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

P.V.Yeswanth; M.Saisanika; S.Deivalakshmi

doi:10.46947/joaasr632024950

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.

DOI:

https://doi.org/10.46947/joaasr632024950

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.

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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). https://doi.org/10.46947/joaasr632024950