Smart Farming: Advancing Techniques for High-Value Crops

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 419

Special Issue Editors


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Guest Editor
Center for Research and Technology Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (iBO), Charilaou-Thermi Rd., 57001 Thessaloniki, Greece
Interests: agriculture; precision agriculture; energy in agriculture; bioeconomy
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Guest Editor
Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Interests: smart farming; farm machinery; remote sensing; sustainability; decision support systems

Special Issue Information

Dear Colleagues,

In the era of polycrisis that we are currently living in, the agricultural sector is facing big challenges: climate change, energy and economic crises, urbanization, an ageing population and labor shortages. Smart farming can contribute significantly and mitigate these challenges, especially for high-value crops such as orchards, vineyards and vegetables, which exhibit higher needs as regards crop monitoring and management.

The main aim of this issue is to identify smart farming-based solutions for high-value crops that hold the key to mitigate the impact of the aforementioned challenges. Researchers are invited to contribute original research articles, reviews, and perspectives that encompass a wide range of topics within the realm of smart farming of high-value crops. The Issue’s scope includes, but is not limited to, investigations into the use of farm machinery, human–machine interaction, AI, extended reality, IoT, smart sensors, ground and aerial robotics, decision support systems, remote sensing, adoption, and environmental impact.

Dr. Thanos Balafoutis
Dr. Evangelos Anastasiou
Guest Editors

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Keywords

  • high-value crops
  • smart farming
  • agricultural robotics
  • decision support systems
  • orchards
  • vineyards
  • vegetables
  • extended reality
  • AI
  • human–machine interaction

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Published Papers (1 paper)

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Research

31 pages, 11649 KiB  
Article
Development of Shunt Connection Communication and Bimanual Coordination-Based Smart Orchard Robot
by Bin Yan and Xiameng Li
Agronomy 2025, 15(8), 1801; https://doi.org/10.3390/agronomy15081801 - 25 Jul 2025
Viewed by 233
Abstract
This research addresses the enhancement of operational efficiency in apple-picking robots through the design of a bimanual spatial configuration enabling obstacle avoidance in contemporary orchard environments. A parallel coordinated harvesting paradigm for dual-arm systems was introduced, leading to the construction and validation of [...] Read more.
This research addresses the enhancement of operational efficiency in apple-picking robots through the design of a bimanual spatial configuration enabling obstacle avoidance in contemporary orchard environments. A parallel coordinated harvesting paradigm for dual-arm systems was introduced, leading to the construction and validation of a six-degree-of-freedom bimanual apple-harvesting robot. Leveraging the kinematic architecture of the AUBO-i5 manipulator, three spatial layout configurations for dual-arm systems were evaluated, culminating in the adoption of a “workspace-overlapping Type B” arrangement. A functional prototype of the bimanual apple-harvesting system was subsequently fabricated. The study further involved developing control architectures for two end-effector types: a compliant gripper and a vacuum-based suction mechanism, with corresponding operational protocols established. A networked communication framework for parallel arm coordination was implemented via Ethernet switching technology, enabling both independent and synchronized bimanual operation. Additionally, an intersystem communication protocol was formulated to integrate the robotic vision system with the dual-arm control architecture, establishing a modular parallel execution model between visual perception and motion control modules. A coordinated bimanual harvesting strategy was formulated, incorporating real-time trajectory and pose monitoring of the manipulators. Kinematic simulations were executed to validate the feasibility of this strategy. Field evaluations in modern Red Fuji apple orchards assessed multidimensional harvesting performance, revealing 85.6% and 80% success rates for the suction and gripper-based arms, respectively. Single-fruit retrieval averaged 7.5 s per arm, yielding an overall system efficiency of 3.75 s per fruit. These findings advance the technological foundation for intelligent apple-harvesting systems, offering methodologies for the evolution of precision agronomic automation. Full article
(This article belongs to the Special Issue Smart Farming: Advancing Techniques for High-Value Crops)
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