MSDP-Net: A YOLOv5-Based Safflower Corolla Object Detection and Spatial Positioning Network

Hui Guo (), Haiyang Chen and Tianlun Wu
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Hui Guo: College of Electrical and Mechanical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Haiyang Chen: College of Electrical and Mechanical Engineering, Hunan Agricultural University, Changsha 410128, China
Tianlun Wu: College of Electrical and Mechanical Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2025, vol. 15, issue 8, 1-19

Abstract: In response to the challenge of low detection and positioning accuracy for safflower corollas during field operations, we propose a deep learning-based object detection and positioning algorithm called the Mobile Safflower Detection and Position Network (MSDP-Net). This approach is designed to overcome issues related to the small size of safflower corollas and their tendency to be occluded in complex agricultural environments. For object detection, we introduce an improved YOLO v5m model, referred to as C-YOLO v5m, which integrates a Convolutional Block Attention Module (CBAM) into both the backbone and neck networks. This modification enhances the model’s ability to focus on key features, resulting in increases in the precision, recall, and mean average precision of 4.98%, 4.3%, and 5.5%, respectively. For spatial positioning, we propose a mobile camera-based method in which a binocular camera is mounted on a translation stage, enabling horizontal movement that maintains optimal positioning accuracy and mitigates occlusion issues. Field experiments demonstrate that this mobile positioning method achieves a success rate of 93.79% with average deviations of less than 3 mm in the X, Y, and Z directions. Moreover, comparisons with five mainstream object detection algorithms reveal that MSDP-Net offers superior overall performance, making it highly suitable for safflower corolla detection. Finally, when applied to our self-developed safflower harvesting robot, 500 indoor trial tests achieved a harvest success rate of 90.20%, and field tests along a 15 m row confirmed a success rate above 90%, thereby validating the effectiveness of the proposed methods.

Keywords: harvesting robot; deep learning; object detection; spatial positioning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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