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India ranked second in world, in rice production after China, with an annual production of about 124 million metric tons in the year 2021-2022. The loss of agricultural economy and losses in the community are therefore significantly influenced by diseases in the paddy plant. It is very difficult for the farmers to detect and recognize the various symptoms and diseases in paddy. So, the major challenge is how one can effectively control the diseases by detecting the symptoms earlier. Keeping this challenge in mind this paper proposes a better and effective solution for the detection and identification of diseases in paddy plants using machine learning techniques. In recent times SVM, K-NN, A-NN and CNN are some of the popular approaches used in similar kind of studies. According to researches done all these techniques have certain demerits. SVM are not suitable in large datasets, for K-NN the computation cost is …