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Agronomy
Special Issues
Harnessing Sensing, Artificial Intelligence, and Robotics for Digital Agriculture
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Harnessing Sensing, Artificial Intelligence, and Robotics for Digital Agriculture

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 2806

Special Issue Editors

Dr. Md Sultan Mahmud

E-Mail Website
Guest Editor
1. Department of Plant Pathology, University of Georgia, Athens, GA 30602, USA
2. School of Environmental, Civil, Agricultural, and Mechanical Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA
Interests: computer vision; machine learning; 3D Sensing; LiDAR sensing; automation; agricultural robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Lirong Xiang

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
Interests: agricultural robotics; plant phenotyping; machine vision; digital twin; automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of sensing, artificial intelligence (AI), and robotics into agriculture marks a transformative era in farming practices. Over the past two decades, advancements in these technologies have revolutionized traditional agricultural methods, thus enhancing productivity, sustainability, and efficiency. This journey began with the adoption of basic sensors for soil and crop monitoring, evolving into sophisticated AI-driven systems that are capable of predictive analytics and autonomous decision-making. Robotics has furthered this evolution, introducing automation in planting, harvesting, and maintenance tasks.

This Special Issue aims to explore the latest innovations in and applications of sensing, AI, and robotics in digital agriculture. We seek to provide a comprehensive platform for researchers, practitioners, and policymakers to share insights, challenges, and breakthroughs. The scope of this Special Issue encompasses a wide range of topics, including, but not limited to, precision agriculture, smart farming systems, autonomous machinery, and data-driven agricultural management.

We are particularly interested in cutting-edge research that presents novel methodologies, experimental results, and practical implementations, and welcome the submission of papers addressing interdisciplinary approaches that integrate agronomy, computer science, and engineering. Additionally, we explicitly invite works that propose validation procedures, operational standards for novel technologies, and economic analyses of the transition to digital agriculture. This Special Issue aims to foster a deeper understanding of how these technologies can sustainably transform agriculture, ensuring food security and environmental stewardship for future generations.

Dr. Md Sultan Mahmud
Dr. Lirong Xiang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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

  • smart farming
  • agricultural robotics
  • data-driven agriculture
  • AI-enhanced farming, autonomous machinery
  • crop monitoring
  • IOT in agriculture
  • digital twin

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Published Papers (4 papers)

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Research

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17 pages, 4281 KiB  
Article
Development and Validation of a Discrete Element Simulation Model for Pressing Holes in Sowing Substrates
by Hongmei Xia, Chuheng Deng, Teng Yang, Runxin Huang, Jianhua Ou, Lingjin Dong, Dewen Tao and Long Qi
Agronomy 2025, 15(4), 971; https://doi.org/10.3390/agronomy15040971 - 17 Apr 2025
Viewed by 185
Abstract
To conduct DEM simulation research on the collision characteristics between seeds and pressed substrate holes, a discrete element model of mechanically pressed holes in sowing substrates was developed in this study. The geometric DEM models of sowing substrate particles were established based on [...] Read more.
To conduct DEM simulation research on the collision characteristics between seeds and pressed substrate holes, a discrete element model of mechanically pressed holes in sowing substrates was developed in this study. The geometric DEM models of sowing substrate particles were established based on the sieve test, and the Hertz–Mindlin with JKR contact model was utilized for simulating of the fine, moist, and cohesive substrate particles. The angle of repose measured by the funnel method was served as the target, Plackett–Burman experiments were conducted to screen significant contact mechanical parameters, while steepest ascent and Box–Behnken experiments were employed to define their value ranges. A neural network model for predicting the angle of repose was constructed, and a genetic algorithm was applied to optimize the significant contact mechanical parameters. The cross-sectional profiles of the pressing hole were obtained through image profile feature extraction in simulation and 3D scanning projection methods in the experiment. The calibrated inter-particle dynamic friction coefficient, inter-particle coefficient of restitution, dynamic friction coefficient between particles and stainless steel, and JKR surface energy of the substrate were 0.0349, 0.5448, 0.0233, and 0.4279, respectively. The deviation of the simulated angle of repose utilizing the optimized contact parameters was 0.4°. The shapes of the pressed holes obtained from simulation and experiment showed good consistency. The pressing speed had no significant effect on the mean depth of all sampling points, suggesting that a higher pressing speed should be set to improve the operation efficiency. The pressing depth has a highly significant effect on the mean depth of all sampled points, but no significant effect on the deviation between the simulated and experimental mean depths. The maximum difference in the mean depth deviation between simulated and experimental sampled points is 1.308 mm. It demonstrates that the established discrete element model can efficiently and accurately simulate the deformation of the pressing hole in sowing substrate. It provides an applicable simulation model for fast optimization design of the pressing hole and sowing equipment. Full article
(This article belongs to the Special Issue Harnessing Sensing, Artificial Intelligence, and Robotics for Digital Agriculture)
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19 pages, 19857 KiB  
Article
A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm
by Hongmei Xia, Shicheng Zhu, Teng Yang, Runxin Huang, Jianhua Ou, Lingjin Dong, Dewen Tao and Wenbin Zhen
Agronomy 2025, 15(2), 375; https://doi.org/10.3390/agronomy15020375 - 31 Jan 2025
Viewed by 507
Abstract
To produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining the position of the seed covered by the substrate within individual [...] Read more.
To produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining the position of the seed covered by the substrate within individual plug-tray holes, a novel method for detecting the growth points of plug seedlings has been proposed. It employs an adaptive grayscale processing algorithm based on the differential evolution extra-green algorithm to extract the contour features of seedlings during the early stages of cotyledon emergence. The pixel overlay curve peak points within the binary image of the plug-tray’s background are utilized to delineate the boundaries of the plug-tray holes. Each plug-tray hole containing a single seedling is identified by analyzing the area and perimeter of the seedling’s contour connectivity domains. The midpoint of the shortest line between these domains is designated as the growth point of the individual seedling. For laboratory-grown plug seedlings of tomato, pepper, and Chinese kale, the highest detection accuracy was achieved on the third-, fourth-, and second-days’ post-cotyledon emergence, respectively. The identification rate of missing seedlings and single seedlings exceeded 97.57% and 99.25%, respectively, with a growth-point detection error of less than 0.98 mm. For tomato and broccoli plug seedlings cultivated in a nursery greenhouse three days after cotyledon emergence, the detection accuracy for missing seedlings and single seedlings was greater than 95.78%, with a growth-point detection error of less than 2.06 mm. These results validated the high detection accuracy and broad applicability of the proposed method for various seedling types at the appropriate growth stages. Full article
(This article belongs to the Special Issue Harnessing Sensing, Artificial Intelligence, and Robotics for Digital Agriculture)
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14 pages, 2564 KiB  
Article
Leveraging Zero-Shot Detection Mechanisms to Accelerate Image Annotation for Machine Learning in Wild Blueberry (Vaccinium angustifolium Ait.)
by Connor C. Mullins, Travis J. Esau, Qamar U. Zaman, Chloe L. Toombs and Patrick J. Hennessy
Agronomy 2024, 14(12), 2830; https://doi.org/10.3390/agronomy14122830 - 28 Nov 2024
Cited by 2 | Viewed by 1304
Abstract
This study conducted an analysis of zero-shot detection capabilities using two frameworks, YOLO-World and Grounding DINO, on a selection of images in the wild blueberry (Vaccinium angustifolium Ait.) cropping system. The datasets included ripe wild blueberries, hair fescue (Festuca filiformis Pourr.), [...] Read more.
This study conducted an analysis of zero-shot detection capabilities using two frameworks, YOLO-World and Grounding DINO, on a selection of images in the wild blueberry (Vaccinium angustifolium Ait.) cropping system. The datasets included ripe wild blueberries, hair fescue (Festuca filiformis Pourr.), blueberry buds, and red leaf disease (Exobasidium vaccinii). Key performance metrics such as Intersection over Union (IoU), precision, recall, and F1 score were utilized for model comparison. Grounding DINO consistently achieved superior performance across all metrics and datasets, achieving significantly higher mean IoUs on berries, red leaf, hair fescue, and buds (0.642, 0.921, 0.735, and 0.629, respectively) compared to YOLO-World (0.516, 0.567, 0.232, and 0.408, respectively). Evidenced by their high recall rates relative to precision, the models displayed a preference for identifying true positives at the cost of increasing false positives. Grounding DINO’s higher precision (overall mean of 0.672), despite the tendency to over-detect, indicated a better balance in minimizing false positives than YOLO-World (overall mean of 0.501). These findings contrast with the foundational study of YOLO-World where it demonstrated superior performance on standard datasets, highlighting the importance of dataset characteristics and optimization processes in model performance. The practical implications of this study include providing a solution for accelerated object detection image annotation in the wild blueberry cropping system. This work, representing a significant advancement in facilitating accurate and efficient annotation of wild blueberry datasets, guides future research in the application of zero-shot detection models to agricultural datasets. Full article
(This article belongs to the Special Issue Harnessing Sensing, Artificial Intelligence, and Robotics for Digital Agriculture)
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Review

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21 pages, 14425 KiB  
Review
Progress and Challenges in Research on Key Technologies for Laser Weed Control Robot-to-Target System
by Rui Lu, Daode Zhang, Siqi Wang and Xinyu Hu
Agronomy 2025, 15(5), 1015; https://doi.org/10.3390/agronomy15051015 - 23 Apr 2025
Viewed by 282
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Harnessing Sensing, Artificial Intelligence, and Robotics for Digital Agriculture)
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