A comprehensive impression on identifying plant diseases using machine learning and deep learning methodologies

Abstract

Maintaining healthy plants is essential for long-term agricultural production because agriculture is the backbone of many economies. Agricultural productivity is greatly endangered by plant diseases, which result in huge economic losses. Identifying plant diseases using traditional approaches can be quite laborious, time-consuming, and knowledge-intensive. Automated, precise, and quick diagnosis of plant diseases has been made possible by recent developments in artificial intelligence, mainly in deep learning, and machine learning. This study gives a thorough analysis of how machine learning and deep learning are currently being used to detect plant diseases. Methodologies, datasets, evaluation measures, and the inherent difficulties of the area are all examined. In order to better understand these technologies in practical agricultural contexts, this review will try to shed light on their advantages and disadvantages.