Detection of Aging Maize Seed Vigor and Calculation of Germ Growth Speed Using an Improved YOLOv8-Seg Network

Helong Yu, Xi Ling, Zhenyang Chen, Chunguang Bi () and Wanwu Zhang ()
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Helong Yu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Xi Ling: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Zhenyang Chen: Smart Agriculture Research Institute, Jilin Agricultural University, Changchun 130118, China
Chunguang Bi: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Wanwu Zhang: Jilin Rural Economy Information Center, Changchun 130000, China

Agriculture, 2025, vol. 15, issue 3, 1-25

Abstract: Crop yields are influenced by various factors, including seed quality and environmental conditions. Detecting seed vigor is a critical task for seed researchers, as it plays a vital role in seed quality assessment. Traditionally, this evaluation was performed manually, which is time-consuming and labor-intensive. To address this limitation, this study integrates the ConvUpDownModule (a customized convolutional module), C2f-DSConv(C2f module with Integrated Dynamic Snake Convolution), and T-SPPF (the SPPF module integrated with the transformer multi-head attention mechanism) into the VT-YOLOv8-Seg network (the improved YOLOv8-Seg Network), an enhancement of the YOLOv8-Seg architecture. The ConvUpDownModule reduces the computational complexity and model parameters. The C2f-DSConv leverages flexible convolutional kernels to enhance the accuracy of maize germ edge segmentation. The T-SPPF integrates global information to improve multi-scale segmentation performance. The proposed model is designed for detecting and segmenting maize seeds and germs, facilitating seed germination detection and germination speed computation. In detection tasks, the VT-YOLOv8-Seg model achieved 97.3% accuracy, 97.9% recall, and 98.5% mAP50, while in segmentation tasks, it demonstrated 97.2% accuracy, 97.7% recall, and 98.2% mAP50. Comparative experiments with Mask R-CNN, YOLOv5-Seg, and YOLOv7-Seg further validated the superior performance of our model in both detection and segmentation. Additionally, the impact of seed aging on maize seed growth and development was investigated through artificial aging studies. Key metrics such as germination rate and germ growth speed, both closely associated with germination vigor, were analyzed, demonstrating the effectiveness of the proposed approach for seed vigor assessment.

Keywords: seed vigor detection; germination rate; YOLOv8-Seg; artificial seed aging; computer vision in agriculture (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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