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Cotton is one of the important cash crops in India grown on more than 9 million hectors. Like other crops this also suffers from diseases but the effect on the yield affects many livelihoods. Thus, it is necessary to detect the disease in early phase so that cure can be taken. This paper proposes a Convolutional Neural Network (CNN) model for detecting diseases in cotton plants. The model implements in proposed work utilize transfer learning technique, leveraging pre-trained model that have been trained on extensive datasets. Specifically, the Resnet152v2 model serves as foundational architecture for the model, enabling accurate classification of distinct objects. In addition to Resnet152V2 base, the proposed work incorporates two supplementary dense neural network layers. These additional layers enhance the model capacity to learn complex pattern and improve its classification capabilities. With integration of transfer learning and inclusion of supplementary dense network layer, the model demonstrates its ability to effectively classify objects. In addition to Resnet152a 98.36% accuracy in distinguishing between healthy and diseased cotton plants. [ABSTRACT FROM AUTHOR]

This research aims to develop a Deep learning-based Convolutional Neural Network (CNN) model for detecting tomato plant diseases. Transfer learning involves training the model on a modest dataset by utilizing characteristics from extensive collections of high-resolution images, all while preserving its capacity to generalize effectively. The model builds upon the Inceptionv3 model and incorporates two dense neural network layers to accurately distinguish between the target object and other objects. The accuracy achieved in classifying the presence of tomato plant diseases is 94.06%. [ABSTRACT FROM AUTHOR]