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Agriculture as a source of food is very important for mankind. Therefore, diagnosis of plant diseases is a major concern. Plant disease diagnosis through plant monitoring is important for sustainable agriculture. Monitoring plant diseases manually is very difficult. Managing plant diseases requires a lot of effort and expertise. Traditionally, this method of diagnosing leaf disease requires a lot of information about subjective, time -consciousness, costly. To solve this problem, we introduce the proposed U-net based Convolutional Neural Network (UNetCNN) with ReLu activation function is used to classify the plant disease. First we collect the dataset from online kaggle then preprocess the image to remove noise using median filter. Second extract the finest features of plant leaf disease from preprocessed image. Finally the proposed UNetCNN and ReLu activation is efficiently classify the plant disease. The proposed method experimental result produces better result than other methods.

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How to Cite
G.Sivakumar, & M.Udayadevi. (2023). Plant leaf disease prediction based on deep learning using U-net based Convolutional Neural Network. International Journal of Intellectual Advancements and Research in Engineering Computations, 11(2), 10–17. Retrieved from