In the sphere of agriculture, automated detection and diagnosis of rice leaf diseases is widely required. data.Here, machine learning is crucial and does a good job of handling the challenges of identifying leaf diseases. In this research, we describe a brand-new machine learning-based approach to disease identification in rice. Here, we have taken into account different diseases that affect rice leaves and have classified these diseases using various machine learning approaches. In this research, we first extract the characteristics from photos of rice leaf disease. The photos were then classified using a various machine learning methods approaches, and it was discovered that a quadratic SVM classifier had an accuracy of 81.8%. In order to distinguish between various forms of rice illnesses, shape characteristics including area, roundness, area tolesion ratio, etc. were also utilised. The outcomes were favourable and satisfactory.