Rice Leaf Disease Prediction Using Machine Learning

MCA

The automated in the realm of agricultural data, detection and cure of rice leaf diseases are greatly needed. In this case, machine learning crucial and does a good job of handling the challenges of identifying leaf diseases. In this research, describe a brand-new machine learning-based approach to disease identification in rice. Here, we considered various kinds of diseases that affect rice leaves and have classified these diseases using various machine learning approaches. In this study, we first extract the characteristics from photos of rice leaf disease. The photos were then classified using a many ML algorithms 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.

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