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Chlorophyll dynamic fusion based on high-throughput remote sensing and machine learning algorithms for cotton yield prediction

文献类型： 外文期刊

作者： Yang, Jiajie 1 ; Xu, Bowei 1 ; Wu, Bin 2 ; Zhao, Rumeng 1 ; Liu, Le 1 ; Li, Fuguang 1 ; Ai, Xiantao 4 ; Fan, Liqiang 1 ; Yang, Zuoren 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Cotton Res, State Key Lab Cotton Biobreeding & Integrated Util, Anyang 455000, Henan, Peoples R China

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

3.Xinjiang Normal Univ, Coll Life Sci, Xinjiang Key Lab Special Species Conservat & Regul, Urumqi 830017, Xinjiang, Peoples R China

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

关键词： Unmanned aerial vehicle; Multi-source remote sensing; Leaf chlorophyll content; Yield; Machine learning algorithms

期刊名称：FIELD CROPS RESEARCH （ 影响因子：6.4； 五年影响因子：6.6 ）

ISSN： 0378-4290

年卷期： 2025 年 333 卷

页码：

收录情况： SCI

摘要： In the development of precision agriculture, accurate monitoring of growth dynamics and yield prediction are important for optimizing the cotton production. However, the reliability of yield prediction for different cotton accessions based on monitoring chlorophyll dynamics using remote sensing techniques remains to be further improved. In this study, field images of 419 cotton accessions were collected at eight time points using an unmanned aerial vehicle (UAV) equipped with RGB and multispectral sensors. Four vegetation indices (VIs) and three color indices (CIs) highly correlated with leaf chlorophyll content (LCC) were screened by a hierarchical segmentation method. Subsequently, four machine learning algorithms were used to construct a chlorophyll prediction model. The random forest (RF) model outperformed the ridge regression (RR), support vector machine (SVM), and partial least squares regression (PLSR) models in predicting the LCC with the best accuracy (R2 = 0.827) after fusing the VIs and CIs. Yield prediction was then performed using multiple linear regression, with LCC data as predictors from the squaring, flowering, boll development, and boll opening stages (as well as a combined multi-stage input). The model using multi-stage LCC data achieved the highest accuracy (R2 = 0.723). Finally, the K-means algorithm was successfully used to demonstrate a strong association between LCC profiles and yield outcomes of 419 cotton accessions. This study fused RGB and multispectral data to predict multiplestage LCC data and construct a yield prediction model, which was innovatively applied to 419 cotton accessions with rich genetic diversity for the first time. It is superior to methods relying on a single remote sensing dataset or applied to a few genetic materials, providing an innovative method for large-scale phenotypic analysis of cotton varieties and a scientific basis for promoting cotton germplasm screening and precision breeding.

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