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Estimation of wheat biomass based on phenological identification and spectral response

文献类型： 外文期刊

作者： Liu, Tao 1 ; Yang, Tianle 1 ; Zhu, Shaolong 1 ; Mou, Nana 1 ; Zhang, Weijun 1 ; Wu, Wei 4 ; Zhao, Yuanyuan 1 ; Yao, Zhaosheng 1 ; Sun, Jianjun 6 ; Chen, Chen 7 ; Sun, Chengming 1 ; Zhang, Zujian 1 ;

作者机构： 1.Yangzhou Univ, Agr Coll, Jiangsu Key Lab Crop Genet & Physiol, Jiangsu Key Lab Crop Cultivat & Physiol, Yangzhou 225009, Peoples R China

2.Yangzhou Univ, Jiangsu Coinnovat Ctr Modern Prod Technol Grain Cr, Yangzhou 225009, Peoples R China

3.Yangzhou Univ, Inst Smart Agr, Coll Agr, Yangzhou 225009, Peoples R China

4.Chinese Acad Agr Sci, Key Lab Agr Blockchain Applicat, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China

5.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

6.Henan Acad Agr Sci, Cereal Inst, Xinxiang 453000, Peoples R China

7.Zhenjiang Agr Sci Res Inst, Jiangsu Hilly Area, Jurong 212400, Peoples R China

关键词： Above ground biomass; Hyperspectral imaging; Phenological Identification; Deep learning; Wheat

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 222 卷

页码：

收录情况： SCI

摘要： Conventional models for estimating crop biomass are influenced by the wheat fertility process and often result in large errors, limiting the accurate crop biomass monitoring throughout the reproductive period. In this study, we developed a ResNet-Wheat model to extract phenological information from wheat and used this information to establish an aboveground biomass (AGB) estimation model. Using the spectral information obtained from unmanned aerial vehicles (UAVs), we inverted the coefficients (k, b) of the biomass estimation model to construct a new wheat biomass estimation model (FIWheat-AGB). The ResNet-Wheat model achieved overall recognition accuracy of 94.4 % at various phenological stages, proving it to be a dependable, accurate source of fertility period index (FI) for the FIWheat-AGB model. By combining the Lasso model with six vegetation indexes (VIs) with strong correlations with AGB, we could invert the biomass coefficients (k, b) with an R2 above 0.86 in all five phenological periods. The RMSE and MAE also remained stable. The FIWheat-AGB model utilized phenological information and VIs, leading to a high degree of precision in predicting AGB with an R2 range of 0.84-0.91, maintaining RMSE at 1.69-2.11 t/ha, and consistently maintaining an MAE of less than 1 t/ha over several periods. Thus, compared with the multi -period segmentation model (with an R2 range of 0.57-0.75) that is directly monitored by spectra, the suggested model is better suited for monitoring the entire wheat phenological period, and it provides a higher accuracy in estimating whole fertility biomass compared with the common spectral biomass estimation model and the crop simulation model. The proposed techniques can aid in estimating agronomic parameters in other crops throughout the fertility period.

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