基于改进YOLOv7-tiny的草莓生长阶段检测研究

doi:10.19640/j.cnki.jtau.2026.01.012

Journal of Tianjin Agricultural University ›› 2026, Vol. 33 ›› Issue (1): 65-70.doi: 10.19640/j.cnki.jtau.2026.01.012

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Strawberry growth stage detection based on improved YOLOv7-tiny

Xu Xin¹, Jing Hanyu², Zhang Tianjian¹, Wang Li¹, Liu Jianbo^{1,Corresponding Author}

1. College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China;
2. School of Software, North University of China, Taiyuan 030051, China

Received:2024-07-11 Online:2026-02-28 Published:2026-02-05

Abstract

Abstract: In order to solve the problems of low accuracy of strawberry recognition and slow running speed of the model by the robot in the process of strawberry picking,and to reduce the size of the model to facilitate the mounting of the model, this study proposes a strawberry growth stage detection algorithm based on the improved YOLOv7-tiny model. Firstly, in order to improve the robustness of the model, data enhancement techniques are used to expand the original data; secondly, the K-Means++ algorithm is used to re-cluster the model’s anchors to obtain a more suitable field of view for strawberry growth stage detection; in order to improve the model accuracy, six SimAM lightweight attention mechanism modules are added under the original network framework; compared to the original model the accuracy was improved by 5 percentage points, and the running speed FPS was increased by 22.1, while the model size was not significantly improved, which is in line with the SimAM lightweight design.

Key words: strawberry, growth stage, YOLOv7-tiny, target detection

CLC Number:

S126
TP18

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URL: http://xuebao.tjau.edu.cn/EN/10.19640/j.cnki.jtau.2026.01.012

http://xuebao.tjau.edu.cn/EN/Y2026/V33/I1/65

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Strawberry growth stage detection based on improved YOLOv7-tiny

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