A Two-Phase Transfer Learning Approach Based on VGG16 for Multi-Class Classification of Cotton Leaf Diseases
Keywords:
Deep Learning, Transfer Learning, VGG16, Plant Disease Classification, Precision Agriculture, Convolutional Neural Networks, Image Processing.Abstract
The swift and precise identification of leaf diseases plays an important role in crop protection and increasing agricultural productivity. Recently, this process has been automated using deep learning models, especially Convolutional Neural Networks (CNNs). A comprehensive approach for classification of seven different diseases of cotton leaves are, Bacterial Blight, Curl virus, Healthy Leaf, Herbicide Growth Damage, Leaf Hopper Jassids, Leaf Redding and Leaf Variegation. We present a transfer learning stage with two phases based on the VGG16 architecture. In the first step, a pre-trained VGG16 model is leveraged as a fixed feature extractor, adding a distractive classifier head on top of the model. Though, this model is trained for 30 epochs. The second fine-tuning stage involves unfreezing the entire model (including the base VGG16 layers) and training it with a low learning rate for another 5 epochs for better performance. It consisted of 7000 images for training and 1800 images for validation. With the final validation accuracy of 94.8%, and an overall test accuracy of 95%, our approach outperforms state of the art with an F1-score of per-class results with the range 0.91—0.99. The results indicate the effectiveness of the proposed two-phase transfer learning strategy in developing a highly efficient and accurate automated plant disease diagnosis system.
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