A GitHub repository for an AI-based system designed to detect pests and diseases in crops. This project leverages machine learning and image processing techniques to identify agricultural threats, enabling early intervention and reducing crop damage. Ideal for researchers and developers in agritech and AI.
This project focuses on developing an AI-based system for detecting pests and diseases in Indian crops using machine learning. The model is built using Convolutional Neural Networks (CNN) and enhanced with transfer learning, which leverages trained models to improve accuracy. Google Colab serves as the development platform, while TensorFlow and Keras libraries are used for model building and training, offering a robust environment for handling large datasets and complex computations.
Specifically meant for Indian crops, this model is designed to recognize common pests and diseases, ensuring region-specific precision. The ultimate goal is to deploy the model as a web application, providing farmers and agricultural experts with an accessible tool for early diagnosis and treatment. This will help in minimizing crop damage, enhancing yield, and supporting sustainable farming practices.
- Variety and Credibility: We've harnessed a comprehensive dataset from Kaggle (New Plant Diseases Dataset) that spans a wide array of Indian plants, featuring 80 meticulously documented and labeled classes.
- Verified Labels: Every data point in our dataset is accompanied by precise, expert-verified labels, ensuring the highest quality of training and evaluation.
- Cutting-Edge Performance: After just 10 training epochs, our machine learning model consistently hits a 97% accuracy rate. This result reflects our commitment to applying cutting-edge methods and carefully adjusting hyperparameters to achieve top-notch performance and to cure the disease image provided.
- Powered by Google Colab: To make robust training accessible to all, we harnessed the free GPU resources provided by Google Colab.
- Tesla T4 GPU: Our model training benefited from the performance and capabilities of the Tesla T4 GPU, enhancing the efficiency and speed of the training process.
-Gemini: "Integrating the Gemini LLM model into our project has significantly enhanced solution accuracy and reliability. Its advanced capabilities have streamlined our problem-solving process with impressive precision."
Our AI-Based Pest and Disease Detection model goes beyond mere technological innovation; it's a significant step toward safeguarding and exploring India’s rich agricultural heritage. Boasting high accuracy and leveraging the power of Google Colab with a Tesla T4 GPU, this model is set to become an invaluable resource. Whether integrated into a user-friendly web application for farmers, aiding in botanical research, or supporting the preservation of traditional medicine, it’s designed to make a meaningful impact on the future of Indian agriculture.