Hackathon Project webapp to identify plant disease from leaf images
This is an innovation week hackathon project at Vellore Institute of Technology where we identify various plant disease by sending the leaf image to a CNN model trained in various plant disease images. It gives us the disease name, cause of disease and how to treat it in multiple languages.
Project Overview
Innovation Week Hackathon Project: AI-Powered Plant Disease Detection
Project Overview
This project was developed during the Innovation Week Hackathon to address plant disease identification challenges faced by farmers and gardeners. The solution uses artificial intelligence to enable rapid and accurate diagnosis of plant diseases through image analysis.
Core Functionality
The system analyzes uploaded images of plant leaves and provides:
Disease Identification: Determines the specific disease affecting the plant.
Cause Analysis: Identifies the underlying cause of the disease.
Treatment Recommendations: Suggests actionable steps for treatment and prevention.
To ensure accessibility, results are delivered in multiple languages, supporting farmers across diverse geographic regions.
Technical Implementation
Model Architecture: Convolutional Neural Networks (CNNs) trained on a dataset of thousands of plant leaf images.
Image Processing: The model processes uploaded leaf images and outputs disease classification along with supplementary diagnostic information.
User Interface: A streamlined interface for image upload and result presentation.
System Workflow
User uploads an image of a plant leaf.
The CNN model processes the image to detect and classify the disease.
The system generates diagnostic output, including disease name, cause, and recommended treatment steps.
Results are presented to the user in a clear, actionable format, available in multiple languages.
Impact and Objectives
This project aims to:
Reduce agricultural crop losses through early disease detection.
Empower farmers with timely, data-driven insights.
Support sustainable agricultural practices by enabling prompt and informed treatment decisions.
Increase accessibility through multilingual support for diverse farming communities.
Conclusion
The AI-powered plant disease detection system demonstrates the potential of machine learning to address real-world agricultural challenges. By combining computer vision with practical diagnostic guidance, the solution provides an accessible tool for improving crop health and supporting farmers worldwide.
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