Research on Lightweight Algorithm Model for Precise Recognition and Detection of Outdoor Strawberries Based on Improved YOLOv5n

Xiaoman Cao, Peng Zhong, Yihao Huang, Mingtao Huang, Zhengyan Huang, Tianlong Zou and He Xing ()
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Xiaoman Cao: School of Mechanical and Electrical Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China
Peng Zhong: School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou 511363, China
Yihao Huang: School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou 511363, China
Mingtao Huang: School of Modern Information Industry, Guangzhou College of Commerce, Guangzhou 511363, China
Zhengyan Huang: School of Mechanical and Electrical Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou 510510, China
Tianlong Zou: Foshan Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics, Foshan 528000, China
He Xing: Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 1, 1-16

Abstract: When picking strawberries outdoors, due to factors such as light changes, obstacle occlusion, and small target detection objects, the phenomena of poor strawberry recognition accuracy and low recognition rate are caused. An improved YOLOv5n strawberry high-precision recognition algorithm is proposed. The algorithm uses FasterNet to replace the original YOLOv5n backbone network and improves the detection rate. The MobileViT attention mechanism module is added to improve the feature extraction ability of small target objects so that the model has higher detection accuracy and smaller module sizes. The CBAM hybrid attention module and C2f module are introduced to improve the feature expression ability of the neural network, enrich the gradient flow information, and improve the performance and accuracy of the model. The SPPELAN module is added as well to improve the model’s detection efficiency for small objects. The experimental results show that the detection accuracy of the improved model is 98.94%, the recall rate is 99.12%, the model volume is 53.22 MB, and the mAP value is 99.43%. Compared with the original YOLOv5n, the detection accuracy increased by 14.68%, and the recall rate increased by 11.37%. This technology has effectively accomplished the accurate detection and identification of strawberries under complex outdoor conditions and provided a theoretical basis for accurate outdoor identification and precise picking technology.

Keywords: strawberry recognition; YOLOv5n; deep learning; image processing (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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