Machine Learning-Based Plant Disease Detection for Agricultural Applications: A Review

The agriculture sector plays a major role in supplying high-quality food and makes the biggest contribution to growing economies and people. Plant ailments have the power to drastically limit food production and eliminate species diversity. Early identification of plant diseases using accurate or automatic detection techniques can increase the quality of food output and decrease financial losses. In recent years, deep learning has considerably improved the identification precision of image classification and object detection systems. The development of agricultural products is crucial for the economy of the country. Farmers have a wide range of alternatives for the production of numerous goods. However, plant diseases can impair any agricultural product. Information technology advancements can be utilised to quickly identify and locate agricultural illnesses. Plant diseases must be categorised in order to evaluate agricultural output, increase market value, and maintain quality standards. Despite the fact that many eminent researchers have contributed their suggestions for developing such systems, there are several shortcomings in the recommended and developed systems. The paper provides in-depth discussions on plant illnesses, disease detection systems, and disease classification using image acquisition, image pre-processing, picture segmentation, feature extraction, and classification.