N ha�1 in two or 90 kg N ha�1 in two or three equal split doses, followed by a corrective N dose guided by GS at panicle initiation stage resulted in rice yield levels comparable to that obtained by following general recommendation, but with lower total N fertilizer application. On an average, N use efficiency was improved by more than 12% when N fertilizer management was guided by GS as compared to when general N fertilizer recommendation was followed. The results prove the inadequacy of general recommendations for N fertilizer management in DDSR and possibility of increasing N use efficiency along with high rice yield levels through site specific N fertilizer management using GS.
    Computers and Electronics in Agriculture 11/2015; 110(January):114-120.
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    ABSTRACT: Novel yield estimation methodology based on photogrammetry and computer vision.•Automatic 3D metric reconstruction of bunches in field conditions.•3D bunch modelling comparison through point cloud and CAD models.•Determination of vineyards productivity by non-invasive and low cost sensors.
    Computers and Electronics in Agriculture 01/2015; 110.
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    ABSTRACT: We present a method to predict the carcass weight from weight trajectories.•We learn herd trajectory instead of having one regression function for each animal.•We use Artificial Intelligence tools: Support Vector Machines for Classification.•Our method outperforms the regression of each animal especially with few weights available.•It enables animals with even one weight to have an acceptable estimation.
    Computers and Electronics in Agriculture 01/2015; 110.
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    ABSTRACT: The traditional Growing Neural Gas (GNG) was used for clustering hen houses by considering distance only.•To improve the solutions, we developed the hybrid GNG by considering both distance and weights of hen house sizes.•Routes determination to allocate hens to the hen houses was also carried out in order to minimize the total distance.•The maximum allowable difference of ages of hens in the same hen house is considered as occurred in real practices.•We proposed the multi-time period clustering based on chick ordering and hen house capacities.
    Computers and Electronics in Agriculture 01/2015; 110.
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    ABSTRACT: The subject of this study was to investigate the possibility of using artificial neural networks as a tool for classification, designed to identify apple orchard pests. The paper presents a classification neural model using optimized learning sets acquired on the basis of the information encoded in the form of digital images of selected pests. This study predominantly deals with the problem of the identification of 6 selected apple pests which are most commonly found in Polish orchards. Neural modeling techniques, including digital image analysis, were used to classify the pests. The qualitative analysis of neural models produced, indicates that multi-layered perceptron (MLP) neural network topology achieve the best classification ability. Representative features, allowing for effective pest identification are 23 visual parameters in the form of 7 selected coefficients of shape and 16 color characteristic of pests. The dominant input variables of a neural model, determining the correct identification of the features, contain information about the color of pests. Our results support the hypothesis that artificial neural networks are an effective tool that supports the process of identification of pests in apple orchards. The resulting neural classifier has been created to assist in the decision-making processes that take place during the production of apples, in the context of protection against pests.
    Computers and Electronics in Agriculture 01/2015; 110:9–16.
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    ABSTRACT: Data from electronic cattle monitoring collars were classified into 5 activities.•Collars contained GPS and accelerometers.•Mixed distributions were used to obtain threshold values for a decision tree.•The 5 activities were classified with 85.5% and 90.5% precision in 2 trials.•Grazing and ruminating were classified most accurately.
    Computers and Electronics in Agriculture 01/2015; 110.
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    ABSTRACT: A method based on contour fragments was developed to detect fruits in tree canopy.•This method can detect citrus fruits even with highlights and shades on surface.•The integrated contour of fruits can be recovered from their fragments.•The partial order relations between overlapped fruits were derived.•The fitting error and time performance were discussed.
    Computers and Electronics in Agriculture 01/2015; 110.
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    ABSTRACT: Visual discrimination between barley varieties is difficult, and it requires training and experience. The development of automatic methods based on computer vision could have positive implications for the food processing industry. In the brewing industry, varietal uniformity is crucial for the production of high quality malt. The varietal purity of thousands of tons of grain has to be inspected upon purchase in the malt house. This paper evaluates the effectiveness of identification of barley varieties based on image-derived shape, color and texture attributes of individual kernels. Varieties can be determined by means of discriminant analysis, including reduction of feature space dimensionality, linear classifier ensembles and artificial neural networks, with high balanced accuracy ranging from 67% to 86%. The study demonstrated that classification results can be significantly improved by standardizing individual kernel images in terms of their anteroposterior and dorsoventral orientation and performing additional analyses of wrinkled regions.
    Computers and Electronics in Agriculture 01/2015; 110:1–8.
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    ABSTRACT: The management zone were delineated by using ECa, terrain elevation and soil depth.•The response to N fertilization of wheat is different among management zone.•The delimiting of management zone improved the N use efficiency (NUE) in wheat.•The application of variable N rates increases production system sustainability.
    Computers and Electronics in Agriculture 01/2015; 110.
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    ABSTRACT: A new cluster-based method for several lines detection is proposed.•The method is based on searching for an approximate globally optimal partition.•A new efficient algorithm for crop rows detection is proposed.
    Computers and Electronics in Agriculture 11/2014; 109.
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    ABSTRACT: We proposed the hybrid model of mobile phone and WSNs for smart poultry farm.•We proposed the use of cloud services as database and computational offloading.•We optimized the transmission logics to monitor/control the environment behavior.•We proposed the methodology for image filer to classify the poultry population.
    Computers and Electronics in Agriculture 11/2014; 109.
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    ABSTRACT: This study uses the methods of computer image analysis and neural modelling for the construction of classification models to identify the stage of early maturity in composted material based on sewage sludge and maize straw. The research material was produced in strictly controlled laboratory conditions with a six-chamber bioreactor. Samples of the material were subjected to image acquisition in visible light (VIS), ultraviolet light from the UV-A range and mixed light (MIX, VIS + UV-A). The acquired images were subjected to broad analysis. As a result the values of 46 parameters providing information about the colour and texture were obtained. The colour was analysed for the RGB, HSV and greyscale model. The texture analysis determined the grey level co-occurrence matrixes (GLCM). The parameters acquired from the image were the basis of train, validation and test sets which were used for the construction of neural classification models. The models were based on the MLP (Multilayer Perceptron) topology. The process of their construction went on in the iterative manner, where the potentially insignificant input parameters were eliminated by means of sensitivity analysis. Finally 21 such models were generated. The classification error for the best model in the MIX light was 1.56%. On the other hand, the models with the best accuracy in the UV-A and VIS light showed the error, which was 1.83% and 2.87% greater than the best model for the MIX light, respectivel
    Computers and Electronics in Agriculture 11/2014; 109:301-310.
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    ABSTRACT: A novel methodology to match NASS cropland data with actual field size distributions.•Automated GIS procedures to process cropland data from county to national scale.•Final reconciled data matches actual crop-specific field size distributions.•The resulting cropland dataset is more suitable for spatial simulation and analysis.•The methodology has broad applicability to other digitized land cover data.
    Computers and Electronics in Agriculture 11/2014; 109.
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    ABSTRACT: A porous media approach of dispersion under slatted floor was developed.•The porous media approach can well predict the velocities compared with direct geometry approach and measurements.•The porous media approach cannot well predict the mass transport process.•The orientation of slat to stream direction affects the dispersion under slatted floor.
    Computers and Electronics in Agriculture 11/2014; 109.