Knowledge Commons of Institute of Automation，CAS
| Classification of Rice Yield Using UAV-Based Hyperspectral Imagery and Lodging Feature | |
Wang, Jian1; Wu, Bizhi2,3; Kohnen, Markus, V2; Lin, Daqi2; Yang, Changcai4; Wang, Xiaowei2; Qiang, Ailing1; Liu, Wei1; Kang, Jianbin5; Li, Hua4; Shen, Jing5; Yao, Tianhao2; Su, Jun2; Li, Bangyu6 ; Gu, Lianfeng2
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| 发表期刊 | PLANT PHENOMICS
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| ISSN | 2643-6515 |
| 2021 | |
| 卷号 | 2021页码:14 |
| 通讯作者 | Su, Jun(junsu@fafu.edu.cn) ; Li, Bangyu(bangyu.li@ia.ac.cn) ; Gu, Lianfeng(lfgu@fafu.edu.cn) |
| 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. |
| DOI | 10.34133/2021/9765952 |
| 关键词[WOS] | DIFFERENCE VEGETATION INDEX ; GRAIN-YIELD ; WHEAT ; SYSTEM |
| 收录类别 | SCI |
| 语种 | 英语 |
| 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] |
| 项目资助者 | 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 |
| WOS研究方向 | Agriculture ; Plant Sciences ; Remote Sensing |
| WOS类目 | Agronomy ; Plant Sciences ; Remote Sensing |
| WOS记录号 | WOS:000705528200008 |
| 出版者 | AMER ASSOC ADVANCEMENT SCIENCE |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/46244 |
| 专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
| 通讯作者 | Su, Jun; Li, Bangyu; Gu, Lianfeng |
| 作者单位 | 1.Ningxia Acad Agr & Forestry Sci, Inst Crop Sci, Yinchuan 750105, Ningxia, Peoples R China 2.Fujian Agr & Forestry Univ, Coll Forestry, Basic Forestry & Prote Res Ctr, Fuzhou 350002, Peoples R China 3.Xiamen Univ, State Key Lab Marine Environm Sci, Xiamen, Peoples R China 4.Fujian Agr & Forestry Univ, Digital Fujian Inst Big Data Agr & Forestry, Key Lab Smart Agr & Forestry, Fuzhou 350002, Peoples R China 5.Seed Workstn Ningxia Hui Autonomous Reg, Yinchuan 750004, Ningxia, Peoples R China 6.Chinese Acad Sci, Aerosp Informat Res Ctr, Inst Automat, Beijing 100190, 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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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