MEGATHON - 2023: Plant Disease Detection using CNN

Project Overview

MEGATHON-2023 was a project aimed at detecting the presence of infections or diseases in plant leaves using Convolutional Neural Network (CNN) models. The project's primary objective was to develop an efficient and accurate system for automated plant disease diagnosis, leveraging the capabilities of deep learning techniques.

Objectives

• Utilize CNN models for image classification tasks related to plant disease detection.
• Experiment with various CNN architectures, including varying the number of layers, units in each layer, and activation functions, to optimize performance.
• Implement convolutional layers and max-pooling techniques to enhance the model's ability to extract meaningful features from plant leaf images.
• Achieve a high level of accuracy in identifying plant diseases or infections to assist farmers and agricultural professionals in early detection and treatment.

Methodology

• Dataset Selection: A large dataset containing images of plant leaves affected by various diseases and infections was chosen for training and testing the CNN models.
• Model Development: CNN models were developed and trained using Python and popular deep learning libraries such as TensorFlow or PyTorch. Experimentation was conducted to determine the optimal architecture and hyperparameters for the models.
• Training and Evaluation: The CNN models were trained on the dataset and evaluated using metrics such as accuracy, precision, recall, and F1-score to assess their performance.
• Integration with Front-End: A front-end application was developed using Flask, HTML, and CSS to provide a user-friendly interface for interacting with the trained models. Users could upload images of plant leaves, and the system would provide predictions regarding the presence of diseases or infections.
• Token Issuance: Depending on the outcome of the predictions, tokens were issued to users, providing them with information about the detected issues and potential treatment options.

Skills Utilized

• Deep Learning: Utilization of CNN models for image classification tasks.
• CNN Architecture Design: Experimentation with different architectures and hyperparameters.
• Python Programming: Development of CNN models and integration with the front-end application.
• Flask Framework: Implementation of the web application for user interaction.
• HTML/CSS: Designing the front-end interface for uploading images and displaying predictions.
• Problem Solving: Addressing challenges related to dataset preprocessing, model optimization, and deployment.

Future Directions

• Enhancing Model Performance: Continuously refining CNN architectures and training methodologies to improve accuracy and efficiency.
• Scaling the Solution: Deploying the system on cloud platforms to make it accessible to a wider audience of farmers and agricultural professionals.
• Incorporating Additional Features: Integrating features such as real-time image capture, disease severity estimation, and treatment recommendations to enhance the system's functionality.

Team members : Vaishnavi Shivkumar , Harshvardhan Pandey , Meet Gera , Kushang Agarwal

References:

1. https://www.kaggle.com/code/itsmegood/rice-leaf-disease

2. https://www.kaggle.com/code/swastikkulkarni/rice-crop-detection

3. OpenAI for researching and fixing minor bugs. Also to learn more about optimising our model.