Integrated Weed Management for Field Crops: Innovations, Integration, and Impact

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Weed Science and Weed Management".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1470

Editors


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Guest Editor
Department of Agriculture, University of Ioannina, 451 10 Ioannina, Greece
Interests: weed biology; weed eco-physiology; herbicide weed control; integrated weed management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Agricultural Development, Democritus University of Thrace, Orestiada, Greece
Interests: symbiotic nitrogen fixation; cultivation techniques; management of field crops; crop growth and development; new and alternative cropsweed management and weed-crop interactions; nutrition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Herbicide resistance, climate variability, and tightening environmental regulations are forcing a paradigm shift from single-tactic weed control to diversified, knowledge-intensive systems. This Special Issue will compile cutting-edge research, reviews, and case studies that advance the science and practical adoption of Integrated Weed Management (IWM) across the world’s major field crops. We welcome manuscripts that quantify synergies among IWM components, evaluate economic and ecological outcomes, and translate innovations into on-farm decision tools.

Scope & Topic Themes (authors are invited to submit under, but not limited to, the following themes).

  1. Weed Ecology and Population Dynamics in Diversified Cropping Systems
    • Seed-bank persistence, dormancy cycling, and emergence modelling
    • Role of climate change (CO2, temperature, rainfall patterns) on weed–crop interactions
  1. Preventive & Cultural Tactics for Weed Suppression
    • Crop rotation design: sequence length, functional groups, break-crop effects
    • Cover-crop species mixtures, termination strategies, and allelopathic mechanisms
    • Row spacing, seeding rate, and cultivar competitiveness trials
  1. Mechanical, Physical, and Robotic Weed Control
    • Precision inter-row cultivation tools: design, soil-engaging geometry, draft forces, robotic intra-row weeders, vision-guided hoes, finger weeders, torsion knives and autonomous steering algorithms
    • Thermal weed control (flaming, steam, infrared, laser) efficacy and energy balance
    • Autonomous robotic platforms: machine-vision algorithms, weed-vs-crop classification accuracy, field speeds, and cost: benefit analyses
  1. Targeted Chemical Management and Herbicide Stewardship
    • Site-specific sprayer technologies (sensor boom, drone spot-spray, AI prescription maps)
    • Low-dose programs, adjuvant innovations, and drift-mitigation nozzles
    • Herbicide mixture and rotation optimization for resistance prevention
  1. Biological Control and Bio-Herbicides
    • Biocontrol agents for invasive weeds in field-crop landscapes
    • Microbial bio-herbicides: formulation, shelf life, and environmental fate
    • Allelopathic crop residues and natural-product herbicides
    • Alternative bioherbicidal products
  1. Harvest Weed Seed Control (HWSC) and Chaff Management
    • Impact mills, chaff decks, chaff lining, bale-direct systems: efficacy on key species
    • Integration with no-till and residue-retention goals
  1. Herbicide-Resistance Diagnostics and Mitigation
    • Rapid molecular assays (qPCR, KASP markers, next-gen sequencing)
    • Regional resistance-mapping networks and predictive modelling
    • “Double-knock” and “triple-knock” strategies: field performance and economics
  1. Decision-Support Systems, Modelling, and Digital Agriculture
    • Process-based models (WEEDSIM, RIM, APSIM-Weed) calibration and validation
    • Smartphone apps, cloud-based scouting platforms, and AI image recognition accuracy
    • Economic optimizers and Monte-Carlo risk analysis for IWM portfolios
  1. Soil Health, Greenhouse-Gas Footprint, and Ecosystem Services
    • Trade-offs between tillage-based weed control and soil carbon sequestration
    • Herbicide fate under reduced-till scenarios: leaching, runoff, microbial degradation
    • Pollinator and non-target impacts of diversified IWM programs
  1. Socio-Economic, Policy, and Adoption Studies
    • Cost–benefit analyses, partial budgets, whole-farm profit comparisons
    • Role of crop-insurance incentives, carbon credits, and sustainability certification
    • Extension methodology: on-farm demonstrations, peer-to-peer learning, social-media campaigns
  1. Crop-Specific Case Studies
    • Maize, soybean, wheat, rice, cotton, canola, sugarcane, sorghum, pulses, etc.
    • Quantified integration of at least three IWM components with yield, weed control, and economic endpoints
  1. Emerging Technologies and Future Scenarios
    • Gene-editing weeds or cover crops for enhanced suppression traits
    • RNAi and spray-induced gene silencing for species-specific control
    • Integration with autonomous farm machinery fleets and 5G connectivity
  1. Precision Weed Management
    • Mapping and Sensing
    • Site Specific Chemical Applications (boom-mounted, drone-based, and robotic spot-spray systems etc.)
    • AI prescription maps (herbicide rate optimization per weed patch and growth stage etc.)

Manuscript Types

Original research articles, systematic reviews, meta-analyses, short communications, and opinion papers.

Dr. Nicholas Emmanuel Korres
Prof. Dr. Spyridon D. Koutroubas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • integrated weed management
  • precision weed control
  • herbicide resistance mitigation
  • weed ecology
  • sustainable agriculture

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Published Papers (1 paper)

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Research

21 pages, 21631 KB  
Article
YOLO-CornSeg: A Lightweight Segmentation Model for Corn Seedlings with an Indirect Weed Detection Strategy
by Jinglin Lei, Jialin Yu, Kang Han, Mian Li, Xiaojun Jin and Honglian Yin
Agronomy 2026, 16(11), 1091; https://doi.org/10.3390/agronomy16111091 - 31 May 2026
Viewed by 411
Abstract
Weed control is crucial for optimizing corn yield. In recent years, advances in computer vision and deep learning have created new opportunities for precision agriculture. However, annotating weed datasets is typically time-consuming, labor-intensive, and costly. To address this challenge, this study proposes an [...] Read more.
Weed control is crucial for optimizing corn yield. In recent years, advances in computer vision and deep learning have created new opportunities for precision agriculture. However, annotating weed datasets is typically time-consuming, labor-intensive, and costly. To address this challenge, this study proposes an indirect weed detection strategy that reduces reliance on explicit weed annotations by focusing on accurate crop segmentation. Specifically, we develop YOLO-CornSeg, a lightweight segmentation model based on an improved YOLOv8n architecture, designed for precise corn seedling segmentation. The model incorporates a C2f_DWR module to enhance multi-scale feature extraction and a Segment_Efficient head to improve segmentation performance while maintaining computational efficiency. Based on the resulting segmentation masks, an indirect weed detection strategy is applied, in which non-crop green regions are identified as weeds using HSV-based image processing. Experimental results show that YOLO-CornSeg achieves a mean Intersection over Union (mIoU) of 91.1% with a model size of 8.3 MB, outperforming several state-of-the-art two-stage semantic segmentation models while maintaining low computational complexity and a compact model size. The improved segmentation accuracy further enhances the reliability of downstream weed inference. Overall, this study highlights the potential of combining lightweight crop segmentation with indirect weed detection strategies to support precision herbicide application. Full article
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