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: 1.49

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 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 2.00
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


  • 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
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Whey is a by-product of cheese making that is a potentially important source of nutrients, but which currently goes to disposal in many parts of the world. In this paper, we analyse the efficiency of investment in whey-processing with the aim of releasing the productive potential of currently unexploited whey supply chains. We introduce a decision support model for production and distribution of products derived from whey that extends a globally inclusive facility location problem. The basic tenet of the model is that equipment selection during the initial stages of facility planning is critical, as capital costs in the early stages of supply chain design go into purchases of new machines and site conditioning. The model selects the optimal combination of whey processing equipment, facility locations and transportation routes subject to budget, equipment availability and final product requirements. The results from the model inform the members of a cluster of cheese makers on the infrastructure investments that better release the productive potential of the supply chain of their valuable by-product and, at the same time, avoid environmental damage. We use the model to find the optimal configuration of a whey supply chain for an actual cluster of small cheese makers in Minas Gerais, Brazil, and demonstrate that important savings can be achieved by investing early on in adequate processing facilities.
    Computers and Electronics in Agriculture 09/2015; 627501(29). DOI:10.1016/j.compag.2015.07.016
  • [Show abstract] [Hide abstract]
    ABSTRACT: An orchard precision management system plays an important role in improvement at the management level and the enhancement of decision abilities. A single orchard tree or an orchard tree microcommunity is the basic management unit, and bidirectional information on the environment and plants is the important content for precision management. A type of RFID label was applied with a UHF chip in the core and a QR code in the surface for single tree identification. A bidirectional acquisition system for orchard production, which included farming information collection for the forward direction and environmental information acquisition for the backward direction, was designed with smart phones. In the farming information collection part, information collection flow that included QR code image acquisition, image preprocessing, barcode decoding and farming information collection was established. An improved local threshold method was adopted to improve the QR code identification rate in the smart phone platform. In the environment information acquisition part, a sensor search rule on the single tree position and a multi-point environment value model were designed. The orchard information bidirectional acquisition system was developed on an Android platform with the Java language, which has the function of QR decoding, farm record information collection, environment information acquisition, data uploading and statistical analysis. The system was tested in an apple orchard. A total of 144 trees were chosen to decode the QR codes in the tree label. The success rate was approximately 96.52%. The identification time of 85% of the trees was less than 4 s for the 20 chosen trees. In taking the temperature, for example, the difference between the computed temperature value and the measured temperature value around each tree was small. The system could decrease the cost of the professional equipment, such as portable RFID readers and writers, which was a low-cost and high-efficiency solution for orchard production information collection.
    Computers and Electronics in Agriculture 08/2015; 116. DOI:10.1016/j.compag.2015.06.003
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    ABSTRACT: Visible/near-infrared spectroscopy is a well-established method to measure optical properties of tissues, assuming that a light propagation model can be used to recover absorption and reduced scattering coefficients from non-invasive probing. Spectroscopic measurements have achieved success in non-destructive assessment of apple optical properties and quality attributes. However, the spectroscopy of apples must consider the size of the fruit and the presence of the thin skin layer that surrounds the flesh, to correctly read the signals acquired on the boundary. In this research, the fruit was modelled as a two layer spherical structure with various radii and finite thickness of the upper skin layer. Monte Carlo computations were performed to generate time-resolved reflectance and spatially-resolved reflectance measurements. Simulated data were then fitted using a procedure based on Levenberg–Marquardt algorithm with specific semi-infinite models. The errors in the retrieved optical properties of the flesh (absorption coefficient μa, and reduced scattering coefficient μ′s) were studied as functions of apple radius, skin thickness, and source–detector distance, for given optical parameter sets assigned to the flesh and the skin. The results suggest that the time-resolved reflectance spectroscopy may probe optical properties of the flesh regardless of the skin layer, when a sufficient source–detector distance (15 mm) is used for the measurements. Similar results were found in case of using the spatially resolved spectroscopy, because measurements extend up to 15–29 mm by steps of 1 mm or 2 mm. The computations also show that the curvature of the boundary has noticeable effect on the errors in the retrieved optical coefficients of the flesh. However, results from time-resolved spectroscopy are more influenced by the size of apples, compared with the spatially-resolved spectroscopy.
    Computers and Electronics in Agriculture 08/2015; 116. DOI:10.1016/j.compag.2015.06.009
  • [Show abstract] [Hide abstract]
    ABSTRACT: The dew point temperature is a significant element particularly required in various hydrological, climatological and agronomical related researches. This study proposes an extreme learning machine (ELM)-based model for prediction of daily dew point temperature. As case studies, daily averaged measured weather data collected for two Iranian stations of Bandar Abass and Tabass, which enjoy different climate conditions, were used. The merit of the ELM model is evaluated against support vector machine (SVM) and artificial neural network (ANN) techniques. The findings from this research work demonstrate that the proposed ELM model enjoys much greater prediction capability than the SVM and ANN models so that it is capable of predicting daily dew point temperature with very favourable accuracy. For Tabass station, the mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R) achieved for the ELM model are 0.3240 °C, 0.5662 °C and 0.9933, respectively, while for the SVM model the values are 0.7561°C, 1.0086°C and 0.9784, respectively and for the ANN model are 1.0324°C, 1.2589°C and 0.9663, respectively. For Bandar Abass station, the MABE, RMSE and R for the ELM model are 0.5203 °C, 0.6709 °C and 0.9877, respectively whereas for the SVM model the values are 1.0413 °C, 1.2105 °C and 0.9733, and for the ANN model are 1.3205 °C, 1.5530 °C and 0.9617, respectively. The study results convincingly advocate that ELM can be employed as an efficient method to predict daily dew point temperature with much higher precision than the SVM and ANN techniques.
    Computers and Electronics in Agriculture 08/2015; 117:214–225. DOI:10.1016/j.compag.2015.08.008
  • [Show abstract] [Hide abstract]
    ABSTRACT: Seed number quantification is an essential agronomic parameter conducted mostly manually or by mechanical counters, both with obvious limitations. Digital image analysis provides a reliable and robust alternative to accurately calculate many biological features. This study presents and evaluates the performance of four open-source image-analysis programs i.e. ImageJ, CellProfiler, P-TRAP and SmartGrain to count crop seeds from digital images captured by camera and scanner. It also evaluates ImageJ program for automated seed counting using macro containing RenyiEntropy threshold algorithm. Digital images of cereal crop seeds were acquired i.e. wheat, barley, maize, rye, oat, sorghum, triticale and rice. All images contained 200 seeds per image present in an area of approx. 1400 cm2. RenyiEntropy threshold increased the seed count accuracy of ImageJ from digital camera images. Generally, seed counts from digital camera images of all crops were accurate, but software–crop combination had significant (p < 0.05) difference from reference value. Among image analysis programs, ImageJ produced mostly higher seed count across all observed crops than other programs. Mean seed counts from scanned images of maize were observed only by CellProfiler and P-TRAP, with other programs inappropriate due to high inaccuracy. These results suggest CellProfiler as a reliable image analysis program for seed counting from digital images. Benchmark test was also performed to compare speed of analysis. The automated seed count produced by image analysis programs described here allows faster, reliable and reproducible analysis, compared to standard manual method. To our knowledge this is the first study on using CellProfiler program for crop seed counting from digital images.
    Computers and Electronics in Agriculture 08/2015; 117:194-199. DOI:10.1016/j.compag.2015.08.010
  • Computers and Electronics in Agriculture 08/2015; 117:154-167. DOI:10.1016/j.compag.2015.06.019
  • [Show abstract] [Hide abstract]
    ABSTRACT: Typical sowing depth for cereal crops is in range of 20–50 mm, depending on the soil type and crop. The variation in the sowing depth causes variation in germination and the seeds placed too deep are not sprouting. To compensate the spatial variation in soil type and conditions, an automatic depth control for a seed drill was developed. The seed drill used in this study was equipped with the wedge-roller type single-disc coulters that help in the working depth regulation but an electronic system is necessary on top of that. The developed electronic control system was compatible with ISO 11783 communication standard. The working depth was measured by using multiple sensors. The control system utilises ISO 11783 remote control messages to command the auxiliary valves of the tractor over ISO 11783 on the implement side. The system was tested on the field, at first to validate the measurement system and later to test the ability of the control system to adjust the working depth. The control system was able to maintain the desired working depth within tolerance ±10 mm at driving speed 10 km/h. The true samples of sowing depth were compared with working depth estimate in the same spot and it was found that the sowing depth was 1.7 mm shallower compared with the working depth on average.
    Computers and Electronics in Agriculture 08/2015; 116:30-35. DOI:10.1016/j.compag.2015.05.016