Scholarlink

<p>Chili seed germination potential is a significant element influencing planting yield. Many researchers focus on feature extraction analysis methods to detect undesirable seed germination characteristics and improve classification accuracy. In deep learning, data collection and model complexity often limit practical application in production environments. This work includes a dataset of macroscopic images of chili seeds. This paper proposes a lightweight model GroupCeptionNet to process macroscopic images for screening non-germinated chili seeds before planting. Experimental results show that GroupCeptionNet achieves 94.66% accuracy and 94.65% F1-score under 1.40&#xa0;M parameters, outperforming the classical CNN and Transformer models. We also explore the impact of background removal and GroupCeptionNet ablation and variant structures. The visualization results further validate the consistency of the model’s focus areas with human-annotated regions. The proposed dataset and model provide technical references for a low-cost and efficient chili seed screening process.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GroupCeptionNet: a lightweight model for classifying chili seed germination with macro images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月2日

查看全文
  • Changyu Ao,
  • Tianpei Xu

摘要

Chili seed germination potential is a significant element influencing planting yield. Many researchers focus on feature extraction analysis methods to detect undesirable seed germination characteristics and improve classification accuracy. In deep learning, data collection and model complexity often limit practical application in production environments. This work includes a dataset of macroscopic images of chili seeds. This paper proposes a lightweight model GroupCeptionNet to process macroscopic images for screening non-germinated chili seeds before planting. Experimental results show that GroupCeptionNet achieves 94.66% accuracy and 94.65% F1-score under 1.40 M parameters, outperforming the classical CNN and Transformer models. We also explore the impact of background removal and GroupCeptionNet ablation and variant structures. The visualization results further validate the consistency of the model’s focus areas with human-annotated regions. The proposed dataset and model provide technical references for a low-cost and efficient chili seed screening process.