Comparison of multi-model fusion and transfer strategies for wheat yield comprehensive estimation under lodging stress from lodging parameters and multi-source remote sensing data

doi:10.1016/j.jia.2025.06.013

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Yongkang Wei¹^,⁴, Shaohua Zhang¹, Ke Wu¹, Yahui Li¹, Ziheng Feng¹, Haiyan Zhang¹^,², Li He¹^,², Jianzhao Duan¹^,², Yonghua Wang¹^,², Binbin Guo³, Yongchao Tian⁴, Wei Feng¹^,^2#

¹Agronomy College of Henan Agriculture University/State Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450046, China

²National Wheat Technology Innovation Center, Zhengzhou 450046, China

³College of Agronomy, Henan University of Science and Technology, Luoyang 471023, China

⁴National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China

Highlights

Ÿ The incorporation of the lodging index improves the prediction of wheat yield under lodging stress

Ÿ Stacking ensemble learning model best estimates wheat yield

Ÿ The combination of Balanced distribution adaptation and model updates can effectively improve model transfer performance

Abstract
References

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摘要

小麦产量估测关系到人民生活质量和国家粮食安全，特别是受到倒伏胁迫后，小麦光合及成熟进程受到严重阻碍，造成产量损失，快速准确估产对灾情掌握及后续农业管理具有重要意义。本研究使用光谱、结构和温度数据，综合分析多源数据在两种情景下的差异表现，筛选较好表征胁迫条件下小麦产量的差异性参数，同时探讨引入特异性参数（倒伏指数）后， Ridge Regression (RR)， Random Forest Regression (RFR)， K-Nearest Neighbors (KNN)，XGBoost (eXtreme Gradient Boosting), Support Vector Regression (SVR)及 Stacking ensemble learning (SEL)6种小麦产量估计模型的预测精度变化。与传统模型相比，SEL在倒伏后3天（R²=0.64）、12天（R²=0.70）和综合多时序特征（R²=0.73）平均预测精度最高，同时引入倒伏指数后，各模型预测精度均得到不同程度的提升，SEL模型平均R²分别提升8.19%，5.09%和6.17%。结合迁移学习方法Transfer Component Analysis(TCA), Joint Distribution Adaptation(JDA), Balanced distribution adaptation(BDA)，在仅补充4%目标数据集的情况下均保持稳定的精度,使模型得到较高的迁移预测效果（R²=0.81）。通过优化倒伏胁迫情景下的数据集，并结合集成学习和迁移学习技术，有效提高了倒伏胁迫下小麦产量估测模型的准确性、稳定性及迁移能力，为小麦灾情评估及补救措施制定提供参考。

Abstract

Estimating wheat yield is crucial for the quality of life and food security of a nation, particularly when crops face lodging stress, which severely hinders photosynthesis and maturation, resulting in yield loss. Rapid and accurate yield estimation is essential for disaster assessment and subsequent agricultural management. This study utilizes spectral, structural, and temperature data to comprehensively analyze the differential performance of multi-source data under two scenarios, selecting parameters that effectively represent yield differences under stress conditions. It also explores how the introduction of a specific parameter (the lodging index) affects the prediction accuracy of six wheat yield estimation models: eXtreme Gradient Boosting (XGBoost), Ridge Regression (RR), Random Forest Regression (RFR), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Stacking Ensemble Learning (SEL). Compared with traditional models, the SEL model had the highest average prediction accuracy at 3 days after lodging (R²=0.64), 12 days after lodging (R²=0.70), and with combined multi-temporal features (R²=0.73). With the introduction of the lodging index, the prediction accuracy of all models improved to different degrees, and the SEL model showing an average R² increase of 8.19, 5.09, and 6.17%, respectively. Combined with the transfer learning methods such as transfer component analysis (TCA), joint distribution adaptation (JDA), and balanced distribution adaptation (BDA), the model maintained stable accuracy even with only 4% of the target dataset supplemented, achieving a high transfer prediction performance (R²=0.81). By optimizing the dataset under lodging stress scenarios and integrating ensemble learning and transfer learning techniques, the accuracy, stability, and transferability of wheat yield estimation models under lodging stress were effectively improved, providing a reference for wheat disaster assessment and the formulation of remedial measures.

Keywords: yield wheat lodging multi-model fusion transfer strategies

Online: 09 June 2025

Fund:

This work was supported by grants from the Opening Foundation of National Key Laboratory of Crop Science on Wheat and Maize (SKL2022KF03), Henan Agricultural University Science and Technology Collaborative Innovation Project (30501534), and the National Agriculture Technology Research System of China (CARS-03-01-22).

About author: #Correspondence Wei Feng, Tel/Fax: +86-371-56990188, E-mail: fengwei78@126.com

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Yongkang Wei, Shaohua Zhang, Ke Wu, Yahui Li, Ziheng Feng, Haiyan Zhang, Li He, Jianzhao Duan, Yonghua Wang, Binbin Guo, Yongchao Tian, Wei Feng. 2025. Comparison of multi-model fusion and transfer strategies for wheat yield comprehensive estimation under lodging stress from lodging parameters and multi-source remote sensing data. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2025.06.013

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