Project Title: A Semi-Autonomous Laser-Based Robotic System for Weed Control in Wheat Fields
Description:
Weeds pose a persistent threat to wheat production worldwide, with reported yield losses as high rate, costing billions of dollars annually. Wheat is similarly central to Iraq's food security and economy, making affordable weed control a national priority. This project presents a semi-autonomous, laser-based robotic system that controls weeds in Iraqi wheat fields, replacing chemical herbicides with a precision laser that protects the environment, human health, and soil without disturbing the crop. The system pairs a YOLO26n deep learning model for real-time weed detection with a laser targeting mechanism, trained on a custom dataset of 2,181 images spanning five Iraqi wheat-field weed species. After training, the model reached 84.5% precision, 82.6% recall, and an mAP@50 of 88.8%, then was deployed on a Raspberry Pi 5 via NCNN at roughly 155 ms per frame and 28.5 FPS through a skip-frame strategy. This approach could cut laser-weeding costs well below commercial systems, strengthening farm income and food security in Iraq, and because its detection classes come from training data rather than a fixed crop, it could extend to other crops via a new weed dataset for that field
Uploaded At: 2026-07-06 02:35:22