Deployment Link: https://sanjaykumar200599-plant-disease-detection-main-app-8fbyrr.streamlit.app/

Project Overview Plant diseases can cause significant losses in agriculture, affecting both crop yield and quality. Early and accurate detection of plant diseases is crucial for effective management and control. This project focuses on developing a machine learning-based plant disease detection system that can classify different plant diseases from leaf images. Objectives Develop a machine learning model for identifying plant diseases using image classification techniques. Use a dataset containing images of healthy and diseased plant leaves. Train the model using CNNs or Transfer Learning . Deploy the model as a web or mobile application for real-world use. Technology Stack Programming Language: Python Libraries & Frameworks: TensorFlow/Keras, OpenCV, scikit-learn, NumPy, Pandas Dataset: PlantVillage dataset (or other custom datasets) Model: CNN-based deep learning model (or pre-trained models like ResNet50, MobileNet) Deployment:streamlit Implementation Steps Data Collection: Gather and preprocess images of plant leaves (healthy and diseased). Model Selection: Use a CNN or a pre-trained model for feature extraction and classification. Training & Evaluation: Train the model using labeled data, tune hyperparameters, and test for accuracy. Deployment: Convert the trained model into a web or mobile app for user-friendly access.