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ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1659781

This article is part of the Research TopicAI-Driven Plant Intelligence: Bridging Multimodal Sensing, Adaptive Learning, and Ecological Sustainability in Precision Plant ProtectionView all articles

Automated seed counting using image processing and deep learning

Provisionally accepted
Qiuyu  ZuQiuyu Zu1Teng  LiuTeng Liu2Wenpeng  ZhuWenpeng Zhu2Yan  PanYan Pan2Jinxu  WangJinxu Wang2Xinru  SongXinru Song2Jialin  YuJialin Yu2Shu  DangShu Dang1Xiaoming  YuXiaoming Yu1Zhenyu  ZhangZhenyu Zhang1*
  • 1Jilin Agricultural Science and Technology University, Jilin, China
  • 2Peking University Institute of Advanced Agricultural Sciences, Weifang, China

The final, formatted version of the article will be published soon.

Introduction: Accurate seed counting is an essential task in agricultural research and farming, supporting activities such as crop breeding, yield prediction, and weed management. Traditional manual seed counting, while accurate, is time-consuming, labor-intensive, and prone to human error, particularly for large quantities of micro-sized seeds. Methods: This study developed two automated computer vision approaches integrated into a mobile application (app) for seed counting: one utilizing image processing (IP) and the other based on deep learning (DL). These methods aim to address the limitations of traditional manual counting by providing automated, efficient alternatives. Results: The IP-based method demonstrated high accuracy comparable to manual counting and offered substantial time savings. However, its reliance on controlled environmental conditions, such as uniform lighting, limits its versatility for field apps. The DL-based method excelled in speed and scalability, processing counts in as little as 0.33 seconds per image, but its accuracy was inconsistent for visually complex or densely clustered seeds. Discussion: Both automated methods significantly enhance the efficiency of seed counting, providing a practical and accessible solution for various agricultural contexts. The integration of these methods into a mobile app streamlines seed counting for laboratory research, field studies, seed production, and breeding trials, offering a transformative approach to modernizing seed counting practices while reducing time and labor requirements.

Keywords: image processing, deep learning, YOLOv5, Mobile app, Seed counting

Received: 04 Jul 2025; Accepted: 08 Aug 2025.

Copyright: © 2025 Zu, Liu, Zhu, Pan, Wang, Song, Yu, Dang, Yu and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Zhenyu Zhang, Jilin Agricultural Science and Technology University, Jilin, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.