AI-Driven Optimization in Robotics and Precision Agriculture
A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Environmental Technology".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 952
Special Issue Editors
Interests: machine vision; recycling; robotics; renewable energy sources; embedded systems; environmental sustainability; neural network; waste management; convolutional neural networks; object detection
Special Issues, Collections and Topics in MDPI journals
Interests: computer architecture; robotics; embedded and cyber–physical systems; gamification; Internet of Things; security; hardware and software co-synthesis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to highlight cutting-edge advancements in the integration of artificial intelligence, robotics, and precision agriculture, with a particular focus on computational optimization and intelligent automation. We invite high-quality original research, reviews, and case studies that explore how AI-driven methodologies can be leveraged to enhance agricultural robotics, decision-making, and field operations.
As modern agriculture increasingly relies on robotics, embedded systems, and real-time decision-making, the integration of AI with high-performance computing (HPC), heterogeneous architectures (including CPUs, GPUs, and FPGAs), and IoT-enabled platforms has become essential. This Special Issue seeks to showcase contributions that harness AI to optimize robotic performance, enhance autonomy, and improve the overall efficiency and sustainability of precision farming operations.
Topics of interest include, but are not limited to, the following:
- AI-based optimization algorithms for agricultural robotics;
- Embedded and real-time AI systems in field environments;
- FPGA and heterogeneous acceleration for robotics applications;
- Sensor fusion, perception, and decision-making in precision agriculture;
- IoT and edge computing frameworks for smart farming;
- Reinforcement learning and computer vision for autonomous systems;
- Scalable HPC architectures for agricultural data analysis;
- Generative AI for simulation, synthetic data generation, and predictive modeling in agricultural systems;
- Augmented reality (AR) for operator training, remote supervision, and interactive agricultural planning;
- Serious games for modeling, simulation, and stakeholder engagement in smart farming and robotics education;
- Digital twins for modeling, simulation, and real-time optimization of agricultural environments and robotic systems;
- Field-level implementations and deployments of AI-powered robotic platforms in real agricultural settings;
- Case studies on operational installations demonstrating practical challenges and performance of AI-integrated systems in precision farming.
Dr. Dimitris Ziouzios
Dr. Minas Dasygenis
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- precision agriculture
- agricultural robotics
- optimization algorithms
- embedded systems
- FPGA acceleration
- heterogeneous computing
- edge AI
- IoT in agriculture
- high-performance computing (HPC)
- sensor fusion
- autonomous systems
- computer vision
- reinforcement learning
- generative AI
- digital twins
- augmented reality
- serious games
- real-time systems
- field deployments
- smart farming
- data-driven agriculture
- robotic perception
- simulation and modeling
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