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Agronomy
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Robotics and Automation in Farming
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Robotics and Automation in Farming

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

Deadline for manuscript submissions: closed (15 May 2025) | Viewed by 9565

Special Issue Editors

Dr. Marco Fontanelli

E-Mail Website
Guest Editor
Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto, 80, 56124 Pisa, Italy
Interests: farm mechanization and farm machinery; precision agriculture; conservation agriculture; nonchemical weed control; machine for turfgrass and landscape management
Special Issues, Collections and Topics in MDPI journals
Dr. Lorenzo Gagliardi

E-Mail Website
Guest Editor Assistant
Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
Interests: assessment and implementation of sustainable weed control strategies within conservation biological farming; viticulture sector

Special Issue Information

Dear Colleagues, 

With the rapid growth of the global population and the increasing demand for food, it has become imperative to adopt technological advancements to improve food production, efficiency, and sustainability. The development of robotics and automation in farming is currently at the forefront of significant research and innovation worldwide.
Applications in this field vary, with an emerging focus on robotic systems capable of selectively harvesting crops, controlling pests, diseases, and weeds, monitoring the agricultural environment and crops, autonomously supporting agricultural logistics operations, and accelerating crop selection or phenotyping. Each of these applications can bring benefits to all agricultural sectors, including arable crops, horticulture, orchards and vineyards, landscape and urban green areas, and the livestock industry.

In this Special Issue, we welcome contributions on innovative technologies, including precision and digital farming technologies, advanced robotic systems, and the use of automation in agriculture aimed at increasing the productivity, efficiency, and sustainability of farming systems. Submissions that explore the integration of these technologies into existing farming practices, the associated economic and environmental impacts, and case studies demonstrating successful implementation are particularly encouraged.

We invite experts and researchers to submit original research, reviews, and opinion pieces addressing the themes of this Special Issue.

Dr. Marco Fontanelli
Guest Editor

Dr. Lorenzo Gagliardi
Guest Editor Assistant

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

  • agricultural robotics
  • automation
  • smart farming
  • precision agriculture

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Published Papers (6 papers)

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Research

20 pages, 6453 KiB  
Article
A Robust Tomato Counting Framework for Greenhouse Inspection Robots Using YOLOv8 and Inter-Frame Prediction
by Wanli Zheng, Guanglin Dai, Miao Hu and Pengbo Wang
Agronomy 2025, 15(5), 1135; https://doi.org/10.3390/agronomy15051135 - 6 May 2025
Viewed by 249
Abstract
Accurate tomato yield estimation and ripeness monitoring are critical for optimizing greenhouse management. While manual counting remains labor-intensive and error-prone, this study introduces a novel vision-based framework for automated tomato counting in standardized greenhouse environments. The proposed method integrates YOLOv8-based detection, depth filtering, [...] Read more.
Accurate tomato yield estimation and ripeness monitoring are critical for optimizing greenhouse management. While manual counting remains labor-intensive and error-prone, this study introduces a novel vision-based framework for automated tomato counting in standardized greenhouse environments. The proposed method integrates YOLOv8-based detection, depth filtering, and an inter-frame prediction algorithm to address key challenges such as background interference, occlusion, and double-counting. Our approach achieves 97.09% accuracy in tomato cluster detection, with mature and immature single fruit recognition accuracies of 92.03% and 91.79%, respectively. The multi-target tracking algorithm demonstrates a MOTA (Multiple Object Tracking Accuracy) of 0.954, outperforming conventional methods like YOLOv8 + DeepSORT. By fusing odometry data from an inspection robot, this lightweight solution enables real-time yield estimation and maturity classification, offering practical value for precision agriculture. Full article
(This article belongs to the Special Issue Robotics and Automation in Farming)
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25 pages, 8579 KiB  
Article
HPS-RRT*: An Improved Path Planning Algorithm for a Nonholonomic Orchard Robot in Unstructured Environments
by Meiqi Hu, Qinpeng Huang, Jiamin Cai, Yu Chen, Jun Li and Linlin Shi
Agronomy 2025, 15(3), 712; https://doi.org/10.3390/agronomy15030712 - 14 Mar 2025
Viewed by 607
Abstract
Path planning is a fundamental challenge for autonomous robots, particularly in unstructured environments, where issues such as low search efficiency, suboptimal path quality, and local optima often arise. To address these challenges and enable a nonholonomic orchard robot to accomplish tasks safely and [...] Read more.
Path planning is a fundamental challenge for autonomous robots, particularly in unstructured environments, where issues such as low search efficiency, suboptimal path quality, and local optima often arise. To address these challenges and enable a nonholonomic orchard robot to accomplish tasks safely and efficiently, this paper proposes a novel HPS-RRT* algorithm based on hybrid exploration and optimization mechanisms to enhance path planning performance. A hybrid sampling strategy adapted to the environmental characteristics is proposed to improve the search efficiency, and an extended step size based on Lévy distribution is designed to balance exploration and optimization. Moreover, a pruning strategy is incorporated to reduce redundant points during the search process, enhancing the efficiency of the exploration tree and reducing unnecessary expansion. Furthermore, a novel leader-based sparrow optimization algorithm is proposed to ensure that the planned path is suitable for the nonholonomic orchard robot. It can overcome the limitations of traditional smoothing methods by simultaneously optimizing curvature and path length. Compared with existing RRT*-based algorithms in environments of varying complexity, the proposed HPS-RRT* reduces the final path length by 1.7% to 27%, improves planning efficiency by 77.7% to 93.3%, and enhances path smoothness by 27.9% to 41.7%, while maintaining a 100% success rate. Furthermore, its feasibility for a nonholonomic orchard robot is validated through a multi-target planning task with curvature constraints. Full article
(This article belongs to the Special Issue Robotics and Automation in Farming)
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22 pages, 9277 KiB  
Article
LRNTRM-YOLO: Research on Real-Time Recognition of Non-Tobacco-Related Materials
by Chunjie Zhang, Lijun Yun, Chenggui Yang, Zaiqing Chen and Feiyan Cheng
Agronomy 2025, 15(2), 489; https://doi.org/10.3390/agronomy15020489 - 18 Feb 2025
Cited by 2 | Viewed by 683
Abstract
The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related [...] Read more.
The presence of non-tobacco-related materials can significantly compromise the quality of tobacco. To accurately detect non-tobacco-related materials, this study introduces a lightweight and real-time detection model derived from the YOLOv11 framework, named LRNTRM-YOLO. Initially, due to the sub-optimal accuracy in detecting diminutive non-tobacco-related materials, the model was augmented by incorporating an additional layer dedicated to enhancing the detection of small targets, thereby improving the overall accuracy. Furthermore, an attention mechanism was incorporated into the backbone network to focus on the features of the detection targets, thereby improving the detection efficacy of the model. Simultaneously, for the introduction of the SIoU loss function, the angular vector between the bounding box regressions was utilized to define the loss function, thus improving the training efficiency of the model. Following these enhancements, a channel pruning technique was employed to streamline the network, which not only reduced the parameter count but also expedited the inference process, yielding a more compact model for non-tobacco-related material detection. The experimental results on the NTRM dataset indicate that the LRNTRM-YOLO model achieved a mean average precision (mAP) of 92.9%, surpassing the baseline model by a margin of 4.8%. Additionally, there was a 68.3% reduction in the parameters and a 15.9% decrease in floating-point operations compared to the baseline model. Comparative analysis with prominent models confirmed the superiority of the proposed model in terms of its lightweight architecture, high accuracy, and real-time capabilities, thereby offering an innovative and practical solution for detecting non-tobacco-related materials in the future. Full article
(This article belongs to the Special Issue Robotics and Automation in Farming)
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19 pages, 3615 KiB  
Article
Analysis of Football Pitch Performances Based on Different Cutting Systems: From Visual Evaluation to YOLOv8
by Sofia Matilde Luglio, Christian Frasconi, Lorenzo Gagliardi, Michele Raffaelli, Andrea Peruzzi, Marco Volterrani, Simone Magni and Marco Fontanelli
Agronomy 2024, 14(11), 2645; https://doi.org/10.3390/agronomy14112645 - 10 Nov 2024
Cited by 1 | Viewed by 1313
Abstract
The quality of sports facilities, especially football pitches, has gained significant attention due to the growing importance of sports globally. This study examines the effect of two different cutting systems, a traditional ride-on mower and an autonomous mower, on the quality and functional [...] Read more.
The quality of sports facilities, especially football pitches, has gained significant attention due to the growing importance of sports globally. This study examines the effect of two different cutting systems, a traditional ride-on mower and an autonomous mower, on the quality and functional parameters of a municipal football field. The analysis includes visual assessments, measurements of grass height, and evaluations of surface hardness, comparing the performance of the two cutting systems. Additionally, studies of turfgrass composition and machine learning techniques, particularly with YOLOv8s and YOLOv8n, are conducted to test the capability of assessing weed and turfgrass species distribution. The results indicate significant differences in grass color based on the position (5.36 in the corners and 3.69 in the central area) and surface hardness between areas managed with a traditional ride-on mower (15.25 Gmax) and an autonomous mower (10.15 Gmax) in the central region. Higher height values are recorded in the area managed with the ride-on mower (2.94 cm) than with the autonomous mower (2.61 cm). Weed presence varies significantly between the two cutting systems, with the autonomous mower demonstrating higher weed coverage in the corners (17.5%). Higher overall performance metrics were obtained through YOLOv8s. This study underscores the importance of innovative management practices and monitoring techniques in optimizing the quality and playability of a football field while minimizing environmental impact and management efforts. Full article
(This article belongs to the Special Issue Robotics and Automation in Farming)
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19 pages, 6552 KiB  
Article
Map Construction and Positioning Method for LiDAR SLAM-Based Navigation of an Agricultural Field Inspection Robot
by Jiwei Qu, Zhinuo Qiu, Lanyu Li, Kangquan Guo and Dan Li
Agronomy 2024, 14(10), 2365; https://doi.org/10.3390/agronomy14102365 - 13 Oct 2024
Cited by 3 | Viewed by 3234
Abstract
In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. [...] Read more.
In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot’s positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings. Full article
(This article belongs to the Special Issue Robotics and Automation in Farming)
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24 pages, 10818 KiB  
Article
ADL-YOLOv8: A Field Crop Weed Detection Model Based on Improved YOLOv8
by Zhiyu Jia, Ming Zhang, Chang Yuan, Qinghua Liu, Hongrui Liu, Xiulin Qiu, Weiguo Zhao and Jinlong Shi
Agronomy 2024, 14(10), 2355; https://doi.org/10.3390/agronomy14102355 - 12 Oct 2024
Cited by 4 | Viewed by 2336
Abstract
This study presents an improved weed detection model, ADL-YOLOv8, designed to enhance detection accuracy for small targets while achieving model lightweighting. It addresses the challenge of attaining both high accuracy and low memory usage in current intelligent weeding equipment. By overcoming this issue, [...] Read more.
This study presents an improved weed detection model, ADL-YOLOv8, designed to enhance detection accuracy for small targets while achieving model lightweighting. It addresses the challenge of attaining both high accuracy and low memory usage in current intelligent weeding equipment. By overcoming this issue, the research not only reduces the hardware costs of automated impurity removal equipment but also enhances software recognition accuracy, contributing to reduced pesticide use and the promotion of sustainable agriculture. The ADL-YOLOv8 model incorporates a lighter AKConv network for better processing of specific features, an ultra-lightweight DySample upsampling module to improve accuracy and efficiency, and the LSKA-Attention mechanism for enhanced detection, particularly of small targets. On the same dataset, ADL-YOLOv8 demonstrated a 2.2% increase in precision, a 2.45% rise in recall, a 3.07% boost in mAP@0.5, and a 1.9% enhancement in mAP@0.95. The model’s size was cut by 15.77%, and its computational complexity was reduced by 10.98%. These findings indicate that ADL-YOLOv8 not only exceeds the original YOLOv8n model but also surpasses the newer YOLOv9t and YOLOv10n in overall performance. The improved algorithm model makes the hardware cost required for embedded terminals lower. Full article
(This article belongs to the Special Issue Robotics and Automation in Farming)
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