Abstract

The detection and diagnosis of plant diseases are crucial for ensuring agricultural productivity and sustainability. This project presents a solution for plant disease detection using deep learning, integrated with a user-friendly web application. Utilizing Convolutional Neural Networks (CNN), this system accurately identifies and classifies 39 different plant diseases from leaf images. The integration of these modules creates a comprehensive tool that not only identifies plant diseases with high accuracy but also offers actionable insights for managing plant health. This application aims to support farmers and gardeners by providing timely and accurate disease diagnoses, ultimately contributing to better crop management and yield.

The project includes both frontend and backend development, employing HTML and CSS for the user interface, Firebase for database management, and Python for backend processing. Users can easily interact with the system through several key modules:

• User Authentication: Login and Signup functionalities ensure secure access to the system.
• Image Upload: Users can upload images of plant leaves for analysis. The system processes these images to detect the presence and type of disease.
• Disease Classification: The trained model analyzes the uploaded leaf image and classifies them into one of 39 predefined disease categories.
• Prediction History: Users can view the history of their previous predictions, allowing for tracking and management of plant health over time
• Remedies and Fertilizers: Based on the detected disease, the system provides users with appropriate remedies and recommended fertilizers to address the issue effectively.

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Technologies Used

Modules

1. User

• 🍁 Login/Signup
• 🍁 Upload Leaf image
• 🍁 39 plant diseases
• 🍁 View History of previous predictions
• 🍁 View remedies and fertilizers