Detection of Seed Potato Sprouts Based on Improved YOLOv8 Algorithm

Yufei Li, Qinghe Zhao, Zifang Zhang, Jinlong Liu and Junlong Fang ()
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Yufei Li: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Qinghe Zhao: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Zifang Zhang: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Jinlong Liu: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China
Junlong Fang: Electrical Engineering and Information College, Northeast Agricultural University, Harbin 150030, China

Agriculture, 2025, vol. 15, issue 9, 1-22

Abstract: Seed potatoes without sprouts usually need to be manually selected in mechanized production, which has been the bottleneck of efficiency. A fast and efficient object recognition algorithm is required for the additional removal process to identify unqualified seed potatoes. In this paper, a lightweight deep learning algorithm, YOLOv8_EBG, is proposed to both improve the detection performance and reduce the model parameters. The ECA attention mechanism was introduced in the backbone and neck of the model to more accurately extract and fuse sprouting features. To further reduce the model parameters, Ghost convolution and C3ghost were introduced to replace the normal convolution and C2f blocks in vanilla YOLOv8n. In addition, a bi-directional feature pyramid network is integrated in the neck part for multi-scale feature fusion to enhance the detection accuracy. The experimental results from an isolated test dataset show that the proposed algorithm performs better in detecting sprouts under natural light conditions, achieving an mAP 0.5 of 95.7% and 91.9% AP for bud recognition. Compared to the YOLOv8n model, the improved model showed a 6.5% increase in mAP 0.5 , a 12.9% increase in AP 0.5 for bud recognition, and a 5.6% decrease in the number of parameters. Additionally, the improved algorithm was applied and tested on mechanized sorting equipment, and the accuracy of seed potato detection was as high as 92.5%, which was sufficient to identify and select sprouted potatoes, an indispensable step since only sprouted potatoes can be used as seed potatoes. The results of the study can provide technical support for subsequent potato planting intelligence.

Keywords: seed potato; objects detection algorithm; attention mechanism; lightweight network; Bidirectional Feature Pyramid Network (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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