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Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature

文献类型：期刊论文

作者Wang, Jian4; Wu, Bizhi2,3; Kohnen, Markus, V3; Lin, Daqi3; Yang, Changcai1; Wang, Xiaowei3; Qiang, Ailing4; Liu, Wei4; Kang, Jianbin6; Li, Hua1
刊名PLANT PHENOMICS
出版日期2021
卷号2021页码:14
ISSN号2643-6515
DOI10.34133/2021/9765952
通讯作者Su, Jun(junsu@fafu.edu.cn) ; Li, Bangyu(bangyu.li@ia.ac.cn) ; Gu, Lianfeng(lfgu@fafu.edu.cn)
英文摘要High-yield rice cultivation is an effective way to address the increasing food demand worldwide. Correct classification of high-yield rice is a key step of breeding. However, manual measurements within breeding programs are time consuming and have high cost and low throughput, which limit the application in large-scale field phenotyping. In this study, we developed an accurate large-scale approach and presented the potential usage of hyperspectral data for rice yield measurement using the XGBoost algorithm to speed up the rice breeding process for many breeders. In total, 13 japonica rice lines in regional trials in northern China were divided into different categories according to the manual measurement of yield. Using an Unmanned Aerial Vehicle (UAV) platform equipped with a hyperspectral camera to capture images over multiple time series, a rice yield classification model based on the XGBoost algorithm was proposed. Four comparison experiments were carried out through the intraline test and the interline test considering lodging characteristics at the midmature stage or not. The result revealed that the degree of lodging in the midmature stage was an important feature affecting the classification accuracy of rice. Thus, we developed a low-cost, high-throughput phenotyping and nondestructive method by combining UAV-based hyperspectral measurements and machine learning for estimation of rice yield to improve rice breeding efficiency.
WOS关键词DIFFERENCE VEGETATION INDEX ; GRAIN-YIELD ; WHEAT ; SYSTEM
资助项目Agreement on Functional Gene-Mining and Selection of Superior Crop Performances to Lianfeng Gu, Digital Fujian Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University[KJG18019A08] ; Autonomous Region Key RD Program[2018BEB04002] ; Innovation Team of Intelligence Assisted Phenotypic Analysis for Ningxia Crop. ; Agricultural Breeding in Ningxia Hui Autonomous Region[2018NYYZ03]
WOS研究方向Agriculture ; Plant Sciences ; Remote Sensing
语种英语
出版者AMER ASSOC ADVANCEMENT SCIENCE
WOS记录号WOS:000705528200008
资助机构Agreement on Functional Gene-Mining and Selection of Superior Crop Performances to Lianfeng Gu, Digital Fujian Institute of Big Data for Agriculture and Forestry, Fujian Agriculture and Forestry University ; Autonomous Region Key RD Program ; Innovation Team of Intelligence Assisted Phenotypic Analysis for Ningxia Crop. ; Agricultural Breeding in Ningxia Hui Autonomous Region
源URL[http://ir.ia.ac.cn/handle/173211/46243]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
通讯作者Su, Jun; Li, Bangyu; Gu, Lianfeng
作者单位1.Fujian Agr & Forestry Univ, Digital Fujian Inst Big Data Agr & Forestry, Key Lab Smart Agr & Forestry, Fuzhou 350002, Peoples R China
2.Xiamen Univ, State Key Lab Marine Environm Sci, Xiamen, Peoples R China
3.Fujian Agr & Forestry Univ, Coll Forestry, Basic Forestry & Prote Res Ctr, Fuzhou 350002, Peoples R China
4.Ningxia Acad Agr & Forestry Sci, Inst Crop Sci, Yinchuan 750105, Ningxia, Peoples R China
5.Chinese Acad Sci, Aerosp Informat Res Ctr, Inst Automat, Beijing 100190, Peoples R China
6.Seed Workstn Ningxia Hui Autonomous Reg, Yinchuan 750004, Ningxia, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Jian,Wu, Bizhi,Kohnen, Markus, V,et al. Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature[J]. PLANT PHENOMICS,2021,2021:14.
APA Wang, Jian.,Wu, Bizhi.,Kohnen, Markus, V.,Lin, Daqi.,Yang, Changcai.,...&Gu, Lianfeng.(2021).Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature.PLANT PHENOMICS,2021,14.
MLA Wang, Jian,et al."Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature".PLANT PHENOMICS 2021(2021):14.

入库方式： OAI收割

来源：自动化研究所

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