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

• URL: http://arxiv.org/abs/2502.06255v1
• Date: Mon, 10 Feb 2025 08:42:46 GMT
• Title: Towards Efficient and Intelligent Laser Weeding: Method and Dataset for Weed Stem Detection
• Authors: Dingning Liu, Jinzhe Li, Haoyang Su, Bei Cui, Zhihui Wang, Qingbo Yuan, Wanli Ouyang, Nanqing Dong,
• Abstract summary: This study is the first empirical investigation of weed recognition for laser weeding. We integrate the detection of crop and weed with the localization of weed stem into one end-to-end system. The proposed system improves weeding accuracy by 6.7% and reduces energy cost by 32.3% compared to existing weed recognition systems.
• Score: 51.65457287518379
• License:
• 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.

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