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2025 Vol.70, Issue 3 Preview Page

Original Research Article

Assessment of UAV-based RGB and Multispectral Imagery for Detecting Lodging in Wheat Fields

UAV기반 RGB 및 다중분광 영상자료를 활용한 밀 재배지 도복 탐지 평가

Hyun-Jin Jung1
,
Han-Yong Jeong2
,
Jinhee Park2
,
Yurim Kim2
,
Go Eun Lee2
,
Chuloh Cho2
,
Yurim Kim3
,
Sookjin Kim4*
정 현진1
,
정 한용2
,
박 진희2
,
김 유림2
,
이 고은2
,
조 철오2
,
김 유림3
,
김 숙진4*
1Post-doctoral Researcher, Winter Crop Research Division, National Institute of Crop Science, Rural Development Administration, Wanju 55365, Republic of Korea
2Researcher, Winter Crop Research Division, National Institute of Crop Science, Rural Development Administration, Wanju 55365, Republic of Korea
3Post-master Researcher, Winter Crop Research Division, National Institute of Crop Science, Rural Development Administration, Wanju 55365, Republic of Korea
4Senior Researcher, Winter Crop Research Division, National Institute of Crop Science, Rural Development Administration, Wanju 55365, Republic of Korea
1농촌진흥청 국립식량과학원, 맥류작물과 박사후연구원
2농촌진흥청 국립식량과학원, 맥류작물과 농업연구사
3농촌진흥청 국립식량과학원, 맥류작물과 석사후연구원
4농촌진흥청 국립식량과학원, 맥류작물과 농업연구관
*Corresponding Author
1 September 2025. pp. 171-184
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Abstract

Lodging in wheat fields is a major factor that reduces both yield and grain quality, including protein content. Rapid lodging detection can support timely management to minimize quality loss. In this study, we evaluates the potential of unmanned aerial vehicl (UAV)-based imagery combined with machine learning (ML) algorithms in lodging detection, and identified the optimal imagery-model combination for its application in wheat fields. We acquired RGB and multispectral (MS) UAV imagery from two experimental fields(one for model training and validation and another for independent testing) and used Random Forest (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbors (KNN) algorithms for lodging classification. The reesults from Field 1 demonstrated that MS imagery outperformed RGB across all ML algorithms, with the MS-XGB combination achieving the highest accuracy and stability. When applied to Field 2, the MS-XGB model generated prediction maps consistent with ground truth data. Adjusting the majority rule threshold to 5~10% improved prediction accuracy to 1.0 by reducing unrecognized lodging areas. This approach can serve as an early monitoring system to understand wheat quality (e.g., protein content)-affecting factors, by providing timely spatial information on lodging. Lodging detection results can also inform decision-making to maintain grain quality and improve production efficiency.

Keywords
lodging
multispectral
remote sensing
RGB
UAV
wheat
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Information
  • Publisher :The Korean Society of Crop Science
  • Publisher(Ko) :한국작물학회
  • Journal Title :The Korean Journal of Crop Science
  • Journal Title(Ko) :한국작물학회지
  • Volume : 70
  • No :3
  • Pages :171-184
  • Received Date : 2025-08-07
  • Revised Date : 2025-08-21
  • Accepted Date : 2025-08-22
  • DOI :https://doi.org/10.7740/kjcs.2025.70.3.171
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