Deep Convolutional Neural Network BasedCotton Leaf Disease Classification LeveragingTransfer Learning
Keywords:
Deep Learning, Convolutional Neural Network, Transfer Learning, Cotton Leaf Disease Classification, Smart AgricultureAbstract
Cotton is one of the most important crops in Indian agriculture, serving as the primary raw material for the textile industry. However, its productivity is severely impacted by various leaf diseases, making the early and accurate disease detection crucial for effective crop management and yield improvement. Traditional disease identification methods rely on manual inspection, which is time-consuming, labor-intensive, and prone to errors. To address these challenges, this study proposes an automated cotton leaf disease classification system using deep convolutional neural networks (CNNs) with transfer learning. We evaluate three pretrained CNN models—InceptionV3, ResNet152V2, and MobileNetV2—for image-based disease classification. These models leverage transfer learning to enhance feature extraction and classification accuracy while reducing training time. The system is trained and tested on a benchmark cotton leaf disease dataset, and its performance is assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results demonstrate that transfer learning significantly improves classification performance, enabling rapid and reliable disease identification and empowering farmers with an automated tool to take timely preventive measures. This research contributes to smart agriculture by integrating AI-driven solutions for sustainable farming and improved cotton yield.
\keywords{Deep Learning, Convolutional Neural Network, Transfer Learning, Cotton Leaf Disease Classification, Smart Agriculture.
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Copyright (c) 2025 Saikat Samanta, Subhayu Ghosh, Debtanu Ghosh, Dr. Nanda Dulal Jana (Author)
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