Juntao Xiong
Papers
22
Total Citations
1,493
H-Index
17
About
Juntao Xiong is a prominent researcher specializing in agricultural robotics, computer vision, and precision horticulture, with a particular focus on automating fruit harvesting through advanced visual detection systems. His work centers on developing intelligent algorithms that enable robotic systems to detect, localize, and interact with fruits and plant structures in complex, real-world environments. Xiong's most significant contributions include pioneering multi-modal detection frameworks that integrate color, depth, and shape information for robust 3D fruit recognition, alongside deep learning approaches such as DeepLabV3+ for semantic segmentation of delicate plant structures like litchi branches. His research addresses practical harvesting challenges, including nighttime detection, green fruit identification under camouflage conditions, and collision-free robotic picking through precise pose estimation using low-cost RGB-D sensors. With collectively over 1,200 citations across his top ten papers, Xiong's influence on the field is substantial. His studies spanning litchi, guava, and citrus crops demonstrate a versatile and translatable methodology applicable across horticultural contexts. Notably, his early work on nocturnal litchi cluster recognition laid foundational groundwork for after-dark harvesting robotics, a technically demanding frontier that continues to inspire follow-up research across the agricultural engineering community.
Research Focus
Key Achievements
Top Papers
- 1Color-, depth-, and shape-based 3D fruit detection172 citations · 2019
- 2Semantic Segmentation of Litchi Branches Using DeepLabV3+ Model162 citations · 2020
- 3Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field158 citations · 2019
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- 5A visual detection method for nighttime litchi fruits and fruiting stems145 citations · 2020
- 6In-field citrus detection and localisation based on RGB-D image analysis112 citations · 2019
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