Juxia Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Juxia Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Fumin Ma
College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050, China
2 shared publications - Hongxiong Su
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China
2 shared publications - Sa Wang
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China
2 shared publications - Yanwen Li
College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
2 shared publications - Honglin Su
College of Information Science and Engineering, Shanxi Agricultural University, Taigu 030801, China
1 shared publication
Latest Publications
- Open Access
- 2 February 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (3) , 355
Zucchini Fruit Detection and Weight Prediction Based on H2RBox-v2-SF in Complex Greenhouse Environments
The accurate detection and non-contact weight estimation of zucchini fruits are crucial for automated harvesting systems. This study presents a novel weakly supervised oriented object detection method for zucchini fruit recognition and weight prediction in complex greenhouse environments. Our approach, termed H2RBox-v2-SF, introduces three key enhancements to the original H2RBox-v2 model. First, the Swin Transformer V2 (SwinV2) is adopted as the backbone network to replace 50-layer Residual Networks (ResNet-50), significantly strengthening feature extraction capabilities. Second, the Bi-directional Feature Pyramid Network (BiFPN) is employed instead of the original Feature Pyramid Network (FPN) to achieve more efficient multi-scale feature fusion. Third, the FPDIoU loss is introduced to replace the CircumIoU loss, enhancing the accuracy and efficiency of bounding box regression. Furthermore, we propose a Morphology-based Fruit Weight Estimation (MFWE) algorithm that leverages depth information for non-contact weight estimation. Experimental results demonstrate that the improved model achieves an AP@0.75 of 87.8%, a precision of 69.8%, and a recall of 91.5%, representing improvements of 9.6%, 5.0%, and 4.7% respectively over the original model. Additionally, the weight estimation achieves a mean absolute error (MAE) of 55.05 g, a coefficient of determination () of 0.899, and a root mean square error (RMSE) of 63.59 g. The proposed method achieves high accuracy for ‘Jinghu No. 43’ zucchini fruit detection and weight estimation under greenhouse conditions, offering an effective technical solution for automated zucchini harvesting.- 2 February 2026
- Published by MDPI AG in Agriculture
- Vol. 16 (3) , 355
Open AccessZucchini Fruit Detection and Weight Prediction Based on H2RBox-v2-SF in Complex Greenhouse Environments
The accurate detection and non-contact weight estimation of zucchini fruits are crucial for automated harvesting systems. This study presents a novel weakly supervised oriented object detection method for zucchini fruit recognition and weight prediction in complex greenhouse environments. Our approach, termed H2RBox-v2-SF, introduces three key enhancements to the original H2RBox-v2 model. First, the Swin Transformer V2 (SwinV2) is adopted as the backbone network to replace 50-layer Residual Networks (ResNet-50), significantly strengthening feature extraction capabilities. Second, the Bi-directional Feature Pyramid Network (BiFPN) is employed instead of the original Feature Pyramid Network (FPN) to achieve more efficient multi-scale feature fusion. Third, the FPDIoU loss is introduced to replace the CircumIoU loss, enhancing the accuracy and efficiency of bounding box regression. Furthermore, we propose a Morphology-based Fruit Weight Estimation (MFWE) algorithm that leverages depth information for non-contact weight estimation. Experimental results demonstrate that the improved model achieves an AP@0.75 of 87.8%, a precision of 69.8%, and a recall of 91.5%, representing improvements of 9.6%, 5.0%, and 4.7% respectively over the original model. Additionally, the weight estimation achieves a mean absolute error (MAE) of 55.05 g, a coefficient of determination () of 0.899, and a root mean square error (RMSE) of 63.59 g. The proposed method achieves high accuracy for ‘Jinghu No. 43’ zucchini fruit detection and weight estimation under greenhouse conditions, offering an effective technical solution for automated zucchini harvesting. - Open Access
- 16 September 2025
- Published by MDPI AG in Agriculture
- Vol. 15 (18) , 1954
YOLOv8n-Pose-DSW: A Precision Picking Point Localization Model for Zucchini in Complex Greenhouse Environments
Zucchini growth in greenhouse environments presents significant challenges for fruit recognition and picking point localization due to characteristics such as foliage occlusion, high density, structural complexity, and diverse fruit morphologies. Current recognition and localization algorithms exhibit limitations including low accuracy, restricted applicability, and procedural complexity, falling short of the requirements for precise and robust intelligent harvesting. To address these issues, this study constructs a zucchini dataset of 942 images using an Intel RealSense D455 depth camera and a smartphone, and proposes a novel keypoint detection model named YOLOv8n-Pose-DSW. The model introduces three key enhancements compared with YOLOv8n-Pose. First, the conventional upsample operator is replaced with an adaptive point sampling operator called Dysample, improving detection accuracy while reducing GPU memory consumption. Second, a Slim-Neck structure is designed to decrease computational overhead through lightweight bottleneck architecture, while preserving robust feature representation. Third, the WIoU-v3 loss is adopted to optimize bounding box regression for object detection, thereby enhancing localization accuracy. Experimental results demonstrate that YOLOv8n-Pose-DSW achieves a zucchini detection P, R, mAP@50, and mAP@50–95 of 92.1%, 90.7%, 94.0%, and 71.4%, respectively. These metrics represent improvements of 3.3%, 11.7%, 7.4%, and 15.4%, respectively, over the original model. For picking point localization, the improved model attains a P of 93.1%, R of 89.5%, mAP@50 of 95.6%, and mAP@50–95 of 95.2%, corresponding to gains of 8.8%, 11.0%, 11.3%, and 27.9% over the original model. Further error analysis shows that picking point localization errors are concentrated within the 0–4-pixel range, demonstrating enhanced localization precision critical for practical harvesting applications. The proposed algorithm effectively addresses greenhouse environmental challenges and provides essential technical support for intelligent zucchini harvesting systems.
- 16 September 2025
- Published by MDPI AG in Agriculture
- Vol. 15 (18) , 1954
Open AccessYOLOv8n-Pose-DSW: A Precision Picking Point Localization Model for Zucchini in Complex Greenhouse Environments
Zucchini growth in greenhouse environments presents significant challenges for fruit recognition and picking point localization due to characteristics such as foliage occlusion, high density, structural complexity, and diverse fruit morphologies. Current recognition and localization algorithms exhibit limitations including low accuracy, restricted applicability, and procedural complexity, falling short of the requirements for precise and robust intelligent harvesting. To address these issues, this study constructs a zucchini dataset of 942 images using an Intel RealSense D455 depth camera and a smartphone, and proposes a novel keypoint detection model named YOLOv8n-Pose-DSW. The model introduces three key enhancements compared with YOLOv8n-Pose. First, the conventional upsample operator is replaced with an adaptive point sampling operator called Dysample, improving detection accuracy while reducing GPU memory consumption. Second, a Slim-Neck structure is designed to decrease computational overhead through lightweight bottleneck architecture, while preserving robust feature representation. Third, the WIoU-v3 loss is adopted to optimize bounding box regression for object detection, thereby enhancing localization accuracy. Experimental results demonstrate that YOLOv8n-Pose-DSW achieves a zucchini detection P, R, mAP@50, and mAP@50–95 of 92.1%, 90.7%, 94.0%, and 71.4%, respectively. These metrics represent improvements of 3.3%, 11.7%, 7.4%, and 15.4%, respectively, over the original model. For picking point localization, the improved model attains a P of 93.1%, R of 89.5%, mAP@50 of 95.6%, and mAP@50–95 of 95.2%, corresponding to gains of 8.8%, 11.0%, 11.3%, and 27.9% over the original model. Further error analysis shows that picking point localization errors are concentrated within the 0–4-pixel range, demonstrating enhanced localization precision critical for practical harvesting applications. The proposed algorithm effectively addresses greenhouse environmental challenges and provides essential technical support for intelligent zucchini harvesting systems.