SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment

These findings indicate that SoyCountNet offers a reliable and scalable solution for precise soybean seed counting and localization in complex field environments. Its lightweight architecture allows deployment on intelligent agricultural platforms, supporting high-throughput phenotyping, yield prediction, and precision breeding, while providing a foundation for the future development of intelligent and sustainable agricultural technologies.

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Publication Details

Journal
Frontiers in Plant Science
Published
2026-02-25
DOI
https://doi.org/10.3389/fpls.2026.1743104
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
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article

SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment

Z. Wang, zhongzhi Han, Haoyu Wang, Shudong Wang et al.
Frontiers in Plant Science
Smart Agriculture and AI
article

SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment

Z. Wang, zhongzhi Han, Haoyu Wang, Shudong Wang, Longgang Zhao, Fei Liu, Hexiang Luan, Qiong Wu
article en

Abstract

These findings indicate that SoyCountNet offers a reliable and scalable solution for precise soybean seed counting and localization in complex field environments. Its lightweight architecture allows deployment on intelligent agricultural platforms, supporting high-throughput phenotyping, yield prediction, and precision breeding, while providing a foundation for the future development of intelligent and sustainable agricultural technologies.

Frontiers in Plant ScienceVol. 17
Qingdao Agricultural University (CN), Shandong Academy of Agricultural Sciences (CN), Institute of Software (CN), China University of Petroleum, East China (CN)
Life in Land
Openalex Percentile: Top 45%
Smart Agriculture and AI
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