A model suitable for estimating above-ground biomass of potatoes at different regional levels
文献类型： 外文期刊
作者： Liu, Yang 1 ; Fan, Yiguang 1 ; Yue, Jibo 2 ; Jin, Xiuliang 3 ; Ma, Yanpeng 1 ; Chen, Riqiang 1 ; Bian, Mingbo 1 ; Yang, Guijun 1 ; Feng, Haikuan 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China
3.Chinese Acad Agr Sci, Inst Crop Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Beijing 100081, Peoples R China
关键词： Potato; Hierarchical linear model; Hyperspectral; Meteorological data; Biomass
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： Above-ground biomass (AGB) is an important agronomic indicator that reflects crop growth and estimates yield. The AGB estimation using remote sensing becomes a non-destructive, rapid, and alternative method to postharvest laboratory measurements. However, most of the AGB estimation models constructed based on remote sensing data are difficult to expand regionally, which limits the applicability of the models. This study combined ground-based hyperspectral and meteorological data by using a hierarchical linear modeling (HLM) to construct an AGB estimation model that was generalized across different regions. Experimental data from both regions were acquired and validated, namely from Xiaotangshan Experimental Base, Beijing, 2019 (North China) and Keshan Farm, Qiqihar Branch, Heilongjiang General Bureau of Reclamation, 2022 (Northeast China). Compared to OLS, RFR and GRU, the HLM method was better for estimating potato AGB in different regions with R 2 = 0.54, RMSE = 429.62 kg/hm 2 , NRMSE = 28.20 %. The results of this study demonstrated that HLM could be used as a powerful method to improve the transferability of AGB estimation at different regions.
- 相关文献
作者其他论文 更多>>
-
Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data
作者：Liu, Yang;Feng, Haikuan;Fan, Yiguang;Chen, Riqiang;Bian, Mingbo;Ma, Yanpeng;Li, Jingbo;Xu, Bo;Yang, Guijun;Liu, Yang;Liu, Yang;Feng, Haikuan;Yue, Jibo;Jin, Xiuliang
关键词：AGB; Hyperspectral features; Deep features; SPA; LSTM; PLSR
-
Winter Wheat Yield Estimation with Color Index Fusion Texture Feature
作者：Yang, Fuqin;Yan, Jiayu;Guo, Lixiao;Tan, Jianxin;Meng, Xiangfei;Xiao, Yibo;Liu, Yang;Feng, Haikuan;Liu, Yang;Feng, Haikuan
关键词：UAV; color index; fusion texture; partial least squares; random forest
-
Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation
作者：Hu, Jingyu;Feng, Hao;Shen, Jianing;Wang, Jian;Guo, Wei;Qiao, Hongbo;Yue, Jibo;Wang, Qilei;Liu, Yang;Liu, Yang;Feng, Haikuan;Yang, Hao;Niu, Qinglin;Niu, Qinglin
关键词：unmanned aerial vehicle; crop leaf chlorophyll content; fractional vegetation cover; maturity; deep learning; ensemble learning; maize
-
Improving potato above ground biomass estimation combining hyperspectral data and harmonic decomposition techniques
作者：Liu, Yang;Feng, Haikuan;Fan, Yiguang;Chen, Riqiang;Ma, Yanpeng;Bian, Mingbo;Yang, Guijun;Liu, Yang;Liu, Yang;Feng, Haikuan;Yue, Jibo
关键词：AGB; ASD; UHD185; Harmonic components; PLSR
-
A fast and lightweight detection model for wheat fusarium head blight spikes in natural environments
作者：Gao, Chunfeng;Guo, Wei;Gong, Zheng;Yue, Jibo;Fu, Yuanyuan;Yang, Chenghai;Feng, Haikuan
关键词：Deep learning; YOLOv5s; Fusarium head blight; Real -time detection; Lightweight architecture
-
Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm
作者：Li, Yafeng;Xu, Xingang;Zhu, Yaohui;Xue, Hanyu;Li, Yafeng;Xu, Xingang;Wu, Wenbiao;Yang, Guijun;Yang, Xiaodong;Meng, Yang;Jiang, Xiangtai;Xue, Hanyu
关键词：different varieties of grapes; leaf chlorophyll content; hyperspectral remote sensing; data-processing; RFR
-
Real-time monitoring of maize phenology with the VI-RGS composite index using time-series UAV remote sensing images and meteorological data
作者：Feng, Ziheng;Ma, Xinming;Feng, Ziheng;Cheng, Zhida;Ren, Lipeng;Liu, Bowei;Zhang, Chengjian;Zhao, Dan;Sun, Heguang;Feng, Haikuan;Long, Huiling;Xu, Bo;Yang, Hao;Song, Xiaoyu;Yang, Guijun;Zhao, Chunjiang
关键词：UAV; Real-time; Composite index; Maize phenology; BBCH
