Evidence map›Paper›PMID 42197735›Full record

ReviewSensors (Basel, Switzerland)2026

A Review of Robotic Weeding Modalities for Site-Specific Weed Management.

Feng Gao, Shugui Ding, Wenpeng Zhu, Kang Han, Bin Wu, Maocheng Zhao, Zhong Li, Xiaojun Jin

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Feng GaoAgricultural and Forestry Products Deep Processing Technology and Equipment Engineering Center of Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China.ORCID 0009-0009-2552-2212
Shugui DingNanjing Lehui Finnah Packtec Co., Ltd., Nanjing 211151, China.
Wenpeng ZhuNational Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China.
Kang HanCenter for Agricultural Robotics and Automation, Jurong Institute of Smart Agriculture, Zhenjiang 212441, China.
Bin WuCollege of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Maocheng ZhaoNational Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China.
Zhong LiAgricultural and Forestry Products Deep Processing Technology and Equipment Engineering Center of Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China.
Xiaojun JinAgricultural and Forestry Products Deep Processing Technology and Equipment Engineering Center of Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China.ORCID 0000-0001-5087-6877

Funding

Weifang Science and Technology Development Plan Project 2024ZJ1097
6 · The paper itself

Abstract

Weed control remains a critical challenge in modern crop production, particularly under increasing pressure to reduce chemical inputs and improve environmental sustainability. Recent advances in precision agriculture and robotic systems have enabled site-specific weed management, where interventions are applied selectively based on detected weed locations. While extensive research has focused on improving weed detection algorithms, comparatively less attention has been paid to the characteristics and constraints of different weeding modalities, which ultimately determine field performance. This review presents a systematic analysis of robotic weeding modalities from an actuation-oriented perspective. Specifically, we establish a comprehensive taxonomy of weeding approaches, including mechanical, chemical, thermal, laser-based, electrical, and other emerging methods, and analyze their underlying mechanisms and operational characteristics. Furthermore, we examine the coupling between sensing and actuation, highlighting how different intervention modalities impose distinct requirements on perception outputs. A scenario-based comparison framework is then developed to evaluate the suitability of different modalities across representative agricultural conditions, including pre-emergence control, in-row selective weeding, dense-row crop systems, and large weed situations. Based on this analysis, the limitations of single-modality systems are discussed, and emerging trends toward multi-modality integration and air-ground collaborative weed management are reviewed. Overall, this review shifts the focus from detection-centric approaches to the integration of sensing and actuation in robotic weeding systems and provides a decision-oriented framework to support the design, selection, and deployment of next-generation robotic weed management technologies.

Indexed as

AgriculturePlant WeedsRoboticsWeed ControlCrops, Agriculturalprecision agriculturerobotic weedingsensing–actuation couplingsite-specific weed managementweeding modalities

Identifiers

PMID42197735
PMCPMC13210440

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.