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UAV-based LiDAR and multispectral sensors fusion for cotton yield estimation: Plant height and leaf chlorophyll content as a bridge linking remote sensing data to yield

文献类型： 外文期刊

作者： Wu, Bin 1 ; Fan, Liqiang 2 ; Xu, Bowei 2 ; Yang, Jiajie 2 ; Zhao, Rumeng 2 ; Wang, Qiong 4 ; Ai, Xiantao 5 ; Zhao, Huixin 1 ; Yang, Zuoren 2 ;

作者机构： 1.Xinjiang Normal Univ, Coll Life Sci, Xinjiang Key Lab Special Species Conservat & Regul, Urumqi 830017, Peoples R China

2.Chinese Acad Agr Sci, Inst Cotton Res, State Key Lab Cotton Biobreeding & Integrated Util, Anyang 455000, Henan, Peoples R China

3.Inst Western Agr CAAS, Xinjiang Key Lab Crop Gene Editing & Germplasm Inn, Changji 831100, Xinjiang, Peoples R China

4.Xinjiang Acad Agr & Reclamat Sci, Northwest Inland Reg Key Lab Cotton Biol & Genet B, Cotton Res Inst, Minist Agr, Urumqi, Xinjiang, Peoples R China

5.Xinjiang Univ, Res Inst, Coll Smart Agr, Urumqi 830046, Xinjiang, Peoples R China

6.Xinjiang Agr Univ, Coll Agr, Engn Res Ctr Cotton, Minist Educ, 311 Nongda East Rd, Urumqi 830052, Peoples R China

关键词： Cotton; Yield estimation; UAV; Data fusion; Machine learning

期刊名称：INDUSTRIAL CROPS AND PRODUCTS （ 影响因子：6.2； 五年影响因子：6.2 ）

ISSN： 0926-6690

年卷期： 2025 年 230 卷

页码：

收录情况： SCI

摘要： Accurate crop yield prediction is essential for enhancing agricultural sustainability and guiding economic policy decisions. It is effective to fuse multi-source remote sensing data to predict crop yields, but difficult to reveal the effects of physiological processes on yield estimation models, and challenging to guide crop field production and management. In this study, an innovative framework was introduced to construct plant height (PH) and leaf chlorophyll content (LCC) inversion models for UAV LiDAR and multispectral data through different strategies. PH and LCC, two key growth features affecting cotton yield, were evaluated using multiple linear regression (MLR), partial least squares regression (PLSR), and extreme gradient boosting (XGBoost) algorithms for singlefeature and multi-feature fusion, respectively. The multi-feature fusion model based on the XGBoost algorithm was significantly better than the single-feature model (R2=0.744). Further optimization of the multi-feature fusion model revealed that multi-temporal growth features as input variables significantly improved the accuracy of the multi-feature fusion model compared with that based on single-temporal (R2=0.802). Shapley additive explanations (SHAP) analysis revealed the key contribution of LCC to yield formation at the flowering and boll development stage in different cotton varieties. Cluster analysis confirmed that the dynamic trends of PH and LCC were closely related to yield, indicating that PH and LCC could be used as a bridge between remote sensing data and yield. This study highlights the value of UAV-based multi-dimensional and multi-temporal data fusion of growth features in yield estimation models, enabling a deeper understanding of yield formation mechanisms and providing novel methodological tools for phenomics research and precision agriculture management.

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