Design and Analysis of Paddy Crop Disease Prediction using Deep Learning Network

A computerized technique for early detection of nutritional deficits in paddy crops is provided by this initiative. Development of an automated system for agriculture that would classify and identify different crop diseases is essential. Agriculture not only keeps people alive, but it also contributes significantly to a country's GDP. Millions of dollars are spent annually to safeguard the crops. Identifying the illnesses is the first step in preventing losses in agricultural production, both qualitative and quantitative. Farmers are put in grave danger when there is a deficiency of nutrients, which negatively affects agricultural productivity. By identifying nutritional inadequacies early on, crop protection and output can be improved. Manual illness identification is quite challenging and imprecise. This means that image-processing methods are essential for surpassing the limitations of human vision and supporting the timely and precise detection of illnesses. This approach suggests a fresh approach to disease classification and diagnosis.