Progress and Challenges in Research on Key Technologies for Laser Weed Control Robot-to-Target System

The development of precise and sustainable agriculture has made non-chemical, highly selective laser weed control technology a hot research topic. The core of this technology lies in the overall performance of the targeting system, which consists of three key technologies, namely, target identification, dynamic positioning, and precise removal, which are interrelated and jointly determine the overall performance of the weed control system. In this paper, the key technologies of the targeting system are systematically analyzed to clarify the coupling relationship among the technologies and their role in performance optimization. This review systematically compares the mainstream recognition algorithms for the needs of laser weeding for specific parts, reveals the performance bottleneck of the existing algorithms in the laser weeding environment, and points out new research directions, such as developing weed apical growth zone recognition algorithms. The influence of laser beam control technology on weeding accuracy is analyzed, the advantages of vibroseis technology are explored, and the applicability problems of existing vibroseis technology in farmland environments are revealed, such as the shift of irradiation point caused by ground undulation. The key laws of laser parameter optimization are summarized, guiding the optimal design of the system. Through the systematic summary and in-depth analysis of the related research, this review reveals the key challenges facing the development of laser technology. It provides a prospective outlook on the future research direction, aiming to promote the development of laser weed control technology in terms of high efficiency, precision, and intelligence.

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Publication Details

Journal
Agronomy
Published
2025-04-23
DOI
https://doi.org/10.3390/agronomy15051015
Citations
1
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
1.75
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article

Progress and Challenges in Research on Key Technologies for Laser Weed Control Robot-to-Target System

Daode Zhang, Siqi Wang, Xinyu Hu, Lu Rui
1 citations
Agronomy
Smart Agriculture and AI
1.75
article

Progress and Challenges in Research on Key Technologies for Laser Weed Control Robot-to-Target System

Daode Zhang, Siqi Wang, Xinyu Hu, Lu Rui
article en
1 citations

Abstract

The development of precise and sustainable agriculture has made non-chemical, highly selective laser weed control technology a hot research topic. The core of this technology lies in the overall performance of the targeting system, which consists of three key technologies, namely, target identification, dynamic positioning, and precise removal, which are interrelated and jointly determine the overall performance of the weed control system. In this paper, the key technologies of the targeting system are systematically analyzed to clarify the coupling relationship among the technologies and their role in performance optimization. This review systematically compares the mainstream recognition algorithms for the needs of laser weeding for specific parts, reveals the performance bottleneck of the existing algorithms in the laser weeding environment, and points out new research directions, such as developing weed apical growth zone recognition algorithms. The influence of laser beam control technology on weeding accuracy is analyzed, the advantages of vibroseis technology are explored, and the applicability problems of existing vibroseis technology in farmland environments are revealed, such as the shift of irradiation point caused by ground undulation. The key laws of laser parameter optimization are summarized, guiding the optimal design of the system. Through the systematic summary and in-depth analysis of the related research, this review reveals the key challenges facing the development of laser technology. It provides a prospective outlook on the future research direction, aiming to promote the development of laser weed control technology in terms of high efficiency, precision, and intelligence.

AgronomyVol. 15(5)
Hubei Engineering University (CN), Hubei University of Technology (CN)
Openalex Percentile: Top 16%
Smart Agriculture and AI
1.75
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