Self-Governing Feedback Network (SGFN) Based Super Resolution for bean leaf disease detection

Crop loss caused by diseases that result from a range of insects, bacteria, viruses, and fungi has been a severe concern for generations that demands global attention. As a result, diagnosing crop diseases as soon as feasible can dramatically reduce production loss and enhance monetary value. The Self-governing Feedback Network (SGFN) model is suggested in this paper for producing Super Resolution images from low-resolution bean leaf images and recognizing disease. On the bean leaf dataset, the proposed SGFN model is tested for super-resolution factors 2, 4, and 6. PSNRs of 31.27, 35.653, and 37.721 are achieved for super-resolution factors 2, 4, and 6, respectively, with classification accuracies of 99.54, 98.73, and 97.64.