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Agronomy
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Next-Generation Crop Management: Bridging AI Vision and Sensor Fusion for...
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Next-Generation Crop Management: Bridging AI Vision and Sensor Fusion for Smarter Agronomy

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 15 March 2027 | Viewed by 1642

Special Issue Editors

Dr. Xiaojun Jin

E-Mail Website
Guest Editor
National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence; machine vision; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Kaixiang Zhang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824, USA
Interests: robotics; agricultural robots; automation and control systems; visual servoing; detection and localization

Special Issue Information

Dear Colleagues,

As agriculture enters a new era of data-driven decision-making, the integration of artificial intelligence (AI)-powered vision systems and advanced sensor fusion is poised to transform crop management. With heightened challenges related to climate variability, resource limitations, and global food security, there is an urgent need to develop smarter, more adaptive solutions for sustainable agronomy.

This Special Issue focuses on innovative approaches that harness the synergy between AI-based computer vision and multisensor data fusion—including thermal, hyperspectral, LiDAR, and IoT-based environmental sensing—to enable precise, efficient, and intelligent crop monitoring and management. We welcome research that leverages these technologies to enhance phenotyping, stress detection, irrigation control, nutrient management, yield prediction, and post-harvest processing.

We encourage the submission of original research articles, comprehensive reviews, and technical communications that present novel methodologies, field validations, or integrated platforms combining AI and sensor technologies for next-generation precision agriculture.

By fostering interdisciplinary collaboration between AI, agronomy, and sensor engineering, this Special Issue aims to advance the scientific foundation and practical implementation of climate-smart and resource-efficient farming systems.

We look forward to receiving contributions that push the boundaries of innovation in intelligent, sensor-integrated, and sustainable crop production.

Dr. Xiaojun Jin
Dr. Kaixiang Zhang
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-blind 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

  • AI-powered vision systems
  • sensor fusion
  • precision agriculture
  • multimodal crop monitoring
  • smart irrigation and fertilization
  • deep learning for plant phenotyping
  • thermal and hyperspectral sensing
  • field robotics and UAV-based sensing
  • climate-smart agronomy
  • agricultural intelligence systems

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Published Papers (2 papers)

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16 pages, 2473 KB  
Article
Incorporating Crop-Centric Segmentation and Enhanced YOLOv10 for Indirect Weed Detection in Bok Choy Fields
by Weili Li, Wenpeng Zhu, Qianyu Wang, Feng Gao, Kang Han and Xiaojun Jin
Agronomy 2026, 16(9), 907; https://doi.org/10.3390/agronomy16090907 - 30 Apr 2026
Viewed by 269
Abstract
Weed infestation poses a significant threat to bok choy (Brassica rapa subsp. chinensis) cultivation, reducing crop yield and quality through resource competition and pest facilitation. Traditional weed detection methods face two major bottlenecks: one is data annotation, arising from the need for [...] Read more.
Weed infestation poses a significant threat to bok choy (Brassica rapa subsp. chinensis) cultivation, reducing crop yield and quality through resource competition and pest facilitation. Traditional weed detection methods face two major bottlenecks: one is data annotation, arising from the need for extensive, species-diverse datasets, and the other is visual discrimination, due to the high morphological similarity between crops and weeds at certain growth stages. To address these challenges, this study proposed an indirect weed detection framework that combines an optimized You Only Look Once version 10 (YOLOv10) model for crop detection with Excess Green ExG-based segmentation of residual vegetation. The model incorporates RFD and C2f-WDBB modules to improve feature preservation and multi-scale fusion. Compared with baseline YOLOv10, the final proposed RCW-YOLOv10 reduced the number of parameters by 1.04 million and improved detection performance, achieving increases of 3.5%, 1.5%, and 1.1% percentage points in Precision, Recall, and mAP50, respectively, under field conditions. The system initially detected bok choy plants, subsequently localizing weeds by masking crop regions and thresholding residual ExG signals in the uncovered areas. The detected weed coordinates were used to construct a distribution map that may support targeted control in precision agriculture. This approach simplifies weed identification under the tested bok choy field conditions and may be adaptable to other crops after further validation. Full article
(This article belongs to the Special Issue Next-Generation Crop Management: Bridging AI Vision and Sensor Fusion for Smarter Agronomy)
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19 pages, 5721 KB  
Article
Efficient Weed Detection in Cabbage Fields Using a Dual-Model Strategy
by Mian Li, Wenpeng Zhu, Xiaoyue Zhang, Ying Jiang, Jialin Yu, Aimin Li and Xiaojun Jin
Agronomy 2026, 16(1), 93; https://doi.org/10.3390/agronomy16010093 - 29 Dec 2025
Viewed by 698
Abstract
Accurate weed detection in crop fields remains a challenging task due to the diversity of weed species and their visual similarity to crops, especially under natural field conditions where lighting and occlusion vary. Traditional methods typically attempt to directly identify various weed species, [...] Read more.
Accurate weed detection in crop fields remains a challenging task due to the diversity of weed species and their visual similarity to crops, especially under natural field conditions where lighting and occlusion vary. Traditional methods typically attempt to directly identify various weed species, which demand large-scale, finely annotated datasets and often suffer from low generalization. To address these challenges, this study proposes a novel dual-model framework that simplifies the task by dividing it into two tractable stages. First, a crop segmentation network is used to identify and remove cabbage (Brassica oleracea L. ssp. pekinensis) regions from field images. Since crop categories are visually consistent and singular, this stage achieves high precision with relatively low complexity. The remaining non-crop areas, which contain only weeds and background, are then subdivided into grid cells. Each cell is classified by a second lightweight classification network as either background, broadleaf weeds, or grass weeds. The classification model achieved F1 scores of 95.1%, 91.1%, and 92.2% for background, broadleaf weeds, and grass weeds, respectively. This two-stage approach transforms a complex multi-class detection task into simpler, more manageable subtasks, improving detection accuracy while reducing annotation burden and enhancing robustness under the tested field conditions. Full article
(This article belongs to the Special Issue Next-Generation Crop Management: Bridging AI Vision and Sensor Fusion for Smarter Agronomy)
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