![]() ![]() ![]() InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. Manual plant disease monitoring is both laborious and error-prone. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Plants are a major source of food for the world population. ![]()
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