(untitled) (Aintelia Science Notes, 2024)
Weeds represent one of the most economically damaging biotic stresses in row-crop production, yet current chemical control strategies face mounting regulatory pressure and resistance evolution. This paper presents the design, integration, and preliminary field evaluation of an autonomous ground robot for in-season mechanical weed control in row-crop systems, combining Edge-AI inference with a five-band multispectral vision pipeline. The system architecture comprises a four-wheel differential-drive platform, a MicaSense-class multispectral imaging module, an NVIDIA Jetson Orin edge-computing unit, and a servo-actuated inter-row/intra-row tine mechanism. A YOLOv8-nano model, trained on a field-collected and synthetically augmented dataset of 14,200 annotated multispectral frames covering four principal row-crop weed species (Chenopodium album, Amaranthus retroflexus, Galium aparine, Convolvulus arvensis), is deployed for real-time per-frame weed localisation at 18 frames per second on the embedded platform. Vegetation index fusion combining NDVI and NDRE channels provides a secondary spectral discriminator that reduces false-positive detections on soil and crop residue by 34% relative to RGB-only inference. RTK-GNSS-guided row-following navigation achieves lateral deviation of ≤1.8 cm (1σ) at 0.6 m/s travel speed. Field trials on maize and soybean demonstrate a weed removal efficacy of 82–89% with a crop damage rate below 1.2%, at a treatment throughput of 0.45 ha/h.