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 | Leaf Disease Detection Using Deep Learning |
|---|---|
| ABSTRACT | Plant diseases significantly affect agricultural productivity and crop quality. Early and accurate disease detection is essential for improving crop yield and reducing economic losses. This paper presents a Plant Leaf Disease Detection and Recommendation System Using Deep Learning The proposed model simultaneously identifies the plant species and detects the corresponding leaf disease from an input image. The system is developed using PyTorch and deployed through a Flask web application for real-time disease prediction. In addition to disease classification, it provides treatment recommendations, preventive measures, and pesticide suggestions. The lightweight MobileNetV3 architecture ensures high accuracy with low computational cost, making the system suitable for precision agriculture and real-time farming applications. |
| AUTHOR | DR. T.V.S.SRIRAM, D.APPALARAJU, S.NAVEEN Sr. Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407013 |
| pdf/13_Leaf Disease Detection Using Deep Learning.pdf | |
| KEYWORDS | |
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