ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Technical Advances in Plant Science

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1582303

This article is part of the Research TopicMachine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume IIView all 27 articles

Comparative Analysis of Adaptive and General Labeling Methods for Soybean Leaf Detection

Provisionally accepted
Yuseok JeongYuseok Jeong1Song Lim KimSong Lim Kim2Thanh Tuan ThaiThanh Tuan Thai3,4Anh Tuan LeAnh Tuan Le5Chaewon LeeChaewon Lee2Hyo Jun BaeHyo Jun Bae2Inchan ChoiInchan Choi2Sheikh MansoorSheikh Mansoor4Yong Suk ChungYong Suk Chung4*Kyung-Hwan KimKyung-Hwan Kim2
  • 1Department of Agricultural Engineering, National Institute of Agricultural Science (South Korea), Wanju, Republic of Korea
  • 2Rural Development Administration (South Korea), Jeonju, North Jeolla, Republic of Korea
  • 3Communication Multimedia Laboratory, University of Information Technology, Ho Chi Minh, Ho Chi Minh, Vietnam
  • 4Jeju National University, Jeju City, Jeju, Republic of Korea
  • 5Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

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Soybeans are important due to their nutritional benefits, economic role, agricultural contributions, and various industrial applications. Effective leaf detection plays a crucial role in analyzing soybean growth within precision agriculture. This study examines the influence of different labeling methods on the efficiency of artificial intelligence (AI) based soybean leaf detection. We compare a traditional general labeling technique against a new context-aware method that utilizes information about leaf length and bottom extremities. Both approaches were employed to train a YOLOv5L deep learning model using high-resolution soybean imagery. Results show that the general labeling method excelled with soybean varieties that have wider internodes and distinctly separated leaves. In contrast, the context-aware labeling method outperformed the general approach for medium soybean varieties characterized by narrower internodes and overlapping leaves. By optimizing labeling strategies, the accuracy and efficiency of AI-based soybean growth analysis can be significantly improved, particularly in high-throughput phenotyping systems. Ultimately, the findings suggest that a thoughtful approach to labeling can enhance agricultural management practices, contributing to better crop monitoring and improved yields.

Keywords: deep learning, image analysis, object detection, phenotyping, crop management

Received: 24 Feb 2025; Accepted: 15 May 2025.

Copyright: © 2025 Jeong, Kim, Thai, Le, Lee, Bae, Choi, Mansoor, Chung and Kim. 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: Yong Suk Chung, Jeju National University, Jeju City, 690-756, Jeju, Republic of Korea

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.

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