Plant Disease Detection Using Fusion of Deep Learning Algorithms

This research paper introduces a novel application for predicting plant diseases in cotton and potato plants using Convolutional Neural Networks (CNNs). Separate CNN models were trained on labeled datasets of cotton and potato leaves, each associated with their respective diseases. The primary goal is to employ a fusion of two standard CNN systems to detect various diseases in cotton and potato plants. Given India's heavy reliance on agriculture, this innovation is crucial to address challenges faced by the sector, including technological limitations, limited access to credit and markets, and the impact of climate change.Cotton and potatoes are significant crops; this research paper are susceptible to various diseases that can impede their growth and result in substantial yield losses. The conventional disease detection methods involve manual inspection and disease prognosis, which are time consuming and less accurate. The research showcases the effectiveness of the automated plant disease detection system, with two best models achieving impressive accuracies of 97.10% and 96.94% for cotton and potato plants, respectively. These results offer promising insights for potential applications in crop management, benefiting the agricultural sector and contributing to increased productivity and profitability.