About
I am a PhD student in Agricultural Electrification and Automation at Shenyang Agricultural University, advised by Prof. Tongyu Xu and Dr. Teng Miao.
Prior to this, I received a BSc degree in a related field from Shenyang Agricultural University.
My current research focuses on 3D point cloud-based high-throughput phenotyping in crops, with an emphasis on developing deep learning methods for plant segmentation, trait extraction, and intelligent agricultural applications.
Selected Publications
View All →PlaneSegNet: A Deep Learning Network with Plane Attention for Plant Point Cloud Segmentation in Agricultural Environments
Xin Yang, Chenyi Xu, Yan Wang, Ruixia Feng, Jinshi Yu, Zichen Su, Teng Miao, Tongyu Xu
Artificial Intelligence in Agriculture
PlaneSegNet is a voxel-based semantic segmentation network featuring a novel plane attention module that fuses projection features from the XZ and YZ planes to better capture vertical geometric structures. Designed for complex agricultural environments, it effectively addresses challenges such as high noise, dense plant distribution, and ambiguous boundaries between plant and non-plant regions. Evaluated across diverse scenarios—including open fields, greenhouses, and large rural landscapes—PlaneSegNet outperforms both geometry-based and deep learning baselines in plant/non-plant separation, enabling direct extraction of high-quality plant-only point clouds with minimal manual preprocessing. The dataset and code are publicly available.
PACANet: A Paired-Attention Central Axis Aggregation Network for Plant Population Point Cloud Segmentation and Phenotypic Trait Extraction---A Case Study on Maize
Xin Yang, Teng Miao, Yitong Tao, Bo Zhang, Xiaotong Wu, Xiaodan Han, Jinshi Yu, Yuncheng Zhou, Hanbing Deng, Ying Wang, Tongyu Xu
Computers and Electronics in Agriculture
A novel framework, Paired-Attention Central Axis Aggregation Network (PACANet), is proposed for segmenting individual maize plants from dense 3D canopy point clouds. It combines a 3D paired-attention backbone to enrich point-wise features and a central axis projection strategy to enhance spatial coherence during instance separation. To reduce dependency on manual annotations, simulated point clouds are used for training. PACANet achieves state-of-the-art performance with an average precision of 0.9246 using only synthetic data and enables accurate extraction of plant- and organ-level phenotypic traits across varying planting densities. The code and data are publicly available.
Maize Stem--Leaf Segmentation Framework Based on Deformable Point Clouds
Xin Yang, Teng Miao, Xueying Tian, Dabao Wang, Jianxiang Zhao, Lili Lin, Chao Zhu, Tao Yang, Tongyu Xu
ISPRS Journal of Photogrammetry and Remote Sensing
A 3D point-cloud dataset of 428 maize plants (2-12 leaves) is introduced for stem-leaf segmentation, accompanied by a deformation-based augmentation strategy that enhances morphological diversity while preserving organ geometry. Using only 22 labeled samples, the approach achieves high segmentation accuracy—91.93% mIoU with PointNet++ and 93.74% mAP with HAIS—demonstrating efficient model training with minimal annotations. The dataset and source code are publicly available.
News
Our work has been accepted by Artificial Intelligence in Agriculture 🎉