Abstract

The rapid rise in India's population necessitates an equally quick rise in agricultural production. In India, rice is the staple food crop. However, disease-causing organisms are notoriously easy to introduce to rice crops, resulting in lower yields. Even though pests, climate change, and illnesses all pose problems for agricultural yield, rice agriculture still has the most difficulty with crop diseases. Most crop diseases are caused by or related with bacteria or fungus, and they may strike at any time, from seedling development through harvest. Traditional methods for identifying leaf diseases have relied on human observation. They're time-consuming, costly, and need the expertise of professionals to complete. The human vision-based technique relies heavily on the eyesight of the farmer or expert to be correct. Automated classifier models based on Machine Learning (ML) are required to address the shortcomings of traditional methods. Rice plant diseases (RPD) may be prevented and their effects mitigated if they are detected early. Better crop quality and yields cannot be achieved without controlling the spread of diseases.

Description