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Article

Study on Cherry Blossom Detection and Pollination Parameter Optimization Using the SMD-YOLO Model

1
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian 271018, China
2
Shandong Key Laboratory of Intelligent Production Technology and Equipment for Facility Horticulture, Taian 271018, China
3
Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
4
Shandong Academy of Agricultural Machinery Sciences, Jinan 250100, China
5
Huang Huai Hai Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Jinan 250100, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1915; https://doi.org/10.3390/agronomy15081915
Submission received: 11 July 2025 / Revised: 4 August 2025 / Accepted: 7 August 2025 / Published: 8 August 2025

Abstract

In response to the need for precise blossom identification and optimization of key operational parameters in intelligent cherry spraying pollination, the SMD-YOLO (You Only Look Once with spatial and channel reconstruction convolution, multi-scale channel attention, and dual convolution modules) cherry blossom detection model is proposed, along with a pollination experiment platform for parameter optimization. The SMD-YOLO model, built upon YOLOv11, enhances feature extraction through the ScConvC3k2 (spatial and channel reconstruction convolution C3k2) module, incorporates the MSCA (multi-scale channel attention) attention mechanism, and employs the DualConv module for a lightweight design, ensuring both detection accuracy and operational efficiency. Tested on a self-constructed cherry blossom dataset, the model delivered a precision of 87.6%, a recall rate of 86.1%, and an mAP(mean average precision) reaching 93.1% with a compact size of 4765 KB, 2.28 × 106 parameters, a computational cost of 5.8 G, and a detection speed of 75.76 FPS, demonstrating strong practicality and potential for embedded real-time detection in edge devices, such as cherry pollination robots. To further enhance pollination effectiveness, a dedicated pollination experiment bench was designed, and a second-order orthogonal rotational combination experiment method was employed to systematically optimize three key parameters: spraying distance, spraying time, and liquid flow rate. Experimental results indicate that the optimal pollination effect occurs when the spraying distance is 3.4 cm, spraying time is 1.9 s, and liquid flow rate is 339 mL/min, with a deposition amount of 0.18 g and a coverage rate of 97.25%. This study provides a high-precision image detection algorithm and operational parameter optimization basis for intelligent and precise cherry blossom pollination.
Keywords: deep learning; cherry blossom; object detection; pollination experiment bench; parameter optimization deep learning; cherry blossom; object detection; pollination experiment bench; parameter optimization

Share and Cite

MDPI and ACS Style

Ren, L.; Du, Y.; Li, Y.; Gao, A.; Ma, W.; Song, Y.; Han, X. Study on Cherry Blossom Detection and Pollination Parameter Optimization Using the SMD-YOLO Model. Agronomy 2025, 15, 1915. https://doi.org/10.3390/agronomy15081915

AMA Style

Ren L, Du Y, Li Y, Gao A, Ma W, Song Y, Han X. Study on Cherry Blossom Detection and Pollination Parameter Optimization Using the SMD-YOLO Model. Agronomy. 2025; 15(8):1915. https://doi.org/10.3390/agronomy15081915

Chicago/Turabian Style

Ren, Longlong, Yonghui Du, Yuqiang Li, Ang Gao, Wei Ma, Yuepeng Song, and Xingchang Han. 2025. "Study on Cherry Blossom Detection and Pollination Parameter Optimization Using the SMD-YOLO Model" Agronomy 15, no. 8: 1915. https://doi.org/10.3390/agronomy15081915

APA Style

Ren, L., Du, Y., Li, Y., Gao, A., Ma, W., Song, Y., & Han, X. (2025). Study on Cherry Blossom Detection and Pollination Parameter Optimization Using the SMD-YOLO Model. Agronomy, 15(8), 1915. https://doi.org/10.3390/agronomy15081915

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