This project aims to automate the detection of plant diseases using deep learning techniques. By leveraging transfer learning with the ResNet50 model pretrained on ImageNet, I have developed a robust system capable of identifying diseases in plant leaves from images. The model is fine-tuned and optimized to classify leaves as healthy or diseased with high accuracy.
https://drive.google.com/file/d/1kA_JWhHQhyzzuzlpzppK2nNTtbiR2N77/view
-Transfer Learning: Utilizes ResNet50 pretrained on ImageNet for feature extraction and fine-tuning.
-Data Augmentation: Enhances model robustness with augmented training data.
-Class Imbalance Handling: Addresses data imbalance using class weights for improved performance.
-Evaluation and Visualization: Provides metrics and visualizations for training/validation accuracy and loss.
-Data Preparation: Organize your dataset into directories (e.g., train and validation) with subdirectories for each class (healthy and diseased).
-Model Training: Fine-tune the ResNet50 model using provided scripts or notebooks.
-Evaluation: Evaluate model performance using validation data and visualize training metrics.
-Prediction: Use the trained model for real-time disease detection on new images.
