Rice Leaves Disease Diagnose Empowered with Transfer Learning

In the agricultural industry, rice infections have resulted in significant productivity and economic losses. The infections must be recognized early on to regulate and mitigate the effects of the attacks. Early diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses, reduce pesticide use, and boost ta country's economy. Assessing the health of a rice plant through its leaves is usually done as a manual ocular exercise. In this manuscript, three rice plant diseases: Bacterial leaf blight, Brown spot, and Leaf smut, were identified using the Alexnet Model. Our research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply, lowering prices, and reducing production's environmental impact that affects the economy of any country. Farmers would be able to get more exact and faster results with this technology, allowing them to administer the most acceptable treatment available. By Using Alex Net, the proposed approach achieved a 99.0% accuracy rate for diagnosing rice leaves disease.