This project involves the detection and classification of soybean field components using a lightweight deep learning model based on MobileNetV2. The model identifies four classes: Soybean, Soil, Broadleaf, and Weed, and groups them into two categories:
- Category 1 (Non-Weeds): Soil, Soybean, Broadleaf
- Category 2 (Weeds): Weed
The aim is to assist farmers in managing weeds effectively and improving crop yields through precise identification.
- Lightweight Model: Built using MobileNetV2, ensuring efficient performance and deployment on resource-constrained devices.
- Multi-Class Classification: Accurately differentiates between field components and weeds.
- Data Augmentation: Preprocessed dataset with techniques to improve model robustness against variations in field conditions.
- Deployment-Ready: Optimized for real-time applications, such as drones or automated sprayers.
- Input: Field images are fed into the model.
- Processing: The model classifies the input image into one of the four classes.
- Output: Results guide weed management decisions, such as precision spraying.
- Model Architecture: MobileNetV2
- Frameworks: TensorFlow/Keras
- Preprocessing Tools: OpenCV for image resizing and augmentation.
- Precision Agriculture: Identify and target weeds for selective herbicide application.
- Field Monitoring: Real-time weed and crop health analysis using drones or IoT devices.
- Expanding the dataset to cover more weed and crop varieties.
- Improving performance under extreme environmental conditions.
- Integrating the model with geographic information systems (GIS).