A Fine-tuned Deep Learning-based VGG16 Model for Cotton Leaf Disease Classification

This research paper explored the innovative utilization of deep learning algorithms for cotton leaf disease classification, the VGG16 model fine-tuned being the most powerful tool in diagnosing bacterial blight, curl virus, fusarium wilt, and distinguishing these from healthy leaf states. Typically, traditional disease detection methods are prone to human error and are labor-intensive, which makes this paper propose an alternative that uses CNN that is automatic, accurate, and scalable. The VGG16 model that was initially developed for massive image recognition tasks was later fine-tuned to fit the cotton leaf disease classification task and take advantage of a large dataset annotated with leaf images with high resolution. These images which are diverse sets of symptoms as well as other environmental conditions used, processed, and then augmented to help the model in training effectively. The findings of the research emphasize the transformative capability of applying deep learning technology for the identification and management of diseases in agriculture. The study has provided a diagnostic scalable, efficient, and accurate tool that contributes to the advancement of sustainable agriculture by enabling targeted, more effective disease management strategies. The fact that the fine-tuned VGG16 model has been proved successful in our research sets a new direction for the following studies concerning the use of other CNN architectures and the combination of these technologies with existing digital agriculture systems. This development guarantees a giant leap in the agricultural field and also offers a plausible solution to the challenge of plant diseases that impact crop yields and agricultural production.