Mobile Based Deep Learning Application for Weed and Medicinal Plant Detection Using YOLOV5

YOLOv5-based approach for detecting plant weeds, and medicinal plants. The method utilizes a diverse Kaggle dataset and employs Image Label for precise annotations. The YOLOv5 model is trained to identify weeds, and medicinal plants. Performance evaluation demonstrates high accuracy, with metrics such as precision, recall, and F1 score. The approach shows potential for real-time applications in agriculture and botanical research, weed detection, and identification of valuable medicinal plants. The developed solution can greatly benefit farmers, researchers, and botanists in their efforts to tackle plant-related challenges.