Yuanzhi Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yuanzhi Wang has a total of 16 co-authors. Here are the top co-authors by number of shared publications:
- Chengming Sun
Cultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou, PR China
1 shared publication - Chenliang Wu
School of Computer and Information, Anqing Normal University, Anqing 246011, China
1 shared publication Dongwei Han
Cultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou, PR China
1 shared publicationFei Wu
Precision Agriculture Lab, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
1 shared publicationHainie Zha
AnQing Normal University, AnQing, PR China
1 shared publication- Hainie Zha
Anqing Normal University, Anking, China, China
1 shared publication
Latest Publications
- 9 February 2026
- Published by Springer Nature in Precision Agriculture
- Vol. 27 (2) , 24
Wheat biomass estimation by fusing color index and canopy volume based on UAV RGB images
- 9 February 2026
- Published by Springer Nature in Precision Agriculture
- Vol. 27 (2) , 24
Wheat biomass estimation by fusing color index and canopy volume based on UAV RGB images
- Open Access
MaizeStar-YOLO: Precise Detection and Localization of Seedling-Stage Maize
Efficient detection and localization of maize seedlings in complex field environments is essential for accurate plant segmentation and subsequent three-dimensional morphological reconstruction. To overcome the limited accuracy and high computational cost of existing models, we propose an enhanced architecture named MaizeStar-YOLO. The redesigned backbone integrates a novel C2F_StarsBlock to improve multi-scale feature fusion, while a PKIStage module is introduced to enhance feature representation under challenging field conditions. Evaluations on a diverse dataset of maize seedlings show that our model achieves a mean average precision (mAP) of 92.8%, surpassing the YOLOv8 baseline by 3.6 percentage points, while reducing computational complexity to 3.0 GFLOPs, representing a 63% decrease. This efficient and high-performing framework enables precise plant–background segmentation and robust three-dimensional feature extraction for morphological analysis. Additionally, it supports downstream applications such as pest and disease diagnosis and targeted agricultural interventions.
Open AccessMaizeStar-YOLO: Precise Detection and Localization of Seedling-Stage Maize
Efficient detection and localization of maize seedlings in complex field environments is essential for accurate plant segmentation and subsequent three-dimensional morphological reconstruction. To overcome the limited accuracy and high computational cost of existing models, we propose an enhanced architecture named MaizeStar-YOLO. The redesigned backbone integrates a novel C2F_StarsBlock to improve multi-scale feature fusion, while a PKIStage module is introduced to enhance feature representation under challenging field conditions. Evaluations on a diverse dataset of maize seedlings show that our model achieves a mean average precision (mAP) of 92.8%, surpassing the YOLOv8 baseline by 3.6 percentage points, while reducing computational complexity to 3.0 GFLOPs, representing a 63% decrease. This efficient and high-performing framework enables precise plant–background segmentation and robust three-dimensional feature extraction for morphological analysis. Additionally, it supports downstream applications such as pest and disease diagnosis and targeted agricultural interventions.