文献类型： 外文期刊

作者： Yang, Fu-qin ¹ ; Chen, Ri-qiang ² ; Liu, Yang ² ; Chen, Xin-xin ¹ ; Xiao, Yi-bo ¹ ; Li, Chang-hao ¹ ; Wang, Ping ³ ; Feng, Hai-kuan ² ;

作者机构： 1.Henan Univ Engn, Coll Civil Engn, Zhengzhou 451191, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

3.Hebei Zhongbao Green Crop Technol Corp, Langfang 065000, Peoples R China

4.Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, Nanjing 210095, Peoples R China

关键词： Potato; Importance of variable projection; Aboveground biomass; Plant nitrogen content; UAV

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.8； 五年影响因子：0.7 ）

ISSN： 1000-0593

年卷期： 2025 年 45 卷 6 期

页码：

收录情况： SCI

摘要： Above-ground biomass and plant nitrogen content play a crucial role in crop growth, development, and yield formation. Therefore, dynamic monitoring of crop growth and nutritional status is of considerable importance. The study used unmanned aerial vehicles to obtain hyperspectral data and above-ground biomass during the budding stage, tuber formation stage, tuber growth and starch accumulation stage, to analyze the correlation and the importance of variable projection between vegetation indices and biomass and plant nitrogen content, and to screen out vegetation indices that are sensitive to biomass and plant nitrogen content combining deep neural network (DNN), partial least squares (PLSR), clastic network regression (ENR), ridge regression (RR) and support vector machine (SVR) to estimate biomass and plant nitrogen content and comparing the effectiveness of different models in estimating biomass and plant nitrogen content, The results showed that (1) the correlation between vegetation indices and both biomass and plant nitrogen content reached 0, 01 significant level, and the importance of the variable projection was used to screen out the vegetation indices that were sensitive to biomass and plant nitrogen content; (2) Comparing the remote sensing estimation models for the five growth stages, the best model for biomass and plant nitrogen content was constructed at the tuber formation stage, the worst model for biomass was estimated at the present bud stage, and the worst model for plant nitrogen content was estimated at the tuber growth stage. (3) The optimum biomass model constructed in the tuber formation stage using the PLSR method was modelled with R, RMSE and NRMSE was 0.60, 235.65 kg center dot hm(-2 )and 0. 15 kg hm respectively, and validated with R-2, RMSE and NRMSE was 0.58, 344.72 kg center dot hm(-2) and 0.26 kg center dot hm(-2), The optimum plant nitrogen content model constructed during tuber formation stage using RR method was modelled with R-2, RMSE and NRMSE was 0.74, 0.31% and 0.15%, validated R-2, RMSE and NRMSE was 0.77, 0.58% and 0.28%. Comprehensively comparing the DNN, PLSR, ENR, RR, and SVR algorithms for estimating biomass and plant nitrogen content models, the accuracy of the estimated plant nitrogen content model is found to be better than that of the estimated biomass model. The plant's nitrogen content can be used to more effectively monitor crop growth and nutritional characteristics, providing a reference for informed agricultural management.