BSS-YOLO: honey peach ripeness detection in complex orchard scenes under simulated adverse-weather conditions and its implications for fruit industry management
4 affiliations
- 1College of Artificial Intelligence, Zhejiang A&F University
- 2School of Information and Communication Engineering, Hainan University
- 3Jiyang College, Zhejiang A&F University
- 4College of Economics and Management, Zhejiang A&F University
Abstract
Accurate honey peach ripeness detection in natural orchards remains challenging because complex orchard factors, including strong illumination, fruit occlusion and overlap, target-scale variation, and weather-related image degradation such as haze and rainfall, can degrade image quality and weaken the visual discrimination among different ripeness stages. To improve ripeness detection under such interference, this study proposes an improved YOLOv12s-based method termed BiFPN–SCSA–SD-DIoU YOLO (BSS-YOLO). First, a multi-meteorological honey peach ripeness data set is constructed by combining field-collected orchard images with physics-driven haze and rainfall simulation, providing diverse weather-degraded samples for model training and evaluation. Second, a Spatial-Channel Synergistic Attention (SCSA) mechanism is embedded into the backbone network to strengthen the representation of ripeness-related regions while suppressing weather-induced background interference. Third, a Weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced into the neck to enhance adaptive multi-scale feature fusion for peaches with different sizes and occlusion levels. Furthermore, a scale-dynamic distance intersection over union (SD-DIoU) loss function is adopted to improve bounding box regression stability for multi-scale targets under the evaluated simulated fog- and rain-degraded conditions. Experimental results show that BSS-YOLO achieves 93.4% mAP50 and 85.0% mAP50–95 on the constructed multi-meteorological dataset containing original sunny-weather images and simulated fog and rain images, with only 8.57 million parameters and an inference speed of 63.5 FPS. Compared with the YOLOv12s baseline, the proposed method improves mAP50 and mAP50–95 by 2.0 and 1.9 percentage points, respectively, while reducing the parameter count from 9.07M to 8.57M. Moreover, compared with the recently proposed peach-related EMA-YOLO model, BSS-YOLO achieves higher mAP50 and mAP50-95, showing higher detection accuracy on the evaluated dataset. These results indicate that the proposed method has potential for honey peach ripeness monitoring under the evaluated weather-related degradation conditions, and may provide auxiliary support for automated fruit sorting, harvest-related decision-making, and operational efficiency improvement in fruit industry management.
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BibTeX
@article{tian2026bssyolo,
title = {{BSS-YOLO: honey peach ripeness detection in complex orchard scenes under simulated adverse-weather conditions and its implications for fruit industry management}},
author = {Junwei Tian and Xiaoye Zhang and Feipeng Gao and Jie Yang and Cong Zhou},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1929998},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1929998/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - BSS-YOLO: honey peach ripeness detection in complex orchard scenes under simulated adverse-weather conditions and its implications for fruit industry management
AU - Junwei Tian
AU - Xiaoye Zhang
AU - Feipeng Gao
AU - Jie Yang
AU - Cong Zhou
PY - 2026
DA - 2026/10/06
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1929998
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1929998/full
AB - Accurate honey peach ripeness detection in natural orchards remains challenging because complex orchard factors, including strong illumination, fruit occlusion and overlap, target-scale variation, and weather-related image degradation such as haze and rainfall, can degrade image quality and weaken the visual discrimination among different ripeness stages. To improve ripeness detection under such interference, this study proposes an improved YOLOv12s-based method termed BiFPN–SCSA–SD-DIoU YOLO (BSS-YOLO). First, a multi-meteorological honey peach ripeness data set is constructed by combining field-collected orchard images with physics-driven haze and rainfall simulation, providing diverse weather-degraded samples for model training and evaluation. Second, a Spatial-Channel Synergistic Attention (SCSA) mechanism is embedded into the backbone network to strengthen the representation of ripeness-related regions while suppressing weather-induced background interference. Third, a Weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced into the neck to enhance adaptive multi-scale feature fusion for peaches with different sizes and occlusion levels. Furthermore, a scale-dynamic distance intersection over union (SD-DIoU) loss function is adopted to improve bounding box regression stability for multi-scale targets under the evaluated simulated fog- and rain-degraded conditions. Experimental results show that BSS-YOLO achieves 93.4% mAP50 and 85.0% mAP50–95 on the constructed multi-meteorological dataset containing original sunny-weather images and simulated fog and rain images, with only 8.57 million parameters and an inference speed of 63.5 FPS. Compared with the YOLOv12s baseline, the proposed method improves mAP50 and mAP50–95 by 2.0 and 1.9 percentage points, respectively, while reducing the parameter count from 9.07M to 8.57M. Moreover, compared with the recently proposed peach-related EMA-YOLO model, BSS-YOLO achieves higher mAP50 and mAP50-95, showing higher detection accuracy on the evaluated dataset. These results indicate that the proposed method has potential for honey peach ripeness monitoring under the evaluated weather-related degradation conditions, and may provide auxiliary support for automated fruit sorting, harvest-related decision-making, and operational efficiency improvement in fruit industry management.
SN - 1664-462X
ER -