Dual sampling linear regression ensemble to predict wheat yield across growing seasons with hyperspectral sensing

文献类型： 外文期刊

第一作者： Fei, Shuaipeng

作者： Fei, Shuaipeng;Xiao, Shunfu;Ma, Yuntao;Fei, Shuaipeng;Xiao, Yonggui;Zhu, Jinyu

作者机构：

关键词： Yield prediction; Ensemble modeling; Breeding; Remote sensing; Canopy reflectance

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 216 卷

页码：

收录情况： SCI

摘要： In the domain of crop yield assessment, the combination of remote sensing and advanced regression techniques has gained significant attention. This study focused on predicting wheat yield across growing seasons using hyperspectral reflectance data. The performance of two decision tree-based algorithms, deep forest (DF) and random forest (RF), as well as a novel ensemble method called dual sampling linear regression ensemble (DSLRE), was evaluated. Canopy hyperspectral reflectance data were collected at two stages of wheat growth: early grain filling (EGF) and mid-grain filling (MGF), in three distinct growing environments. Prediction accuracy was assessed using the Pearson correlation coefficient (PCC) and mean absolute error (MAE). The results showed that the DF algorithm outperformed the RF algorithm in most scenarios. In modeling framework A, DF achieved the highest accuracy at the MGF stage using truncated reflectance (PCC = 0.60, MAE = 0.82 t/ha). In modeling framework B, DF performed better than RF in three out of four conditions, while RF exhibited higher prediction accuracy during the EGF stage using raw reflectance (PCC = 0.59, MAE = 1.48 t/ha). Moreover, the DSLRE algorithm outperformed DF and RF in six of the eight modeling scenarios and demonstrated robustness in coping with noisy features. The heritability analysis indicated that DSLRE predictions effectively captured the effects of genetic factors on yield variation. These findings illustrate the significance of the proposed DSLRE algorithm in improving yield prediction, providing valuable guidance for effective crop yield management.

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