On-Edge Weed Detection Using Unmanned Aerial Vehicles

Mohamed Elmohandes, Diaa Addeen Abuhani, Maya Haj Hussain, Jowaria Khan, Imran A. Zualkernan

Keywords: Precision agriculture, Weed detection, Unmanned aerial vehicles, Edge computing, Real-time, Deep learning, U-Net

TL;DR: We propose an efficient real-time weed-detection pipeline using UAVs that uses a lightweight model and operates with low power consumption.

Abstract: Weed detection is a critical task in precision agriculture, as weeds significantly reduce crop yields and increase production costs. This work presents an efficient, low-cost, and low-power UAV-deployable weed detection pipeline that relies solely on consumer grade RGB cameras. We propose novel lightweight U-Net and Attention U-Net architectures optimized for real-time semantic segmentation on an edge device. To enhance segmentation accuracy, we integrate additional RGB derived features. Experiments on the CoFly-WeedDB detection dataset demonstrate that both architectures perform effectively on RGB imagery, with further improvements when incorporating hue and edge detection features. The proposed lightweight U-Net architectures, made more efficient through quantization, achieves IoU scores above 50% and Dice scores exceeding 60% on the CoFly-WeedDB dataset across multiple augmentation levels. These findings highlight the practicality of deploying lightweight deep learning models for precision weed detection in resource-constrained agricultural environments.

Submission Number: 16