文献类型： 外文期刊

作者： Feng, Haikuan ¹ ; Fan, Yiguang ¹ ; Yue, Jibo ² ; Bian, Mingbo ¹ ; Liu, Yang ¹ ; Chen, Riqiang ¹ ; Ma, Yanpeng ¹ ; Fan, Jiejie ¹ ; Yang, Guijun ¹ ; Zhao, Chunjiang ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Minist Agr & Rural Affairs, Key Lab Quantitat Remote Sensing Agr, Beijing 100097, Peoples R China

2.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

3.Nanjing Agr Univ, Coll Agr, Nanjing 210095, Jiangsu, Peoples R China

关键词： Hyperspectral; Above-ground biomass; Potato; Deep learning; Leaf area index

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 232 卷

页码：

收录情况： SCI

摘要： Accurate estimation of above-ground biomass (AGB) in potato plants is essential for effective monitoring of potato growth and reliable yield prediction. Remote sensing technology has emerged as a promising method for monitoring crop growth parameters due to its high throughput, non-destructive nature, and rapid acquisition of information. However, the sensitivity of remote sensing vegetation indices to crop AGB parameters declines at moderate to high crop coverage, known as the "saturation phenomenon," which limits accurate AGB monitoring during the mid-to-late growth stages. This challenge also hinders the development of a multi-growth-cycle AGB estimation model. In this study, a novel VGC-AGB model integrated with hyperspectral remote sensing was utilized for multi-stage estimation of potato AGB. This study consists of three main components: (1) addressing the "saturation problem" encountered when using spectral indices from remote sensing to monitor crop biomass across multiple growth stages. The VGC-AGB model calculates the leaf biomass by multiplying leaf dry mass content (Cm) and leaf area index (LAI) and vertical organ biomass using the multiplication of crop density (Cd), crop height (Ch) and the crop stem and reproductive organs' average dry mass content (Csm); (2) estimating the VGC-AGB model parameters Cm and LAI by integrating hyperspectral remote sensing data with a deep learning model; (3) comparing the performance of three methods-(i) hyperspectral + Ch, (ii) ground-measured parameters + VGC-AGB model, and (iii) hyperspectral remote sensing + VGC-AGB model-using a five-year dataset of potato above-ground biomass. Results indicate that (1) the VGC-AGB model achieved high accuracy in estimating AGB (R2 = 0.853, RMSE = 751.12 kg/ha), significantly outperforming the deep learning model based on hyperspectral + Ch data (R2 = 0.683, RMSE = 1122.03 kg/ha); (2) the combination of the VGC-AGB model and hyperspectral remote sensing provided highly accurate results in estimating AGB (R2 = 0.760, RMSE = 965.59 kg/ha), surpassing the results obtained using the hyperspectral + Ch-based method. Future research will primarily focus on streamlining the acquisition of VGC-AGB model parameters, optimizing the acquisition and processing of remote sensing data, and enhancing model validation and application. Furthermore, it is essential to conduct cross-regional validation and optimize model parameters for various crops to improve the universality and adaptability of the proposed model.