no code implementations • 30 Mar 2024 • Gengming Zhang, Hao Cao, Kewei Hu, Yaoqiang Pan, Yuqin Deng, Hongjun Wang, Hanwen Kang
Accurately identifying lychee-picking points in unstructured orchard environments and obtaining their coordinate locations is critical to the success of lychee-picking robots.
no code implementations • 24 Mar 2024 • Junhong Zhao, Wei Ying, Yaoqiang Pan, Zhenfeng Yi, Chao Chen, Kewei Hu, Hanwen Kang
This study investigates a learning-based phenotyping method using the Neural Radiance Field to achieve accurate in-situ phenotyping of pepper plants in greenhouse environments.
no code implementations • 7 Nov 2023 • Kewei Hu, Ying WEI, Yaoqiang Pan, Hanwen Kang, Chao Chen
Recently, a promising development has emerged in the form of Neural Radiance Field (NeRF), a novel method that utilises neural density fields.
no code implementations • 4 Aug 2022 • Hanwen Kang, Xing Wang
In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor.
no code implementations • 8 Dec 2021 • Hanwen Kang, Xing Wang, Chao Chen
It is vital for robots to recognise and localise fruits before the harvesting in natural orchards.
no code implementations • 30 Mar 2020 • Hanwen Kang, Chao Chen
In this research, a fully neural network based visual perception framework for autonomous apple harvesting is proposed.
no code implementations • 29 Dec 2019 • Hanwen Kang, Chao Chen
This paper develops a framework of visual perception and modelling for robotic harvesting of fruits in the orchard environments.
no code implementations • 28 Nov 2019 • Hanwen Kang, Chao Chen
The robustness and efficiency of the DaSNet-V2 in detection and segmentation are validated by the experiments in the real-environment of apple orchard.