A5068728787- Smart Agriculture and AI
- Modular Robots and Swarm Intelligence
- Plant Surface Properties and Treatments
- Greenhouse Technology and Climate Control
- Robotic Path Planning Algorithms
- Electrowetting and Microfluidic Technologies
- Plant Disease Management Techniques
- Remote Sensing and LiDAR Applications
- Optimization and Search Problems
- Polymer-Based Agricultural Enhancements
- Distributed Control Multi-Agent Systems
- Robotics and Automated Systems
- Date Palm Research Studies
- Automated Road and Building Extraction
- Legume Nitrogen Fixing Symbiosis
- Fluid Dynamics and Heat Transfer
- Plant Virus Research Studies
- Nanoparticles: synthesis and applications
- Micro and Nano Robotics
- Robotics and Sensor-Based Localization
- Control and Dynamics of Mobile Robots
Chonnam National University
2020-2024
Fruit and vegetable harvesting robots have been widely studied developed in recent years. However, despite extensive research commercial tomato still remain a challenge. In this paper, we propose an efficient robot that combines the principle of 3D perception, Manipulation, End-effector. For robot, tomatoes are detected based on deep learning, after which coordinates target crop extracted motion control manipulator coordination. addition, suction pad featuring kirigami pattern, is part...
Abstract Nanomaterials associated with plant growth and crop cultivation revolutionize traditional concepts of agriculture. However, the poor reiterability these materials in agricultural applications necessitates development environmentally‐friendly approaches. To address this, biocompatible gelatin nanoparticles (GNPs) as nanofertilizers a small size (≈150 nm) positively charged surface (≈30 mV) that serve versatile tool practices is designed. GNPs load agrochemical agents to improve...
This study proposes a novel spray drift analysis method, based on 3D deep learning, managing and reducing using mobile LiDAR method. point clouds were trained to classify segment spraying forms from orchards the PointNet++ model, which is learning structure. The model represented an accuracy of 96.23%. system was demonstrated through its application in intelligent systems. Three control field experiments performed pear orchard verify effectiveness system. obtained results confirm...
The application of robots to the agricultural sector has significantly improved productivity in field. Studies investigating various forms end-effectors are being carried out constantly, especially relation harvests fruits that require a lot labor. However, majority studies focus on stably cutting pedicel after accurately recognizing target, while there is relatively little interest method and process transferring produce harvest. development dedicated end-effector essential reduce duration...
This paper introduce advancements in agricultural robotics response to the increasing demand for automation agriculture. Our research aims develop humancentered robotic systems designed enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor significantly impact performance, addressing four primary systems, i.e., harvesting robots, intelligent spraying autonomous driving robots greenhouse operations,...
This study proposes a discrete event system-based control strategy for autonomous tributary mapping using multi-unmanned aerial vehicle. When considering the unmanned vehicles as systems, supervisory theory is used to model and individual vehicle behavior in system. In mapping, situation changes each time depending on environmental factors (e.g. weather) work must be performed an unstructured environment. Therefore, this article vehicle-based system solve real-field problems. Unlike systems...
In this paper, we proposed a deep learning-based intelligent spraying system for pear orchard. A fruit tree detection was developed using the SegNet model, one of semantic segmentation structures. The learning model performed with an accuracy 75.39%. To operate nozzle, each image captured form camera separated lengthwise into quarters and mapped to nozzles. Then, nozzle opened when area trees in zone exceeded 20%. field test orchard verify effectiveness our system. From obtained results,...
This paper proposes a variable flow control system in real time with deep learning using the segmentation of fruit trees pear orchard. The rate time, undesired pressure fluctuation and theoretical modeling may differ from those world. Therefore, two types preliminary experiments were designed to examine linear relationship modeling. Through experiment, parameters pulse width modulation (PWM) controller optimized, an actual field experiment was conducted confirm performance system. As result...