Mitigating Agricultural Challenges: A Comprehensive Study on the Impact of Crop Diseases on Rice Production in India

Agriculture holds a pivotal role in fostering the economic growth of a country, contributing significantly to its progress. In India, rice stands as a staple for over 60% of the population, serving as a fundamental grain. Unfortunately, in recent years, the prevalence of diverse diseases affecting rice plants has been on the rise. Specialized viruses known as phages, targeting bacterial, fungal, and viral pathogens, pose a threat to the rice plant from both the upper and lower surfaces of the leaves. Various environmental factors, such as sunlight, temperature, radiation, atmosphere, humidity, soil, and water, intricately influence the natural growth of plants. The diseases affecting rice plants significantly contribute to a decline in production and food quality. Recognizing and preventing these diseases can enhance overall production. These agricultural challenges act as impediments for farmers, impacting their livelihoods and economic well-being, as well as affecting the agricultural industry at large. Hence, there is an imperative need to address and overcome these diseases promptly. Image processing emerges as a valuable tool in diagnosing rice plant illnesses. This study explores the application of Convolutional Neural Network (CNN) technology for identifying rice leaf diseases. A 6-layered CNN-based model is employed for the diagnosis, utilizing a unique dataset derived from field observations and a supplementary Kaggle dataset containing images of rice leaf diseases.