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AgriEngineering
Special Issues
Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering
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Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering

A Special Issue of AgriEngineering (ISSN 2624-7402) belonging to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 4714

Editors

Dr. Mahdi Vahdanjoo

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
Interests: robotics; route planning; precision agriculture; machine learning; optimization; simulation
Prof. Dr. Claus Grøn Sørensen

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
Interests: operations research; route planning; logistics; supply chain management; system engineering; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo Ferro

E-Mail Website
Guest Editor
Department of Agricultural, Food, and Forest Sciences (SAAF), Viale delle Scienze, Building 4, “H” Entry, 90128 Palermo, Italy
Interests: precision and digital farming; vineyard spatial variability monitoring; proximal and remote sensing; agricultural engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing integration of robotics/unmanned aerial vehicles (UAVs) and computer vision technologies is reshaping the landscape of modern agricultural engineering. These innovations are driving the transition toward intelligent, data-driven, and sustainable farming systems capable of addressing global challenges such as labor shortages, environmental sustainability, and food security. Robotics and UAVs equipped with advanced sensors and vision-based algorithms enable precise field operations, continuous crop monitoring, and real-time decision-making. This Special Issue aims to highlight cutting-edge research and practical advancements that explore how these technologies contribute to improving efficiency, productivity, and sustainability across all stages of agricultural production.

Research Areas

This Special Issue welcomes original research articles, reviews, and technical papers focusing on (but not limited to) the following areas:

  • Autonomous Agricultural Robots and UAVs.
  • Computer Vision and Artificial Intelligence in Agriculture.
  • UAV-Based Remote Sensing and Mapping.
  • Coverage Path Planning and Cooperative Navigation.
  • Multi-Sensor Fusion and Perception Systems.
  • Automation of Agricultural Operations.
  • AI-Driven Decision Support Systems.
  • Simulation and Digital Twin Technologies.
  • Sustainability, Safety, and Environmental Assessment.

Dr. Mahdi Vahdanjoo
Prof. Dr. Claus Grøn Sørensen
Dr. Massimo Ferro
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. AgriEngineering 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 1800 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
  • unmanned aerial vehicles (UAVS)
  • precision agriculture
  • computer vision
  • artificial intelligence (AI)
  • machine/deep learning
  • field automation
  • autonomous navigation
  • coverage path planning
  • sensor fusion
  • remote sensing
  • crop monitoring
  • digital twin
  • smart farming
  • decision support systems
  • sustainable agriculture

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

15 pages, 1008 KB  
Communication
Assessing Olive Diseases in Albania for UAV- and AI-Based Monitoring
by Genta Rexha, Erion Papalilo, Arbri Jesku, Aleksandër Biberaj and Elson Agastra
AgriEngineering 2026, 8(8), 346; https://doi.org/10.3390/agriengineering8080346 - 18 Aug 2026
Viewed by 314
Abstract
Olive cultivation is an important agricultural sector in Albania, where disease monitoring remains challenging due to fragmented orchards, heterogeneous management practices, and limited adoption of advanced sensing technologies. Recent studies have shown the potential of unmanned aerial vehicles (UAVs), sensor systems, and artificial [...] Read more.
Olive cultivation is an important agricultural sector in Albania, where disease monitoring remains challenging due to fragmented orchards, heterogeneous management practices, and limited adoption of advanced sensing technologies. Recent studies have shown the potential of unmanned aerial vehicles (UAVs), sensor systems, and artificial intelligence (AI) for monitoring specific olive diseases in different olive-growing regions. However, the suitability of olive diseases reported in Albania for monitoring with UAVs and AI has not yet been systematically assessed. This paper examines the main olive diseases relevant to Albania using a semi-quantitative, literature-based multicriteria framework in which five monitoring criteria are scored from 1 to 3 and combined using equal weights. It also formalizes a UAV-first screening workflow that links image acquisition, AI-based canopy segmentation, feature extraction, anomaly scoring, decision thresholds, and targeted field or laboratory confirmation. Based on this assessment, the study identifies the most promising disease targets for future research and outlines key considerations for sensor selection and validation. The paper provides a context-specific foundation for future UAV- and AI-supported disease monitoring in Albanian olive groves. The revised analysis also distinguishes indicative acquisition targets from experimentally validated detection limits and specifies practical requirements for ground truth, radiometric calibration, dataset design, and geospatial validation. Full article
(This article belongs to the Special Issue Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering)
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21 pages, 15386 KB  
Article
YieldNet: A Lightweight YOLOv8n Enhancement for Immature Green Tomato Detection in UAV Images: Real-Time Edge Demonstration Toward Pre-Harvest Yield Estimation
by Chenyu Yu, Lu Li and Bolin Huang
AgriEngineering 2026, 8(8), 311; https://doi.org/10.3390/agriengineering8080311 - 28 Jul 2026
Viewed by 321
Abstract
Accurate early-stage monitoring of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically highly similar to foliage, making detection from low-altitude UAV imagery extremely [...] Read more.
Accurate early-stage monitoring of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically highly similar to foliage, making detection from low-altitude UAV imagery extremely difficult, while onboard edge processors impose stringent power and weight constraints. To address this, we propose YieldNet, a lightweight framework that introduces targeted enhancements to vanilla YOLOv8n: the backbone is replaced with ShuffleNetV2 to reduce computation; Efficient Channel Attention (ECA) modules are embedded after the P3–P5 layers in the neck for channel recalibration; and PIoU v2 loss is adopted for bounding-box regression. This study focuses on immature green tomatoes in low-altitude UAV imagery and evaluates the detector on an RK3588 edge device. The model is evaluated on both a self-collected real-world UAV dataset comprising 600 low-altitude green-tomato images and a public multi-ripeness benchmark. Compared with the YOLOv8n baseline, YieldNet achieves relative improvements in mAP@50-95, Recall, and F1-score by 18.9%, 6.1%, and 5.8%, respectively, on the Tomato-Recog public validation set, and enhances Recall, F1-score, and Precision by relative gains of 4.3%, 4.0%, and 3.8%, respectively, on the GreenTomato-UAV validation set, while increasing parameters only from 3.0 M to 3.3 M and reducing FLOPs from 8.1 G to 8.0 G. A representative live camera-to-display reading on the Orange Pi 5 Max was 37.6 FPS with 86 ms end-to-end latency. YieldNet supplies countable detections for future pre-harvest yield-estimation studies; UAV flight deployment, fruit-size estimation, and harvest-weight validation were not evaluated. Full article
(This article belongs to the Special Issue Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering)
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22 pages, 1371 KB  
Article
Assessment of Autonomous Aerial and Ground Vehicles in Comparison to Conventional Tractor-Mounted Spraying Systems in Terms of Energy Efficiency, Economic Viability, and Environmental Impact in Orchard Spraying
by Michail Semenišin, Tadas Jomantas, Aurelija Kemzūraitė, Dainius Savickas, Albinas Andriušis and Dainius Steponavičius
AgriEngineering 2026, 8(6), 246; https://doi.org/10.3390/agriengineering8060246 - 14 Jun 2026
Viewed by 682
Abstract
Perennial crop systems (e.g., orchards) require frequent spraying with plant protection products. Equipment plays a crucial role in assessing energy efficiency, productivity, economic performance, and the environmental impact of orchard production. In recent years some farmers have replaced conventional tractor-mounted air-blast sprayers (TMABS) [...] Read more.
Perennial crop systems (e.g., orchards) require frequent spraying with plant protection products. Equipment plays a crucial role in assessing energy efficiency, productivity, economic performance, and the environmental impact of orchard production. In recent years some farmers have replaced conventional tractor-mounted air-blast sprayers (TMABS) and switched to unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs). However, there has been a lack of comparative studies on the energy and environmental assessment of these systems. This study aimed to evaluate the overall viability of different orchard spraying technologies in terms of energy efficiency, economic costs, and environmental impact. A life cycle assessment (LCA) of five sprayers was performed: a TMABS, a UGV, and three UAVs. The CML-IA methodology and SimaPro 9.5 software with the Ecoinvent v3 database were used to determine the environmental impact of the compared machines. Energy efficiency was calculated using fuel consumption data, human labor energy, and the energy embodied in the machinery. Economic viability was evaluated through capital depreciation, labor, energy consumption, consumable and maintenance cost per hectare calculation models. The results indicate that UAV systems, as compared to TMABS, can significantly reduce operational energy consumption, water use, and environmental impacts. The GWP of UAV systems was about 67% lower compared to the TMABS, while the UGV, due to lower performance efficiency, exhibited a 4% larger GWP (kg CO2eq ha−1). The findings of this study highlight that UAVs can produce the optimal results in comparison to other application methods. Full article
(This article belongs to the Special Issue Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering)
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30 pages, 14454 KB  
Article
Design and Development of a Lightweight Foldable Robotic Arm with Straight-Line Motion for UAV Manipulation
by Kyler C. Bingham and Taher Deemyad
AgriEngineering 2026, 8(6), 233; https://doi.org/10.3390/agriengineering8060233 - 8 Jun 2026
Viewed by 854
Abstract
Unmanned aerial vehicles (UAVs) are widely used for monitoring and payload transport; however, their application in autonomous physical interaction remains limited due to payload constraints, stability challenges, and the complexity of integrating manipulation systems. This study presents the design and development of a [...] Read more.
Unmanned aerial vehicles (UAVs) are widely used for monitoring and payload transport; however, their application in autonomous physical interaction remains limited due to payload constraints, stability challenges, and the complexity of integrating manipulation systems. This study presents the design and development of a lightweight foldable robotic arm based on the ten-bar Kempe Kite Inversor II linkage for UAV aerial manipulation. The mechanism generates precise straight-line motion using a single degree of freedom. Kinematic modeling and simulation validated a maximum end-effector reach of approximately 0.42 m. Structural optimization using additive manufacturing and honeycomb cellular architectures significantly reduced system weight while maintaining mechanical reliability. A passive compliant gripper, counterbalance mechanism, onboard storage net, and landing gear were integrated to evaluate the arm in a practical harvesting scenario using cherries as the test object. The final integrated system weighs 0.351 kg during operation, remaining approximately 16% below the experimentally determined UAV payload limit of 0.4185 kg. Proof-of-concept flight demonstrations confirmed successful aerial grasping of cherries, validating the feasibility of the proposed lightweight manipulation approach for agricultural applications. Full article
(This article belongs to the Special Issue Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering)
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52 pages, 8301 KB  
Article
Multi-Sensor Fusion-Based Autonomous Navigation for a Tracked Agricultural Chassis in Hilly Farmland: Python and ROS/Gazebo Simulation Validation
by Wei Zhao, Bangbo Liu, Yang Pan, Xiaobiao Shang, Tianle Shi, Xi Xu and Hongfu Zhang
AgriEngineering 2026, 8(6), 231; https://doi.org/10.3390/agriengineering8060231 - 5 Jun 2026
Viewed by 624
Abstract
This paper proposes a multi-sensor fusion autonomous navigation method integrating a nine-axis IMU, the Leishen C16 mechanical LiDAR, and the LakiBeam1L single-line LiDAR, aimed at addressing issues such as track slippage and positioning drift that commonly occur in tracked chassis operating under continuously [...] Read more.
This paper proposes a multi-sensor fusion autonomous navigation method integrating a nine-axis IMU, the Leishen C16 mechanical LiDAR, and the LakiBeam1L single-line LiDAR, aimed at addressing issues such as track slippage and positioning drift that commonly occur in tracked chassis operating under continuously changing conditions on hilly slopes and farmland. IMU-derived slope and attitude information is used as a terrain prior and incorporated into adaptive ground segmentation, slope-cross-slope path cost modeling, and velocity regulation. Leishen C16 LiDAR point clouds are used for NDT scan-to-map localization and spatial obstacle representation, while the LakiBeam1L LiDAR establishes a velocity-dependent near-field safety zone for dynamic obstacle triggering and local avoidance. Python simulations were conducted in simple, general, and complex environments under five slope conditions, forming 15 environment-slope combinations. Three representative scenarios were further validated in ROS/Gazebo. To strengthen statistical reliability, 10 repeated trials were performed for each environment-slope-algorithm combination, and additional stress tests included obstacle-position perturbation, sensor noise perturbation, initial-pose perturbation, dynamic obstacle speed perturbation, and variable slope/local undulation perturbation. An isolated no-LakiBeam1L ablation, significance tests, IMU perturbation tests, planning-weight sensitivity analysis, and stronger-baseline comparison were also added. In the repeated-trial dataset, the proposed method improved the arrival rate from 23.3% to 94.7%, reduced tracking RMSE by 61.46%, reduced localization RMSE by 60.62%, and increased obstacle recall by 26.32%. Under mixed perturbations, the arrival rate of the proposed method was 81.3%, compared with 29.3% for the baseline. These results indicate improved simulation-level stability and perception reliability, while the applicability to real hilly farmland still requires hardware and field validation. Full article
(This article belongs to the Special Issue Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering)
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23 pages, 17045 KB  
Article
Deployment-Aware NAS for Lightweight UAV Object Detectors in Precision Agriculture Crop Monitoring
by Jaša Kerec, Alina L. Machidon and Octavian M. Machidon
AgriEngineering 2026, 8(2), 43; https://doi.org/10.3390/agriengineering8020043 - 1 Feb 2026
Cited by 4 | Viewed by 1252
Abstract
Unmanned aerial vehicles (UAVs) have become essential tools for monitoring crop condition, detecting early signs of plant stress, and supporting timely interventions in modern precision agriculture. However, real-time onboard image analysis remains challenging due to the limited computational and energy resources of small [...] Read more.
Unmanned aerial vehicles (UAVs) have become essential tools for monitoring crop condition, detecting early signs of plant stress, and supporting timely interventions in modern precision agriculture. However, real-time onboard image analysis remains challenging due to the limited computational and energy resources of small embedded UAV platforms. This work presents a deployment-aware neural architecture search (NAS) framework for discovering lightweight object detection networks explicitly optimized for edge hardware constraints. Building on the YOLOv8n baseline, the proposed NAS procedure yields detector architectures that substantially reduce computational load while preserving high detection accuracy for agricultural field monitoring tasks. The best-discovered model reduces GFLOPs by 37.0% and parameters by 61.3% compared to YOLOv8n, with only a 1.96% decrease in mAP@50. When deployed on an NVIDIA Jetson Nano, it achieves a 28.1% increase in inference speed and an 18.5% improvement in energy efficiency under ONNX Runtime, with additional gains using TensorRT FP16. Evaluation on wheat head and cotton seedling datasets demonstrates strong generalization across crop types and varying imaging conditions. By enabling highly efficient onboard inference, the proposed NAS framework supports practical UAV-based crop monitoring workflows and contributes to the development of responsive, field-ready remote sensing systems in resource-limited environments. Full article
(This article belongs to the Special Issue Applications of Robotics/UAVs and Computer Vision in Agricultural Engineering)
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