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AgriVision: An AI-Powered Multilingual Platform for Plant Disease Detection and Crop Recommendation
Subject area: Agriculture and Veterinary Sciences · Area of research: Artificial Intelligence
DOI: 10.64388/ire1724924
Abstract
Crop health and crop choice both depend on conditions that differ from one farm to the next, including weather, soil, season, and water supply, yet farmers in many regions have little access to timely, locally relevant advice. This paper presents AgriVision, a web-based platform that brings plant disease detection, crop recommendation, and farmer guidance together in one application. A user uploads or captures a photograph of a leaf, and a deep-learning classifier returns the most likely disease with a confidence score, followed by symptoms, preventive practices, and management steps. A separate module suggests crops from the farmer's soil type, season, water availability, and location, and the interface is available in Telugu, Hindi, and English with a saved scan history. The prototype is built for farmers in Telangana and is intended as preliminary decision support, not as a replacement for expert diagnosis. We describe the system design, implementation, and limitations, and outline the field validation required before wider use.
Keywords
plant disease detection; smart agriculture; crop recommendation; computer vision; deep learning; multilingual interface; precision agriculture; farmer decision support
References
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How to cite this paper
@article{1724924,
author = {Pareddy Medha Reddy, P. Akshaiya, M. Satya Hansika, P. Aswitha Naidu},
title = {AgriVision: An AI-Powered Multilingual Platform for Plant Disease Detection and Crop Recommendation},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {1778-1788},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724924.pdf},
abstract = {Crop health and crop choice both depend on conditions that differ from one farm to the next, including weather, soil, season, and water supply, yet farmers in many regions have little access to timely, locally relevant advice. This paper presents AgriVision, a web-based platform that brings plant disease detection, crop recommendation, and farmer guidance together in one application. A user uploads or captures a photograph of a leaf, and a deep-learning classifier returns the most likely disease with a confidence score, followed by symptoms, preventive practices, and management steps. A separate module suggests crops from the farmer's soil type, season, water availability, and location, and the interface is available in Telugu, Hindi, and English with a saved scan history. The prototype is built for farmers in Telangana and is intended as preliminary decision support, not as a replacement for expert diagnosis. We describe the system design, implementation, and limitations, and outline the field validation required before wider use.},
keywords = {plant disease detection; smart agriculture; crop recommendation; computer vision; deep learning; multilingual interface; precision agriculture; farmer decision support},
month = {November},
doi = {https://doi.org/10.64388/ire1724924}
}