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 | Plant Disease Detector: Leveraging AI for Agricultural Health |
|---|---|
| ABSTRACT | Undetected plant diseases remain a persistent threat to agricultural productivity worldwide. This paper presents Leaf Guard-AI, a browser-accessible diagnostic tool built on convolutional neural network (CNN) technology to classify leaf diseases from uploaded images. Implemented using Tensor Flow/PyTorch and deployed via a Gradio web interface, the system requires no software installation. Beyond disease labelling, it provides confidence estimates and crop-specific remediation guidance. Evaluation on a held-out test partition confirmed classification accuracy exceeding 90% with sub-two-second inference latency, validating the tool for on-farm deployment. |
| AUTHOR | THANU M R, USHA B M, VAISHNAVI G S, RADHA H HOSATTI Students, Dept. of IS&E, Jain Institute of Technology, Davangere, Karnataka, India Associate Professor, Dept. of IS&E, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404137 |
| pdf/137_Plant Disease Detector Leveraging AI for Agricultural Health.pdf | |
| KEYWORDS | |
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