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

Authors

  • P.V.Yeswanth Department of Electronics and Communication, National Institute of Technology, Tiruchirappalli, India.
  • M.Saisanika Department of Electronics and Communication, National Institute of Technology, Tiruchirappalli, India.
  • S.Deivalakshmi Department of Electronics and Communication, National Institute of Technology, Tiruchirappalli, India.

Keywords:

Super resolution, Self-governing Feedback Network (SGFN), bean leaf disease detection, low resolution, image classification

Abstract

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.

Published

2024-05-30

How to Cite

P.V.Yeswanth, M.Saisanika, & S.Deivalakshmi. (2024). Self-Governing Feedback Network (SGFN) Based Super Resolution for bean leaf disease detection. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(3). Retrieved from http://mail.joaasr.com/index.php/joaasr/article/view/950

Issue

Vol. 6 No. 3 (2024): JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH-ICKE-2023

Section

Conference Proceedings

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.