Computers and Electronics in Agriculture (COMPUT ELECTRON AGR)

Publisher: Elsevier

Journal 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.

Current impact factor: 1.76

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 1.761
2013 Impact Factor 1.486
2012 Impact Factor 1.766
2011 Impact Factor 1.846
2010 Impact Factor 1.431
2009 Impact Factor 1.312
2008 Impact Factor 1.273
2007 Impact Factor 1.242
2006 Impact Factor 0.851
2005 Impact Factor 0.802
2004 Impact Factor 0.863
2003 Impact Factor 0.686
2002 Impact Factor 0.556
2001 Impact Factor 0.626
2000 Impact Factor 0.379
1999 Impact Factor 0.358
1998 Impact Factor 0.347
1997 Impact Factor 0.466

Impact factor over time

Impact factor

Additional details

5-year impact 2.09
Cited half-life 6.00
Immediacy index 0.31
Eigenfactor 0.01
Article influence 0.52
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


  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Authors pre-print on any website, including arXiv and RePEC
    • Author's post-print on author's personal website immediately
    • Author's post-print on open access repository after an embargo period of between 12 months and 48 months
    • 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
    • Author's post-print may be used to update arXiv and RepEC
    • Publisher's version/PDF cannot be used
    • Must link to publisher version with DOI
    • Author's post-print must be released with a Creative Commons Attribution Non-Commercial No Derivatives License
    • Publisher last reviewed on 03/06/2015
  • Classification

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: A fast and fully automated software for image analysis (named IMAFISH_ML) was developed to measure 27 fish morphometric traits (technological traits) on three commercially relevant fish species: gilthead seabream (Sparus aurata L., from 12.5 to 36.6 cm length), meagre (Argyrosomus regius, from 17.5 to 58.4 cm length) and red porgy (Pagrus pagrus, from 16.3 to 29 cm length). This analysis was performed by using two images of each fish from different angles (lateral and dorsal). The computer vision algorithm was programmed in MATLAB® v.7.5 and is freely available to aquaculture industry and research, and it is possible to modify or combine traits in order to obtain new ones, according to specific interests and competence. Additionally, an appropriate, easy-to-perform and reproducible protocol to take photographs was also described. In order to validate the software, 500 fish of each species were laterally and dorsally photographed, and the images were processed by using the IMAFISH_ML. Each fish was manually processed to measure its fork length, body weight and fillet weight (phenotypic traits). Correlation coefficients between each fish technological and phenotypic traits were calculated, all of them were statistically significant (P<0.01). Fork length measured by technological and phenotypic methods showed correlation coefficients between 0.98 and 0.99. The average photograph processing time was 10 seconds and 9.7 seconds for lateral and dorsal images, respectively. IMAFISH_ML software provides fish farmers and researchers with an efficient, fast and automatic tool to objectively asses morphological and growth traits. It is a practical and economical way to evaluate products for industrial purposes. Moreover, it is an especially useful tool to be included within genetic breeding programs, as it provides a high number of fast, easy-to-perform and non-invasive traits measurements, which additionally can be correlated to other production traits.
    Computers and Electronics in Agriculture 01/2016; In press.

  • Computers and Electronics in Agriculture 01/2016; 120:1-6. DOI:10.1016/j.compag.2015.11.004
  • [Show abstract] [Hide abstract]
    ABSTRACT: In the agriculture sector, the efficient management of fertilizers is reflected into a saving of money and time. Many software developments are found in the scientific literature and in the market that optimize the performance and use of fertilizers for specific crops. Most of them do not take into account the current price of fertilizers; and others show a high computational cost, which means high time to solve scenarios with medium-high number of fertilizers to select and high power consumption, being not suitable for mobile devices. In this work Ecofert is presented as a simple and powerful software application developed for Android O.S. that calculates the best combination of fertilizers to obtain the desired nutrient solution for different crops, taking into account the current price of fertilizers in the market. The main novelties of Ecofert is, on the one hand, that it solves the fertilization selection by modelling this as a Linear Programming problem, and using specific mathematical libraries to solve it. On the other hand, Ecofert works with a list of commercial fertilizers hosted in a Data Base in the Cloud, where the composition and price (in Euro) is updated daily. Moreover Ecofert shows a low computational cost, even for large number of fertilizers (>20). Its simplicity permits Ecofert to be executed in mobile devices, giving farmers and agriculture technicians a powerful tool to support agricultural tasks in situ.
    Computers and Electronics in Agriculture 01/2016;

  • Computers and Electronics in Agriculture 01/2016; 120:10-16. DOI:10.1016/j.compag.2015.11.001

  • Computers and Electronics in Agriculture 01/2016; 120:7-9. DOI:10.1016/j.compag.2015.11.005
  • Yi Lin ·
    [Show abstract] [Hide abstract]
    ABSTRACT: Plant phenomics, the link between plant genomics and environment, recently is explosively highlighted. As its basis, a large variety of phenotyping approaches have been developed, but meanwhile, the related technical demands have gone ahead into the levels of high-throughput, field and comprehensive phenotyping. This reality-lagging-behind situation suggests that it is time to envisage the next-generation techniques of plant phenotyping. From the perspective of noninvasive measurement of phenotypic traits, the state-of-the-art remote sensing technology of light detection and ranging (LiDAR) shows the potential. In fact, researchers are calling for more incorporations of LiDAR into phenotyping facilities. At the same time, it has also been realized that the currently-available LiDAR forms cannot effectively support the development of the next-generation techniques of plant phenotyping. In order to bridge this technical gap, the theoretically more-powerful LiDAR variant forms now in research and development, such as high-density, full-waveform and hyperspectral LiDAR, were previewed. Their supposed advanced capacities mean a higher possibility of pushing forward plant phenotyping into a new stage. Overall, with LiDAR determined as a key technical constituent, this study pointed out a novel way for developing the next-generation plant phenotyping techniques, which will be helpful for biologists and agronomists to investigate plant phenomes.
    Computers and Electronics in Agriculture 11/2015; 119:61-73. DOI:10.1016/j.compag.2015.10.011
  • [Show abstract] [Hide abstract]
    ABSTRACT: Different cotton foreign matter causes various levels of damage to textile products and decreases the monetary value of cotton. Hyperspectral imaging technique has shown the capability of discriminating the foreign matter, but its large amount of information which is mostly correlated and redundant limits the classification accuracy and processing speed. The goal of this study was to explore a new method of feature selection (minimum Redundancy Maximum Relevance algorithm) to select optimal wavelengths from the visible to near infrared spectra of the hyperspectral imaging data for cotton foreign matter classification. A spectral dataset containing 480 samples was collected from hyperspectral reflectance images of cotton lint and 15 types of foreign matter. Each sample was represented by a mean spectrum containing 256 wavelengths ranging from 400. nm to 1000. nm. The dataset was pre-processed by removing the noise, and the number of wavelengths was reduced from 256 to 223 by removing those with a signal to noise ratio lower than 10. dB. The optimal wavelengths were selected from the pre-processed dataset by a two-stage approach. The first step was to rank the features using the minimum Redundancy Maximum Relevance algorithm and to provide only the top ranked features for the following feature selection. In the second step, the sequential backward elimination was applied to the top ranked wavelengths to select the optimal wavelengths for foreign matter classification. The generality of the selected wavelengths was evaluated by comparing the classification performance using the Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs). A total of 12 wavelengths were selected as the optimal feature set for foreign matter classification. Eight wavelengths from the visible range were related to the natural or artificial pigments of foreign matter, and the other four from the near-infrared range were related to the proteins or nutrients in foreign matter. The selected wavelengths achieved average classification rates of 91.25%, 86.67%, and 86.67% for the LDA, SVM, and ANNs, respectively, indicating the generality of the selected features. This study explored a new method for hyperspectral imaging optimal wavelength selection and the selected wavelengths can be used with different classifiers for cotton foreign matter classification.
    Computers and Electronics in Agriculture 11/2015; 119:191-200. DOI:10.1016/j.compag.2015.10.017
  • [Show abstract] [Hide abstract]
    ABSTRACT: In China, investors have contracts with smallholder farmers to plant organic vegetable crops. The objective of the smallholder farmers is to maximize profits per unit of farm area, and minimize the differences in profits between farmers. Farmers' profits are a function of the crop rotation scheduling and the achieved prices. Here we propose an operational model that considers a crop rotation scheduling for an investor that offers contracts to many smallholder farmers. A heuristic algorithm was designed to identify the optimal rotation scheduling that would achieve both objectives of maximizing prices and minimizing the profit differences between smallholder farmers. Real data from a Chinese company was used to parameterize the model. Model results indicate that significant improvements in profits and farmers equality could be obtained if an optimal crop rotation scheduling would be used.
    Computers and Electronics in Agriculture 11/2015; 119:12-18. DOI:10.1016/j.compag.2015.10.002
  • [Show abstract] [Hide abstract]
    ABSTRACT: Classification of insect species of field crops such as corn, soybeans, wheat, and canola is more difficult than the generic object classification because of high appearance similarity among insect species. To improve the classification accuracy, we develop an insect recognition system using advanced multiple-task sparse representation and multiple-kernel learning (MKL) techniques. As different features of insect images contribute differently to the classification of insect species, the multiple-task sparse representation technique can combine multiple features of insect species to enhance the recognition performance. Instead of using hand-crafted descriptors, our idea of sparse-coding histograms is adopted to represent insect images so that raw features (e.g., color, shape, and texture) can be well quantified. Furthermore, the MKL method is proposed to fuse multiple features effectively. The proposed learning model can be optimized efficiently by jointly optimizing the kernel weights. Experimental results on 24 common pest species of field crops show that our proposed method performs well on the classification of insect species, and outperforms the state-of-the-art methods of the generic insect categorization.
    Computers and Electronics in Agriculture 11/2015; 119:123-132. DOI:10.1016/j.compag.2015.10.015
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes and experimentally demonstrates a fault-tolerant optical-penetration-based silkworm gender identification. The key idea lies in the exploitation of the inherent dual wavelength of white and red light illumination. In particular, the image of the posterior area of the silkworm pupa created under white light is not only transformed into an optical region-of-interest but also is used for pinpointing the female silkworm pupa, thus speeding up the identification time twice. For the male and unidentified female silkworm pupae, their images are later on analyzed under red light illumination, implying fault-tolerant operation of the system. Other important features include low cost, ease of implementation, and simplicity in terms of process control. Experimental demonstration shows a highly accurate 92.5% in identifying female silkworm pupae with a faster average system speed of 26.38. ms under white light illumination. Under red light illumination, the remaining male and unidentified female silkworm pupae are clarified with an improved accuracy of 98.9% and the total average analytical time of 53.50 ms.
    Computers and Electronics in Agriculture 11/2015; 119:201-208. DOI:10.1016/j.compag.2015.10.004