Machine Learning and Deep Learning in Crop Management—A Review

Abstract

The use of computer vision techniques based on machine learning (ML) and deep learning (DL) algorithms has increased in order to improve agricultural output in a cost-effective manner. Researchers have used ML and DL techniques for different agriculture applications such as crop classification, automatic crop harvesting, pest and disease detection from the plant, weed detection, land cover classification, soil profiling, and animal welfare. This chapter summarizes and analyzes the applications of these algorithms for crop management activities like crop yield prediction, diseases and pest detection, and weed detection. The study presents advantages and disadvantages of various ML and DL models. We have also discussed the issues and challenges faced while applying the ML and DL algorithms to different crop management activities. Moreover, the available agriculture data sources, data preprocessing techniques, ML algorithms, and DL models employed by researchers and the metrics used for measuring the performance of models are also discussed.