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Rice is amongst the majorly cultivated crops in India and its leaf diseases can have a substantial impact on output and quality. The most important component is identifying rice leaf diseases, which have a direct impact on the economy and food security. Brown spot, Leaf Blast, Hispa are the most frequently occurring rice leaf diseases. To resolve this issue, we have studied various machine learning and deep learning approaches for detecting the diseases on their leaves by calculating their accuracy, recall, and precision to measure the performance. This study helps the farmers by detecting the diseases in rice leaves in order to get a healthy crop yield. The deep learning models perform well when compared with the machine learning methods. After analyzing all of the deep learning models, we found that the 5-layer convolution model had the best accuracy of 78.2 %, while others, such as VGG16, had a lower accuracy of 58.4%.

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