What does this research mean for the field?

An integrated web-based system combining YOLOv8-based multispectral image analysis and Large Language Models provides an effective automated decision-support tool for detecting and managing plant diseases. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop PhytoGuard, an AI-based system for early detection and management of plant diseases to assist farmers.

May 18, 2026International Journal of Novel Research and Development0 citationsOpen Access

Multispectral Yolov8-Based Plant Disease Detection and Intelligent Recommendation System

MMMrs.G.Vijayalaxmi Mrs.G.Vijayalaxmi NSNALLA SIDDIVINAYAKASri Ganapathi Sachchidananda Vagdevi Center VCVEMULAPALLI CHAKRADHARSri Ganapathi Sachchidananda Vagdevi Center

Key Points

To develop PhytoGuard, an AI-based system for early detection and management of plant diseases to assist farmers.
Developed a web-based application integrating a YOLOv8 model for disease detection using RGB and multispectral images.
Utilized the Groq API with a Large Language Model for generating detailed and organized treatment suggestions.
Implemented as a full-stack app using React, Flask, and Supabase PostgreSQL for data management.
The YOLOv8 model accurately identifies disease areas with bounding boxes and confidence scores.
The system provides customized treatment recommendations, enhancing farmers' decision-making capabilities.
Users experienced reduced crop loss and gained knowledge for environmentally sustainable practices.

Abstract

PhytoGuard is a web-based system that uses AI to help farmers find and deal with plant diseases early on. It does this by using advanced computer vision and deep learning techniques. Many farmers can't use traditional methods for diagnosing diseases because they take a lot of time and need expert knowledge. The system uses the YOLOv8 object detection model, which was trained on a wide range of plant diseases, including RGB and multispectral images, to find disease areas on plant leaves and show them with bounding boxes and confidence scores.Along with finding diseases, PhytoGuard uses the Groq API to connect to a Large Language Model that makes detailed and organized treatment suggestions. These suggestions cover the causes of diseases, ways to avoid them, ways to treat them, how to use fertilizer, and how to think about the environment. The system is built as a full-stack web app with React on the front end, Flask on the back end, and Supabase PostgreSQL for safe data storage and user management.PhytoGuard is a complete decision-support tool that combines automated disease detection with smart advisory support. It helps farmers be more productive, lose fewer crops, and use more environmentally friendly farming methods.

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