Present Trends in Research on Application of Artificial Neural Networks in Agricultural Engineering

TLDR

Artificial neural networks are increasingly applied in agriculture, as evidenced by a systematic rise in publications and citations, and future research is expected to expand in areas such as drying‑process modeling and image analysis. This study conducts a bibliometric quantitative analysis of ANN publications in agriculture and agricultural engineering. The authors extracted and analyzed Web of Science records up to 2015 to quantify ANN research trends. The analysis identified 236 ANN‑related articles published 1996‑2015, with 118 in *Computers and Electronics in Agriculture*, a rising publication trend from 2011‑2015, and dominant themes of classification and modeling, suggesting continued growth in ANN applications to agricultural engineering.

Abstract

Abstract 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.