SCORDA-Driven Classification of Weed Seeds via Raspberry PI and Camera Module

Abstract: Seed quality plays a crucial role in ensuring high crop yield and sustainable agriculture. The presence of weed seeds mixed with normal crop seeds reduces germination efficiency, lowers productivity, and increases the cost of weed management. Traditional manual separation of weed seeds is labour-intensive, time-consuming, and prone to errors. To address this challenge, this project proposes a real-time automated weed seed detection system using deep learning. The system employs YOLOv11, a state-of-the-art object detection algorithm, integrated with a Raspberry Pi and camera module for on-field, real-time processing. The YOLOv11 model is trained on a dataset of crop and weed seeds, enabling it to accurately detect and classify weed seeds within seed samples. The Raspberry Pi provides a cost-effective, portable, and low-power platform for implementation, making the system suitable for practical agricultural applications. The proposed solution enhances seed purity assessment by offering high-speed, reliable, and automated detection, ultimately improving crop productivity and reducing dependence on manual labor. This system can be further extended for large-scale seed processing units and integrated with sorting mechanisms for complete automation.

Keywords: Weed Seed Classification, Image Processing, Deep Learning, Raspberry Pi, Camera Module, YOLO v11, Precision Agriculture, Machine Vision.