Project Overview
This project aims to develop a machine learning model capable of accurately diagnosing plant diseases based on images of their leaves. Early detection of plant diseases is crucial for preventing significant crop losses and ensuring agricultural sustainability.
Dataset
- Source: It is already in the repo
- Description: The dataset should include a diverse collection of leaf images from various plant species, covering both healthy and diseased plants.
Data Preprocessing
- Image Augmentation: Techniques like rotation, flipping, and cropping will be used to increase the dataset size and prevent overfitting.
- Normalization: Pixel values will be standardized to a common range.
Feature Extraction
- Deep Learning Features: Pre-trained convolutional neural networks (CNNs) like VGG or ResNet will be used to extract high-level features from the leaf images.
Model Selection and Training
- Algorithm: A suitable algorithm, such as a convolutional neural network (CNN), will be selected based on its performance on the dataset.
- Training: The model will be trained on the preprocessed dataset using appropriate training techniques.
Model Evaluation
- Metrics: The model's performance will be evaluated using metrics like accuracy, precision, recall, and F1-score.
- Fine-tuning: If necessary, the model will be fine-tuned by adjusting hyperparameters or experimenting with different architectures.
Deployment
- Platform: The trained model can be deployed on a web application or mobile app for easy access by farmers.
Future Work
- Real-time Prediction: Explore real-time prediction capabilities for immediate disease diagnosis.
- Disease-Specific Models: Develop specialized models for specific plant diseases or regions.
- Integration with IoT: Integrate the model with IoT devices for automated monitoring and early warning systems.
Usage
- Data Preparation: Place your dataset in the specified directory.
- Training: Run the training script to train the model.
- Prediction: Use the trained model to predict diseases on new leaf images.
Contributing
Contributions are welcome!