Early Disease Prediction Detection of Blast in Oryza Sativa

One of the major challenges in agriculture is feeding the growing population with good quality and quantity yield. It is very obvious that the pest attack on the crop is one of the main reasons for reduced crop yields. In this paper, a system is proposed for the early detection of rice blast, sheath rot, and presence of pests (rice whorl maggot and black bug) through various image processing techniques. The collected data are processed through various machine learning algorithms to analyze the behavior and characteristics of pests and diseases. The derived solution is communicated to the farmers and alerted about the diseases and pests at a very early stage. So that with the lesser dosage of pesticides and chemicals, the required quality and quantity of the food can be achieved.