Transforming Agriculture: Deep Learning for Leaf Disease Detection

The largest agricultural country, India, derives quite a good portion of its economy from agriculture. Leaf diseases are manifested by severe yield losses whereby farmers’ lives and the main food security of the nation as a whole might be negatively affected. The Indian environment which is distinct for its diversity of climatic region and vegetation is vulnerable to almost all possible leaf diseases. Thus, there is a need for thorough research and surveillance on plant health. In the face of the rising worldwide attention on sustainable use and food production, appropriate management strategies are crucial to decrease chemical pesticide usage and its environmental impact, by using the high-level Deep Learning (DL) methods to make predictions as well as to identify the leaf diseases. Additionally, mobile technology would offer a scalable way to achieve cost-effective monitoring, thus allowing for more productive and environmentally friendly agriculture throughout various Indian regions. To strengthen agriculture, a CNN-based DL model (CNN_LR) is proposed to enhance leaf disease detection for achieving 96.35% accuracy in agriculture.