Improving Plant Disease Detection Using Super-Resolution Generative Adversarial Networks and Enhanced Dataset Diversity

Abstract

The detection of plant diseases is critical for maintaining crop health and maximizing agricultural yields. This research proposes a comprehensive approach to improve plant disease detection by addressing challenges related to unbalanced datasets and leveraging generative adversarial networks (GANs). This research focuses on enhancing the accuracy and generalization capabilities of disease recognition models. To address dataset bias, a larger and more diverse dataset is collected, comprising unhealthy plant leaves from various plants, regions, and disease types. The expanded dataset enables comprehensive training and validation, ensuring a representative depiction of leaf variations and diseases. Domain-specific knowledge and expert guidance are incorporated to capture realistic and characteristic attributes of diseased leaves. To overcome overfitting, the regularization technique is applied during training. These techniques promote the learning of generalized representations and mitigate the generation of unrealistic or repetitive images. The proposed approach is extensively evaluated using Plant Village dataset encompassing various plant species, and disease types. By implementing these solutions, this research enhances the accuracy, robustness, and generalization capabilities of plant disease detection systems. It establishes a foundation for reliable and effective detection methods, contributing to the sustainable management of plant diseases and improved agricultural outcomes.