Leaf analysis based early plant disease detection using Internet of Things, Machine Learning and Deep Learning: A comprehensive review

Abstract

In agriculture, the timely identification of plant diseases is vital for reducing crop loss, ensuring high-quality yields, and fostering sustainable farming practices. The agricultural industry has experienced a decline in income in recent years due to the prevalence of bacterial, viral, and fungal infections. These pathogens give rise to diseases that progressively impact plants, leading to crop loss, diminished fruit quality, and plant mortality. Prompt identification of plant disease symptoms plays a pivotal role in enabling farmers to effectively manage these diseases and secure a profitable yield. This paper explores the utilization of IoT, Machine Learning, and Deep Learning techniques to detect disease symptoms at various stages, allowing for timely interventions to prevent extensive crop losses and the spread of diseases within agricultural plots. The objective of this study is to investigate a variety of approaches for early detection of diseases affecting plants.

URN:NBN:sciencein.jist.2024.v12.734