Research Article | Open Access

Volume 2021 |Article ID 9765952 | https://doi.org/10.34133/2021/9765952

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

Jian Wang,¹ Bizhi Wu,^2,3 Markus V. Kohnen,² Daqi Lin,² Changcai Yang,⁴ Xiaowei Wang,² Ailing Qiang,¹ Wei Liu,¹ Jianbin Kang,⁵ Hua Li,⁴ Jing Shen,⁵ Tianhao Yao,² Jun Su iD ,² Bangyu Li iD ,⁶ and Lianfeng Gu iD ²

¹Institute of Crop Sciences, Ningxia Academy of Agriculture and Forestry Science, Yinchuan, Ningxia 750105, China
²Basic Forestry and Proteomics Research Center, College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
³State Key Laboratory of Marine Environmental Science, Xiamen University, China
⁴Digital Fujian Institute of Big Data for Agriculture and Forestry, Key Laboratory of Smart Agriculture and Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
⁵Seed Workstations of the Ningxia Hui Autonomous Region, Yinchuan, Ningxia 750004, China
⁶Aerospace Information Research Center, Institute of Automation, Chinese Academic Science, Beijing 100190, China

Abstract

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.