International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Mulberry Leaf Disease Detection Using Deep Learning |
|---|---|
| ABSTRACT | Plant diseases significantly impact agricultural productivity and economic sustainability, particularly in sericulture, where mulberry (Morus alba) plants serve as the primary food source for silkworms. Early detection and management of mulberry leaf diseases are essential for maintaining crop yield and silk quality. This research presents a deep learning–based framework for automated detection of mulberry plant diseases and recommends appropriate treatment solutions. The proposed system employs a ResNet100-based feature extraction model integrated with a softmax classifier for disease identification. The architecture is trained and validated on a custom dataset of healthy and diseased mulberry leaf images collected under varying lighting and environmental conditions. Experimental results show that the proposed model achieves 97.8% accuracy and effectively distinguishes among major diseases, including leaf rust, powdery mildew, and bacterial blight. Furthermore, a solution recommendation engine is integrated to suggest optimal control measures. |
| AUTHOR | JHANAVI T S, NIRIKSHA S, SAMBRAMA RAWAL, SUPRITHA K, PROF. GOWRAMMA B H B.E, Dept. of Computer Science and Engineering (Data Science), Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India Dept. of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312100 |
| pdf/100_Mulberry Leaf Disease Detection Using Deep Learning.pdf | |
| KEYWORDS | |
| References | [1] H. Li, J. Zhao, and L. Sun, “Hybrid CNN–Transformer Model for Leaf Disease Classification,” IEEE Access, vol. 13, pp. 126489–126500, 2025. [2] R. Kumar and P. Singh, “MobileNetV3-based Lightweight Model for Real-Time Leaf Disease Detection,” Computers and Electronics in Agriculture, vol. 220, 109712, 2024. [3] Y. Zhou, X. Liu, and W. Zhang, “ResNet-based Feature Extraction for Mulberry Leaf Disease Detection,” Applied Intelligence, vol. 54, no. 7, pp. 15984–15996, 2024. [4] S. Das, M. Ghosh, and P. Reddy, “Transfer Learning with VGG19 for Plant Disease Detection,” Expert Systems with Applications, vol. 234, 121047, 2023. [5] Q. Nguyen, J. Wang, and L. Zhao, “Deep CNN with Recommendation System for Rice Disease Management,” IEEE Transactions on Computational Biology and Bioinformatics, vol. 20, no. 2, pp. 678–689, 2023. [6] N. Subramanian and R. Mehta, “Automated Diagnosis of Leaf Diseases Using Deep Residual Networks,” IEEE ICMLA Conference Proceedings, pp. 421–427, 2023. |
