Transfer learning-based cotton plant disease detection using Resnet152V2 and dense neural network layers with image augmentation and fine-tuning techniques.

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]