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Intelligent Automation for Agricultural Robotics: AI, LLMs, and Data Fusion...
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Intelligent Automation for Agricultural Robotics: AI, LLMs, and Data Fusion Approaches

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3731

Special Issue Editors

Dr. Ata Jahangir Moshayedi

E-Mail Website
Guest Editor
Smart Structural Health Monitoring and Control Laboratory, Dongguan University of Technology, Dongguan 523808, China
Interests: robotics and automation; sensor modelling; bio-inspired robots; mobile robot olfaction; plume tracking; embedded systems; machine vision-based systems; virtual reality and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Zeashan H. Khan

E-Mail Website
Guest Editor
IRC-IMR, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Interests: cyber-physical systems in the manufacturing and process industry smart grid, aerospace, and healthcare
Special Issues, Collections and Topics in MDPI journals
Dr. Amin Kolahdooz

E-Mail Website
Guest Editor
School of Engineering and Sustainable Development, De Montfort University, Leicester LE1 9BH, UK
Interests: manufacturing; finite element modeling; optimization; machine learning; VR&AR in manufacturing

Special Issue Information

Dear Colleagues,

Agriculture is entering a new era of automation, with intelligent robots transforming farming practices into data-driven, efficient, and sustainable processes. Robots equipped with artificial intelligence (AI), large language models (LLMs), multimodal data fusion, and edge computing are enabling smart farming solutions that can handle complex tasks such as harvesting, weeding, crop monitoring, and soil analysis in highly unstructured farm environments.

This Special Issue of Automation seeks to bring together cutting-edge research and practical applications that advance the design, development, and deployment of agricultural robots. Our goal is to explore how intelligent automation technologies can improve productivity, sustainability, and precision in agriculture while fostering human–robot collaboration.

We welcome original research articles, reviews, and case studies covering, but not limited to, the following topics:

  • AI- and LLM-driven agricultural robots;
  • Human–robot interactions in farming applications (voice, multimodal, natural language, etc.);
  • Data fusion and multimodal integration (vision, IoT, UAVs, and ground robots);
  • Harvesting, weeding, spraying, and planting robots;
  • Edge and cloud computing for smart agriculture;
  • Crop and soil monitoring using robots and drones;
  • Swarm robotics and drone–robot collaboration;
  • Sustainable and energy-efficient robotic farming solutions;
  • Field trials, case studies, and real-world deployment of agricultural robots.

Dr. Ata Jahangir Moshayedi
Dr. Zeashan H. Khan
Dr. Amin Kolahdooz
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. Automation 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 1200 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

  • Automation
  • robotics
  • AI
  • IoT
  • agriculture

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Published Papers (4 papers)

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Research

33 pages, 1936 KB  
Article
The AgriTrust Framework: Federated Semantic Governance for Trusted and Interoperable Agricultural Data Sharing
by Ivan Bergier, Jayme Garcia Arnal Barbedo, Édson Luis Bolfe, Debora Drucker and Filipi Miranda Soares
Automation 2026, 7(2), 57; https://doi.org/10.3390/automation7020057 - 31 Mar 2026
Viewed by 504
Abstract
New regulations, such as the EU Deforestation-Free Regulation (EUDR), make verifiable agricultural data (AgData) essential for global trade. However, its value is compromised by a widespread “AgData Paradox”, characterized by distrust and fragmentation. To address this problem, we present AgriTrust, a federated semantic [...] Read more.
New regulations, such as the EU Deforestation-Free Regulation (EUDR), make verifiable agricultural data (AgData) essential for global trade. However, its value is compromised by a widespread “AgData Paradox”, characterized by distrust and fragmentation. To address this problem, we present AgriTrust, a federated semantic governance framework that automates and governs data sharing. Its key methodological innovation lies in the deep integration of a multi-sectorial governance model with a semantic digital layer, implemented through the AgriTrust Ontology (an OWL ontology for tokenization and traceability) and a multi-vendor, blockchain-agnostic architecture that avoids single-vendor dependence. We demonstrate the framework’s feasibility through simulated case studies in three critical Brazilian supply chains: coffee (EUDR compliance), soybean (mass balance), and beef (animal traceability). Using a semantic reasoning pipeline on a proof-of-concept federated knowledge graph of 2010 triples, we show how AgriTrust enables verifiable provenance representation, automated compliance checking via executable data contracts, and cross-platform asset management. The results provide initial evidence that AgriTrust offers a conceptually coherent blueprint for agricultural data sharing, though operational deployment, scalability testing, and performance validation under real-world conditions remain as future work. Full article
(This article belongs to the Special Issue Intelligent Automation for Agricultural Robotics: AI, LLMs, and Data Fusion Approaches)
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18 pages, 11760 KB  
Article
Innovative Real-Time Palm Tree Detection, Geo-Localization and Counting from Unmanned Aerial Vehicle (UAV) Aerial Images Using Deep Learning
by Ali Mazinani, Mostafa Norouzi, Amin Talaeizadeh, Aria Alasty, Mahmoud Saadat Foumani and Amin Kolahdooz
Automation 2026, 7(2), 51; https://doi.org/10.3390/automation7020051 - 16 Mar 2026
Viewed by 554
Abstract
Accurate real-time detection, geolocation, and counting of palm trees are essential for plantation management, yield estimation, and resource allocation in precision agriculture. Traditional approaches such as manual surveys or offline image processing are labor-intensive and unsuitable for large-scale applications. This study introduces a [...] Read more.
Accurate real-time detection, geolocation, and counting of palm trees are essential for plantation management, yield estimation, and resource allocation in precision agriculture. Traditional approaches such as manual surveys or offline image processing are labor-intensive and unsuitable for large-scale applications. This study introduces a fully onboard real-time framework that integrates Unmanned Aerial Vehivle (UAV) imagery, the YOLOv12 deep learning model, and a camera projection technique to detect, geolocate, and count palm trees directly during flight. The lightweight YOLOv12n variant, deployed on an NVIDIA Jetson Nano edge device, achieved a detection precision of 92.4%, an average geolocation error of 2.14 m, and a counting error of only 0.2% across 915 trees. Unlike many existing methods that rely on offline processing or offboard computation, the proposed system performs all computations in real time, enabling immediate decision-making for tasks such as plantation density analysis, replanting planning, and yield forecasting. Experimental results demonstrate that the proposed approach provides a scalable, cost-effective, and autonomous solution for modern precision agriculture. Full article
(This article belongs to the Special Issue Intelligent Automation for Agricultural Robotics: AI, LLMs, and Data Fusion Approaches)
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21 pages, 2266 KB  
Article
Path Optimization for Aircraft Based on Geographic Information Systems and Deep Learning
by Saadi Turied Kurdi, Luttfi A. Al-Haddad and Ahmed Ali Farhan Ogaili
Automation 2026, 7(1), 12; https://doi.org/10.3390/automation7010012 - 3 Jan 2026
Cited by 3 | Viewed by 1019
Abstract
Autonomous navigation for agricultural UAVs faces persistent challenges due to atmospheric disturbances such as wind direction, temperature gradients, and pressure variations, which can lead to significant deviations from planned flight paths. This study presents a deep learning-based navigation approach that integrates geographic information [...] Read more.
Autonomous navigation for agricultural UAVs faces persistent challenges due to atmospheric disturbances such as wind direction, temperature gradients, and pressure variations, which can lead to significant deviations from planned flight paths. This study presents a deep learning-based navigation approach that integrates geographic information systems (GIS) with deep neural networks (DNNs) to improve energy efficiency and trajectory accuracy in agricultural UAV operations. To simulate realistic environmental disturbances, actual flight data from an Iraqi Airways short-haul route (Baghdad–Istanbul–Baghdad) were utilized. These trajectories were affected by both tailwinds and headwinds and were analyzed and modeled to train a DNN capable of predicting and correcting path deviations. The optimized system was then tested in a simulated agricultural UAV context. Results show that for tailwind conditions (Baghdad–Istanbul), the GIS-DNN model reduced fuel consumption by 610 L and flight time by 31 min compared to actual conditions. In headwind conditions (Istanbul–Baghdad), the model achieved a 558 L fuel saving and reduced the flight time by 28 min. Based on these results, it can be concluded that deep learning integrated with GIS can significantly enhance UAV path optimization for improved energy efficiency and mission reliability in precision agriculture. Full article
(This article belongs to the Special Issue Intelligent Automation for Agricultural Robotics: AI, LLMs, and Data Fusion Approaches)
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17 pages, 3389 KB  
Article
Offboard Fault Diagnosis for Large UAV Fleets Using Laser Doppler Vibrometer and Deep Extreme Learning
by Mohamed A. A. Ismail, Saadi Turied Kurdi, Mohammad S. Albaraj and Christian Rembe
Automation 2026, 7(1), 6; https://doi.org/10.3390/automation7010006 - 31 Dec 2025
Viewed by 884
Abstract
Unmanned Aerial Vehicles (UAVs) have become integral to modern applications, including smart agricultural robotics, where reliability is essential to ensure safe and efficient operation. It is commonly recognized that traditional fault diagnosis approaches usually rely on vibration and noise measurements acquired via onboard [...] Read more.
Unmanned Aerial Vehicles (UAVs) have become integral to modern applications, including smart agricultural robotics, where reliability is essential to ensure safe and efficient operation. It is commonly recognized that traditional fault diagnosis approaches usually rely on vibration and noise measurements acquired via onboard sensors or similar methods, which typically require continuous data acquisition and non-negligible onboard computational resources. This study presents a portable Laser Doppler Vibrometer (LDV)-based system designed for noncontact, offboard, and high-sensitivity measurement of UAV vibration signatures. The LDV measurements are analyzed using a Deep Extreme Learning-based Neural Network (DeepELM-DNN) capable of identifying both propeller fault type and severity from a single 1 s measurement. Experimental validation on a commercial quadcopter using 50 datasets across multiple induced fault types and severity levels demonstrates a classification accuracy of 97.9%. Compared to conventional onboard sensor-based approaches, the proposed framework shows strong potential for reduced computational effort while maintaining high diagnostic accuracy, owing to its short measurement duration and closed-form learning structure. The proposed LDV setup and DeepELM-DNN framework enable noncontact fault inspection while minimizing or eliminating the need for additional onboard sensing hardware. This approach offers a practical and scalable diagnostic solution for large UAV fleets and next-generation smart agricultural and industrial aerial robotics. Full article
(This article belongs to the Special Issue Intelligent Automation for Agricultural Robotics: AI, LLMs, and Data Fusion Approaches)
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