Towards Efficient and Intelligent Laser Weeding: Method and Dataset for Weed Stem Detection

TL;DR

This work addresses the challenge of laser weeding by focusing on precise weed-stem localization to guide laser cuts while protecting crops. It proposes an end-to-end framework that integrates crop/weed detection with weed-stem coordinate regression and introduces the Weed Stem Detection (WSD) dataset with 7,161 high-resolution images and 11,151 weed-stem instances to enable real-world evaluation. A semi-supervised extension using a teacher–student framework and a weed-embedding bank leverages unlabeled data to further boost performance. Empirical results show a 6.7% improvement in weeding accuracy and a 32.3% reduction in energy cost over prior recognition-based approaches, demonstrating tangible benefits for practical intelligent laser weeding.

Abstract

Weed control is a critical challenge in modern agriculture, as weeds compete with crops for essential nutrient resources, significantly reducing crop yield and quality. Traditional weed control methods, including chemical and mechanical approaches, have real-life limitations such as associated environmental impact and efficiency. An emerging yet effective approach is laser weeding, which uses a laser beam as the stem cutter. Although there have been studies that use deep learning in weed recognition, its application in intelligent laser weeding still requires a comprehensive understanding. Thus, this study represents the first empirical investigation of weed recognition for laser weeding. To increase the efficiency of laser beam cut and avoid damaging the crops of interest, the laser beam shall be directly aimed at the weed root. Yet, weed stem detection remains an under-explored problem. We integrate the detection of crop and weed with the localization of weed stem into one end-to-end system. To train and validate the proposed system in a real-life scenario, we curate and construct a high-quality weed stem detection dataset with human annotations. The dataset consists of 7,161 high-resolution pictures collected in the field with annotations of 11,151 instances of weed. Experimental results show that the proposed system improves weeding accuracy by 6.7% and reduces energy cost by 32.3% compared to existing weed recognition systems.

Figures (8)

• Figure 1: (a) Weed detection with YOLOv7: Yellow dashed lines mark bounding box diagonals, indicating the geometric center, while the blue dot shows the ground-truth weed stem location. (b) Close-up of the red-boxed region, highlighting misalignment between the bounding box center and the ground-truth point. (c) In laser weeding, better weed detection performance (lower mAP) does not always mean better weed stem detection (lower Euclidean Distance).
• Figure 2: Image samples show raw images (left) and 16x zoomed sections (right), highlighting four different species.
• Figure 3: Distribution of instance annotations per image: The X-axis shows the number of instances (including both bounding box and point annotations) per image, while the Y-axis indicates the corresponding image count.
• Figure 4: The pipeline of intelligent laser weeding. The autonomous vehicle captures the image (or video frame). The proposed neural network infers the class and location of each crop and weed. Upon identifying a weed, the model also outputs the stem location of the weed, followed by a laser beam cut.
• Figure 5: Overview of semi-supervised learning process. Pseudo labels are first generated for unlabeled data using a teacher model, followed by training a student model with both labeled and pseudo-labeled data. "Conf" represents the confidence score for classification, and "Sim" denotes the cosine similarity between extracted ground-truth weed embeddings and predicted weed embeddings, used to filter out low-quality weed localization. $\tau$ and $\xi$ are hyper-parameters.