Title: Optimising agricultural practices with machine learning: a comprehensive review

Authors: R. Deepa; A. Sivasamy; S. Selvam

Addresses: Department of Electronics and Communication Engineering, Nehru Institute of Engineering and Technology, Coimbatore – 641105, Tamil Nadu, India ' Department of Agricultural Engineering, Nehru Institute of Technology, Coimbatore – 641105, Tamil Nadu, India ' Department of Mechanical Engineering, Adithya Institute of Technology, Coimbatore – 641107, Tamil Nadu, India

Abstract: The agricultural sector is witnessing a growing utilisation of machine learning (ML) across a range of applications, including yield prediction, crop classification, disease detection, pest monitoring, irrigation management, and soil analysis. ML algorithms can analyse large volumes of data generated from various sources such as remote sensing, weather stations, and soil sensors to provide insights and recommendations that can improve the efficiency and productivity of agricultural systems. The use of AI and deep learning techniques in agriculture has shown promising results in improving crop productivity, disease and pest detection, soil analysis, and irrigation management. In this article, a survey of deep learning and artificial intelligence techniques in agriculture is presented to provide valuable insights into the latest advances and applications. Finally, a conclusion regarding open challenges, and directions for future research are presented. Deep learning and AI have the potential to revolutionise the way we approach agriculture, leading to more efficient and sustainable: 1) crop yield prediction; 2) weed and pest detection; 3) disease detection; 4) precision agriculture; 5) robotic farming. Overall, the use of deep learning and AI in agriculture has the potential to improve efficiency, reduce waste, and increase productivity, leading to a more sustainable and profitable agricultural industry.

Keywords: deep learning; AI; image processing; machine learning; agriculture.

DOI: 10.1504/IJAITG.2023.138116

International Journal of Agriculture Innovation, Technology and Globalisation, 2023 Vol.3 No.4, pp.354 - 369

Received: 15 Jun 2023
Accepted: 03 Feb 2024
Published online: 29 Apr 2024 *

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