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Artificial Neural Networks in Agriculture

Author

Listed:
  • Sebastian Kujawa

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
    These authors contributed equally to this work.)

  • Gniewko Niedbała

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:6:p:497-:d:563390
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    References listed on IDEAS

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    6. Piotr Boniecki & Agnieszka Sujak & Gniewko Niedbała & Hanna Piekarska-Boniecka & Agnieszka Wawrzyniak & Andrzej Przybylak, 2023. "Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
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    8. Bonfiglio, A. & Camaioni, B. & Carta, V. & Cristiano, S., 2023. "Estimating the common agricultural policy milestones and targets by neural networks," Evaluation and Program Planning, Elsevier, vol. 99(C).
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