Artificial Neural Networks in Agriculture

Sebastian Kujawa and Gniewko Niedbała
Additional contact information
Sebastian Kujawa: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Gniewko Niedbała: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland

Agriculture, 2021, vol. 11, issue 6, 1-6

Abstract: Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.

Keywords: yield prediction; crop models; soil and plant nutrition; automated harvesting; model application for sustainable agriculture; precision agriculture; remote sensing for agriculture; decision supporting systems; neural image analysis; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/2077-0472/11/6/497/pdf (application/pdf)
https://www.mdpi.com/2077-0472/11/6/497/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:11:y:2021:i:6:p:497-:d:563390

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().