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

文献类型： 外文期刊

作者： Wang, Jian 1 ; Wu, Bizhi 2 ; Kohnen, Markus, V 2 ; Lin, Daqi 2 ; Yang, Changcai 4 ; Wang, Xiaowei 2 ; Qiang, Ailing 1 ; L 1 ;

作者机构： 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

期刊名称：PLANT PHENOMICS

ISSN： 2643-6515

年卷期： 2021 年 2021 卷

页码：

收录情况： SCI

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.

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