Artificial Intelligence and Ecological Integration Drive Weed Control 4.0 for Sustainable Medicinal Crop Production
Abstract Weed infestation is a persistent constraint on the cultivation of medicinal and aromatic crops, reducing yield, quality consistency, raw-material safety, and production standardization. Although herbicide-based weed management has improved labor efficiency, increasing concerns over pesticide residues, herbicide resistance, crop selectivity, and ecological impacts limit its suitability for high-quality herbal medicine production. This review synthesizes the evolution of weed control in medicinal crop systems from manual and mechanical practices to chemical control, integrated weed management, and emerging intelligent–ecological strategies. We propose the concept of Weed Control 4.0 as a crop-specific framework that combines artificial intelligence, intelligent machinery, bioherbicides, and ecological regulation. The review summarizes how microbial and plant-derived bioherbicides suppress weeds through growth inhibition, allelopathic interference, pathogen-mediated damage, and disruption of weed physiological metabolism. It further discusses how machine vision, sensor fusion, robotic platforms, precision spraying, and data-driven decision support can enable real-time weed identification and site-specific intervention. Ecological approaches, including crop rotation, intercropping, mulching, cover crops, allelopathic crops, beneficial insects, and soil microbial manipulation, are evaluated for their potential to suppress weed communities while maintaining soil health and agroecosystem stability. Finally, we identify key challenges for implementation, including medicinal crop-specific weed databases, biosafety and efficacy evaluation of bioherbicides, integration of field machinery with agronomic practices, and residue-safe regulatory standards. Weed Control 4.0 offers a pathway toward sustainable, precise, and residue-safe weed management for the standardized production of high-quality herbal medicines.
Authors
- Binbin Yan (ORCID: https://orcid.org/0000-0002-3339-083X)
- Chuanzhi Kang (ORCID: https://orcid.org/0000-0002-2758-4785)
- Dan Gao (ORCID: https://orcid.org/0000-0001-8930-3056)
- Yifei Pei (ORCID: https://orcid.org/0000-0002-3638-9017)
- Yuan Cui (ORCID: https://orcid.org/0000-0003-2131-4722)
- Lanping Guo (ORCID: https://orcid.org/0000-0001-6974-1411)
- Yan Zhang
- Chongning Lv
- Tielin Wang
- Yiheng Wang
- Chengcai Zhang
- Yuan Liu
Institutions
- Shenyang Pharmaceutical University (CN)
- China Academy of Chinese Medical Sciences (CN)
Publication Details
- Journal
- ACS Agricultural Science & Technology
- Published
- 2026-10-15
- DOI
- https://doi.org/10.1021/acsagscitech.6c00581
- Primary Topic
- Weed Control and Herbicide Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Academy of Chinese Medical Sciences