A comprehensive project implementing machine learning models for accurate crop and weed detection, enhancing agricultural efficiency through advanced data preprocessing and computer vision techniques.
This project implements machine learning models using YOLOv3 to accurately detect crops and weeds in agricultural images, enhancing agricultural efficiency by differentiating between desired plants and unwanted weeds.
Data: Contains 1300 images of sesame crops and various weeds with YOLO format labels. Training: Scripts and notebooks for training the YOLOv3 model. Detection: Scripts and notebooks for performing detection using the pre-trained model. Models: Pre-trained model weights for detection. Results: Output images and evaluation metrics showcasing model performance.
This project showcases the integration of advanced machine learning techniques to solve real-world agricultural challenges, enhancing crop yield and farming efficiency.