Crop Disease Detection and Pesticide Recommendation Using CNN

Abstract

Abstract: Agriculture plays a crucial role in India’s economy, supporting the livelihoods of 58 percent of the population and contributing 17-18 percent of the GDP. However, plant pests anddiseases pose significant challenges, leading to biotic stress that hampers yield potential and diminishes the quality and quantity of food. Safeguarding crops against diseases is imperative to meet the increasing food demand. Globally, the losses caused by pathogens, pests, and weeds account for 20-40 percent of agricultural productivity. The detection of diseases in cultivated plants is a vital and complex task in agricultural practices. Conventional methods of disease detection and classification are time-consuming and labor-intensive, making it difficult to find optimized solutions. This issue is particularly problematic as farmers and professionals in developing countries require efficient methods to monitor and identify diseases affecting their crops. The implementation of program-based identification for plant diseases offers advantages such as improved detection, reduced human effort, and time savings. In this article, a smart and efficient technique is proposed to detect and classify plant diseases with higher accuracy than existing methods. The pro- posed technique employs Convolutional Neural Networks (CNNs)and focuses on leaf diseases as the main area of interest.