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<p>Understanding dietary preferences in ancient societies is essential for revealing human–environment interactions. Seeds are key archaeological artifacts in archaeobotanical research, yet their identification still relies on expert knowledge, making large-scale analysis inefficient. Although analytical methods in archaeology have advanced, data and methodological gaps persist, particularly in the classification of ancient plant seeds. To address this issue, we construct the first Ancient Plant Seed Image Classification (APS) dataset, containing 8340 images from 17 genus- or species-level categories excavated from 18 archaeological sites across China. We further propose APSNet, a classification framework for ancient plant seeds. APSNet introduces seed scale information via a Size Perception and Embedding (SPE) module to complement fine-grained features, and employs an Asynchronous Decoupled Decoding (ADD) architecture to learn discriminative features from channel and spatial perspectives. Experiments show APSNet outperforms state-of-the-art methods, achieving 90.2% accuracy and providing an effective tool for archaeological research.</p>

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Towards ancient plant seed classification: a benchmark dataset and baseline model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

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  • Rui Xing,
  • Runmin Cong,
  • Yingying Wu,
  • Can Wang,
  • Zhongming Tang,
  • Fen Wang,
  • Hao Wu,
  • Sam Kwong

摘要

Understanding dietary preferences in ancient societies is essential for revealing human–environment interactions. Seeds are key archaeological artifacts in archaeobotanical research, yet their identification still relies on expert knowledge, making large-scale analysis inefficient. Although analytical methods in archaeology have advanced, data and methodological gaps persist, particularly in the classification of ancient plant seeds. To address this issue, we construct the first Ancient Plant Seed Image Classification (APS) dataset, containing 8340 images from 17 genus- or species-level categories excavated from 18 archaeological sites across China. We further propose APSNet, a classification framework for ancient plant seeds. APSNet introduces seed scale information via a Size Perception and Embedding (SPE) module to complement fine-grained features, and employs an Asynchronous Decoupled Decoding (ADD) architecture to learn discriminative features from channel and spatial perspectives. Experiments show APSNet outperforms state-of-the-art methods, achieving 90.2% accuracy and providing an effective tool for archaeological research.