The objective of the paper was to carry out a bibliometric quantitative analysis of publications concerning the application of artificial neural networks in the research area - agriculture and a bibliometric quantitative analysis and subject analysis with regard to agricultural engineering. A number of scientific publications devoted to the ANN found in the data base of the Web of Science - in documents published to 2015 was a basis for the quantitative analysis. Research on the use of artificial neural networks in the research area - agriculture is extending systematically. Moreover, a rapidly growing number of citations prove a continuous increase in the scientists’ interest in possibilities of the ANN applications. The quantitative analysis of scientific publications in 5 selected scientific journals and thematically related to agricultural engineering (indexed in the Web of Science) allowed a statement that 236 scientific articles from 1996- 2015 were related to the ANN application. The biggest number of publications was reported in Computers and Electronics in Agriculture - 118 articles. In 2011-2015 there was a growing trend in dynamics of publishing of scientific papers devoted to the ANN application to agricultural engineering. Thus, we may assume that the research related to application of the artificial neural networks to agricultural engineering will be continued and their scope and number will be still growing. The thematic analysis of the most often quoted publications from 2011-2015 in the journal Computers and Electronics in Agriculture, proved that they concern both the issues related to the classification problem as well as to modelling processes and systems. We should suppose that the subjects related to modelling of drying processes and application of neural networks for image analysis will grow dynamically in the following years.
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Aghbashlo M. Mobli H. Rafiee S. Madadlou A. (2012). The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study. Computers and Electronics in Agriculture 88 32-43.
Boniecki P. Koszela K. Piekarska-Boniecka H. Weres J. Zaborowicz M. Kujawa S. Majewski A. Raba B. (2015). Neural identification of selected apple pests. Computers and Electronics in Agriculture 110 9-16.
Cobaner M. Citakoglu H. Kisi O. Haktanir T. (2014). Estimation of mean monthly air temperatures in Turkey. Computers and Electronics in Agriculture 109 71-79.
Engelbrecht A.P. (2007). Computational Intelligence. An Introduction. 2nd ed.. John Wiley & Sons Ltd. ISBN 978-0-470-03561-0.
Gocic M. Motamedi S. Shamshirband S. Petkovic D. Sudheer C. Hashim R. Arif M. (2015). Soft computing approaches for forecasting reference evapotranspiration. Computers and Electronics in Agriculture 113 164-173.
Gocic M. Trajkovic S. (2011). Service-oriented approach for modeling and estimating reference evapotranspiration. Computers and Electronics in Agriculture 79 153-158.
Hagan M.T. Demuth H.B. Beale M.H. De Jesus O. (2014). Neural Network Design (2nd Edition). Martin Hagan. ISBN-10: 0-9717321-1-6. ISBN-13: 978-0-9717321-1-7.
Hendrawan Y. Murase H. (2011). Neural-Intelligent Water Drops algorithm to select relevant textural features for developing precision irrigation system using machine vision. Computers and Electronics in Agriculture 77 214-228.
Jain L.C. Tan S.C. Lim. C.P. (2008). An Introduction to Computational Intelligence Paradigms. Studies in Computational Intelligence 137 1-23.
Kasabov N.K. (1998). Foundations of neural networks fuzzy systems and knowledge engineering. Cambridge Mass. MIT Press. ISBN 0-262-11212-4.
Kisi O. Sanikhani H. Zounemat-Kermani M. Niazi F. (2015). Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture 115 66-77.
Marti P. Shiri J. Duran-Ros M. Arbat G. de Cartagena F.R. Puig-Bargues J. (2013a). Artificial neural networks vs. Gene Expression Programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents. Computers and Electronics in Agriculture 99 176-185.
Marti P. Gasque M. Gonzalez-Altozano P. (2013b). An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data. Computers and Electronics in Agriculture 91 75-86.
Mohammadi K. Shamshirband S. Motamedi S. Petkovic D. Hashim R. Gocic M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture 117 214-225.
Mollazade K. Omid M. Arefi A. (2012). Comparing data mining classifiers for grading raisins based on visual features. Computers and Electronics in Agriculture 84 124-131.
Nadimi E.S. Jorgensen R.N. Blanes-Vidal V. Christensen S. (2012). Monitoring and classifying animal behaviour using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks. Computers and Electronics in Agriculture 82 44-54.
Nazghelichi T. Aghbashlo M. Kianmehr M.H. (2011). Optimization of an artificial neural network topology using coupled response surface methodology and genetic algorithm for fluidized bed drying. Computers and Electronics in Agriculture 75 84-91.
Nourbakhsh H. Emam-Djomeh Z. Omid M. Mirsaeedghazi H. Moini S. (2014). Prediction of red plum juice permeate flux during membrane processing with ANN optimized using RSM. Computers and Electronics in Agriculture 102 1-9.
Shiri J. Nazemi A.H. Sadraddini A.A. Landeras G. Kisi O. Fard A.F. Marti P. (2014). Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Electronics in Agriculture 108 230-241.
Szczypinski P.M. Klepaczko A. Zapotoczny P. (2015) Identifying barley varieties by computer vision. Computers and Electronics in Agriculture 110 1-8.
Teimouri N. Omid M. Mollazade K. Rajabipour A. (2014). A novel artificial neural networks assisted segmentation algorithm for discriminating almond nut and shell from background and shadow. Computers and Electronics in Agriculture 105 34-43.
Zurada J.M. (1992). Introduction to Artificial Neural Systems. West Publishing Co. St. Paul MN USA. ISBN 0-3 14-93391 -3.