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Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms

文献类型：期刊论文

作者Yu, Danyang1,2; Zha, Yuanyuan1; Sun, Zhigang3,4; Li, Jing3,4; Jin, Xiuliang5; Zhu, Wanxue3,4; Bian, Jiang1; Ma, Li3,4; Zeng, Yijian2; Su, Zhongbo2
刊名PRECISION AGRICULTURE
出版日期2022-07-02
页码22
ISSN号1385-2256
关键词Unmanned aerial vehicle Above-ground biomass Multi-source data DCNN Machine learning
DOI10.1007/s11119-022-09932-0
通讯作者Zha, Yuanyuan(zhayuan87@whu.edu.cn)
英文摘要Accurate estimation of above-ground biomass (AGB) plays a significant role in characterizing crop growth status. In precision agriculture area, a widely-used method for measuring AGB is to develop regression relationships between AGB and agronomic traits extracted from multi-source remotely sensed images based on unmanned aerial vehicle (UAV) systems. However, such approach requires expert knowledges and causes the information loss of raw images. The objectives of this study are to (i) determine how multi-source images contribute to AGB estimation in single and whole growth stages; (ii) evaluate the robustness and adaptability of deep convolutional neural networks (DCNN) and other machine learning algorithms regarding AGB estimation. To establish multi-source image datasets, this study collected UAV red-green-blue (RGB), multispectral (MS) images and constructed the raster data for crop surface models (CSMs). Agronomic features were derived from the above-mentioned images and interpreted by the multiple linear regression, random forest, and support vector machine models. Then, a DCNN model was developed via an image-fusion architecture. Results show that the DCNN model provides the best estimation of maize AGB when a single type of image is considered, while the performance of DCNN degrades when sufficient agronomic features are used. Besides, the information of above three image datasets changes with various growth stages. The structure information derived from CSM images are more valuable than spectrum information derived from RGB and MS images in the vegetative stage, but less useful in the reproductive stage. Finally, a data fusion strategy was proposed according to the onboard sensors (or cost).
WOS关键词CROP YIELD PREDICTION ; VEGETATION ; INDEX ; SOIL ; CLASSIFICATION ; FUSION ; WHEAT ; TOOL
资助项目National Key Research & Development Program of China[2021YFC3201204] ; Key Research and Development Program in Guangxi[AB19245039] ; Pudong New Area Science & Technology Development Fund[PKX2020-R07] ; Fundamental Research Funds for the Central Universities[2042021kf0200] ; Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures, Guangxi Institute of Water Resources Research[GXHRI-WEMS-2022-01]
WOS研究方向Agriculture
语种英语
出版者SPRINGER
WOS记录号WOS:000819884500002
资助机构National Key Research & Development Program of China ; Key Research and Development Program in Guangxi ; Pudong New Area Science & Technology Development Fund ; Fundamental Research Funds for the Central Universities ; Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures, Guangxi Institute of Water Resources Research
源URL[http://ir.igsnrr.ac.cn/handle/311030/180356]  
专题中国科学院地理科学与资源研究所
通讯作者Zha, Yuanyuan
作者单位1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Hubei, Peoples R China
2.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 AE Enschede, Netherlands
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
5.Chinese Acad Agr Sci, Inst Crop Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Yu, Danyang,Zha, Yuanyuan,Sun, Zhigang,et al. Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms[J]. PRECISION AGRICULTURE,2022:22.
APA Yu, Danyang.,Zha, Yuanyuan.,Sun, Zhigang.,Li, Jing.,Jin, Xiuliang.,...&Su, Zhongbo.(2022).Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms.PRECISION AGRICULTURE,22.
MLA Yu, Danyang,et al."Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms".PRECISION AGRICULTURE (2022):22.

入库方式： OAI收割

来源：地理科学与资源研究所

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