数字农科院2.0

CatBoost algorithm for estimating maize above-ground biomass using unmanned aerial vehicle-based multi-source sensor data and SPAD values

文献类型： 外文期刊

作者： Weiguang Zhai;Changchun Li;Shuaipeng Fei;Yanghua Liu;Fan Ding;Qian Cheng;Zhen Chen

作者机构：

关键词： Above-ground biomass;CatBoost;Maize;SPAD values;Unmanned aerial vehicle

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2023 年 214 卷

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： The rapid and accurate estimation of maize above-ground biomass (AGB) is pivotal for precise agricultural management. The rapid evolution of unmanned aerial vehicles (UAVs) and sensor technology has introduced a novel method for obtaining AGB information. Nevertheless, individual sensors may lack comprehensive data, leading to reduced AGB estimation accuracy in certain scenarios. This study collected UAV multi-spectral (MS) and thermal infrared (TIR) data, alongside soil and plant analyzer development (SPAD) values, from maize across multiple growth stages (jointing, trumpet, and big trumpet) during 2022 and 2023. Diverse data fusion programs were devised to explore the potential of combining multi-source sensor data with SPAD values to estimate AGB. The efficacy of CatBoost was evaluated and benchmarked against Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms. For the entire growth, findings reveal that the fusion of multi-source sensor data (MS + TIR) can mitigate the data insufficiency in single-sensor estimations. The resulting R2 values range from 0.608 to 0.817. Optimal estimation outcomes were achieved by the fusion of multi-source sensor data with SPAD values (MS + TIR + SPAD), yielding R2 values ranging from 0.685 to 0.872. For a single growth stage, there are variations in the estimation accuracy across different growth stages. From the jointing stage to the big trumpet stage, the estimation accuracy consistently increases, with the highest accuracy observed during the big trumpet stage, with R2 ranging from 0.721 to 0.901. Additionally, in alignment with the results for the entire growth stage, the fusion of multi-source sensor data with SPAD values still yields the highest estimation accuracy during different growth stages. In a comparison of different machine learning algorithms, for both the entire growth stage and single growth stages, SVR, RFR, and CatBoost achieved R2 values ranging from 0.305 to 0.824, 0.368 to 0.881, and 0.451 to 0.901, respectively. Notably, the CatBoost algorithm exhibited heightened estimation accuracy. The fusion of multi-source sensor data with SPAD values combined with the CatBoost algorithm results in accurate and reliable maize AGB estimation accuracy. This high-throughput approach to crop phenotyping is characterized by speed and accuracy and serves as a valuable reference for rapidly acquiring AGB information in this geographical region.

分类号：

  • 相关文献

[1]Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models. Mi Luo,Shoaib Ahmad Anees,Qiuyan Huang,Xin Qin,Zhihao Qin,Jianlong Fan,Guangping Han,Liguo Zhang,Helmi Zulhaidi Mohd Shafri. 2024

[2]Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms. Yu, Danyang,Zha, Yuanyuan,Sun, Zhigang,Li, Jing,Jin, Xiuliang,Zhu, Wanxue,Bian, Jiang,Ma, Li,Zeng, Yijian,Su, Zhongbo. 2022

[3]Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications. Zhai, Weiguang,Li, Changchun,Cheng, Qian,Mao, Bohan,Li, Zongpeng,Li, Yafeng,Ding, Fan,Qin, Siqing,Fei, Shuaipeng,Chen, Zhen. 2023

[4]Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data. Yafeng Li,Changchun Li,Qian Cheng,Li Chen,Zongpeng Li,Weiguang Zhai,Bohan Mao,Zhen Chen. 2024

[5]High-quality images and data augmentation based on inverse projection transformation significantly improve the estimation accuracy of biomass and leaf area index. Yingpu Che,Qing Wang,Ziwen Xie,Shilin Li,Jinyu Zhu,Baoguo Li,Yuntao Ma. 2023

[6]Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments. Cheng, Qian,Xu, Honggang,Fei, Shuaipeng,Li, Zongpeng,Chen, Zhen. 2022

[7]Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing. Weiguang Zhai,Changchun Li,Qian Cheng,Fan Ding,Zhen Chen. 2023

[8]Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation. Jingyu Hu,Hao Feng,Qilei Wang,Jianing Shen,Jian Wang,Yang Liu,Haikuan Feng,Hao Yang,Wei Guo,Hongbo Qiao,Qinglin Niu,Jibo Yue. 2024

[9]DissectingthemaizedirectandindirectdefenseresponseagainstAsianCornBorer. 汪海,李圣彦,查象敏,朱莉,黄大昉,郎志宏. 2015

[10]TheDifferentialTranscriptionNetworkbetweenEmbryoandEndospermintheEarlyDevelopingMaizeSeed. XiaoduoLu,DijunChen,DefengShu,ZhaoZhang,WeixuanWang,ChristianKlukas,Ling-lingChen,YunliuFan,MingChen,ChunyiZhang. 2015

[11]Stomatal conductance modeling for drip-irrigated kiwifruit in seasonal drought regions of South China: Evaluation of improved empirical models and interpretable machine learning approaches. Shunsheng Zheng,Ningbo Cui,Quanshan Liu,Shouzheng Jiang,Daozhi Gong,Xiaoxian Zhang. 2026

[12]A novel approach for predicting aflatoxin B1 production using regression models and whole-cell biosensors in moldy maize and peanut kernels. Lu Sun,Junning Ma,Yongping Jiang,Giorgia Purcaro,Yuanyuan Tian,Gang Wang,Weizhao Li,Bowen Tai,Fuguo Xing. 2025

[13]Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada. Liu, S.,Liu, S.,Yang, J. Y.,Drury, C. F.,Reynolds, W. D.,Liu, H. L.. 2014

[14]Estimation of Potato Above-Ground Biomass Based on Vegetation Indices and Green-Edge Parameters Obtained from UAVs. Yang Liu,Haikuan Feng,Jibo Yue,Yiguang Fan,Xiuliang Jin,Xiaoyu Song,Hao Yang,Guijun Yang. 2022

[15]Grazing season regulates plant community structure and production by altering plant litter mass in a typical steppe. Shi C.,Li Y.,Bai Z.,Wu L.,Wang H.,Zhang T.,Chang Q.,Li F.Y.. 2022

[16]Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements. Yang Liu,Haikuan Feng,Jibo Yue,Yiguang Fan,Mingbo Bian,Yanpeng Ma,Xiuliang Jin,Xiaoyu Song,Guijun Yang. 2023

[17]Nitrate-Nitrogen Dynamics and Nitrogen Budgets in Rice-Wheat Rotations in Taihu Lake Region, China. Zhang Jun-Hua (2), LIU Jian-Li (2)*, ZHANG Jia-Bao , CHENG Ya-Nan , WANG Wei-Peng. 2013

[18]Phenological changes offset the warming effects on biomass production in an alpine meadow on the Qinghai–Tibetan Plateau. Hasbagan Ganjurjav,Elise Gornish,Guozheng Hu,Jianshuang Wu,Yunfan Wan,Yue Li,Qingzhu Gao. 2021

[19]Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height. Liu, Yang,Feng, Haikuan,Yue, Jibo,Jin, Xiuliang,Li, Zhenhai,Yang, Guijun. 2022

[20]Cross-scale mapping of above-ground biomass and shrub dominance by integrating UAV and satellite data in temperate grassland. Ang Chen,Cong Xu,Min Zhang,Jian Guo,Xiaoyu Xing,Dong Yang,Bin Xu,Xiuchun Yang. 2024

作者其他论文 更多>>