Wenping Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Wenping Zhang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Fan Zhang
College of Mechanical and Electronic Engineering, Qingdao Agricultural University, Qingdao 266109, China
1 shared publication - Hao Yang
College of Mechanical and Electronic Engineering, Qingdao Agricultural University, Qingdao 266109, China
1 shared publication - Jiasheng Wang
College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
1 shared publication - Juan Li
Qingdao Agricultural University
1 shared publication - Yunhao Zhang
College of Mechanical and Electronic Engineering, Qingdao Agricultural University, Qingdao 266109, China
1 shared publication
Latest Publications
- Open Access
- 15 February 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (4) , 456
Orchard Chestnut Visual Harvest Maturity Detection and Segmentation Using an Improved YOLO-Based Method
Visual harvest maturity is a key visual phenotype for orchard management and harvesting decisions, yet chestnut fruits in natural orchards often exhibit weak color contrast, subtle texture variation, blurred boundaries, and frequent occlusion under complex illumination. This study addresses RGB-based visual harvest maturity recognition and proposes AHM-YOLO, an improved instance segmentation model built upon YOLOv11n-seg. The proposed model enhances maturity-related feature representation by strengthening color- and edge-sensitive cues, stabilizing spatial dependencies under occlusion and illumination variation, and improving cross-scale semantic consistency in dense orchard scenes. A chestnut dataset collected from a typical orchard in Shandong Province is annotated into three visual harvest maturity stages (unripe, semi-ripe, and ripe). To ensure reliable evaluation, the dataset is partitioned at the acquisition unit level, and all experiments are conducted using multi-seed repeated runs. Experimental results show that AHM-YOLO achieves 84.3% Mask mAP50 and 72.2% Mask mAP50–95, demonstrating consistent improvements over the baseline model in complex orchard environments.
- 15 February 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (4) , 456
Open AccessOrchard Chestnut Visual Harvest Maturity Detection and Segmentation Using an Improved YOLO-Based Method
Visual harvest maturity is a key visual phenotype for orchard management and harvesting decisions, yet chestnut fruits in natural orchards often exhibit weak color contrast, subtle texture variation, blurred boundaries, and frequent occlusion under complex illumination. This study addresses RGB-based visual harvest maturity recognition and proposes AHM-YOLO, an improved instance segmentation model built upon YOLOv11n-seg. The proposed model enhances maturity-related feature representation by strengthening color- and edge-sensitive cues, stabilizing spatial dependencies under occlusion and illumination variation, and improving cross-scale semantic consistency in dense orchard scenes. A chestnut dataset collected from a typical orchard in Shandong Province is annotated into three visual harvest maturity stages (unripe, semi-ripe, and ripe). To ensure reliable evaluation, the dataset is partitioned at the acquisition unit level, and all experiments are conducted using multi-seed repeated runs. Experimental results show that AHM-YOLO achieves 84.3% Mask mAP50 and 72.2% Mask mAP50–95, demonstrating consistent improvements over the baseline model in complex orchard environments.