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

2025-02-10

AI Summary

To address the lack of precise weed stem localization in laser-based weeding and the trade-off between crop protection and targeting accuracy in existing methods, this paper introduces the first dedicated weed stem detection task for laser weeding. We construct the first high-quality field-scale weed stem detection dataset, comprising 7,161 images with 11,151 annotated instances. Furthermore, we propose an end-to-end multi-task joint model that simultaneously performs crop/weed classification and localizes stem key points and bounding boxes. Built upon a deep learning object detection framework, the model enables root-level precision targeting under realistic field conditions. Experimental results demonstrate that our approach improves weeding accuracy by 6.7% and reduces laser energy consumption by 32.3% compared to state-of-the-art methods, thereby significantly enhancing targeting specificity and ecological sustainability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Object Detection & Categorization
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Enhancing laser weeding precision
• Reducing energy consumption in agriculture
• Developing dataset for weed stem detection

Innovation

• Laser beam targets weed roots
• End-to-end weed stem detection system
• High-resolution dataset improves weeding accuracy