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 | AgroCare Diagnosis in Phytopathology |
|---|---|
| ABSTRACT | In the age of digital agriculture, effective and accessible plant health management systems are crucial for food security and sustainable farming. AgroCare is a modern web and mobile application designed to diagnose plant diseases through leaf image analysis with deep learning techniques. This project aims to help farmers and agricultural professionals by offering a scalable, real-time solution for early detection and treatment of crop diseases. The system enables users to securely register, log in, capture or upload images of affected leaves, and get instant diagnosis results using a Convolutional Neural Network (CNN) model. The application determines if a plant is healthy or diseased, provides information on supplements and fertilizers, offers the Minimum Support Price (MSP), identifies the specific disease (if applicable), and shows a confidence score. It has the potential to reduce crop losses, optimize pesticide usage, and encourage sustainable farming practices. A. Existing Systems Based on its diagnosis, AgroCare recommends treatment methods, including organic and chemical options, as well as preventive measures to reduce future risks with favorable crop information interface . |
| AUTHOR | PROF.PUSHPAVENI.H.P, VISHWANATH HOOLAGERI, VARUN GOWDA C, DEEKSHITH S, ABHISHEK N |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311106 |
| pdf/106_AgroCare Diagnosis in Phytopathology.pdf | |
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