Non-destructive monitoring of amylose content in rice by UAV-based hyperspectral images

Fumin Wang; Qiuxiang Yi; Lili Xie; Xiaoping Yao; Jueyi Zheng; Tianyue Xu; Jiale Li; Siting Chen

doi:10.3389/fpls.2022.1035379

Non-destructive monitoring of amylose content in rice by UAV-based hyperspectral images

Front Plant Sci. 2022 Oct 27:13:1035379. doi: 10.3389/fpls.2022.1035379. eCollection 2022.

Authors

Fumin Wang^{1

2}, Qiuxiang Yi³, Lili Xie¹, Xiaoping Yao¹, Jueyi Zheng¹, Tianyue Xu¹, Jiale Li¹, Siting Chen¹

Affiliations

¹ Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou, China.
² Key Laboratory of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou, China.
³ State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China.

Abstract

Amylose content (AC) is an important indicator for rice quality grading. The rapid development of unmanned aerial vehicle (UAV) technology provides rich spectral and spatial information on observed objects, making non-destructive monitoring of crop quality possible. To test the potential of UAV-based hyperspectral images in AC estimation, in this study, observations on five rice cultivars were carried out in eastern China (Zhejiang province) for four consecutive years (from 2017 to 2020). The correlations between spectral and textural variables of UAV-based hyperspectral images at different growth stages (booting, heading, filling, and ripening) and AC (%) were analyzed, and the linear regression models based on spectral variables alone, textural variables alone, and combined spectral and textural variables were established. The results showed that the sensitive bands (P< 0.001) to AC were mainly centered in the green (536∽568 nm) and red regions (630∽660nm), with spectral and textural variables at the ripening stage giving the highest negative correlation coefficient of -0.868 and -0.824, respectively. Models based on combined spectral and textural variables give better estimation than those based on spectral or textural variables alone, characterized by less variables and higher accuracy. The best models using spectral or textural variables alone both involved three growth stages (heading, filling, and ripening), with root mean square error (RMSE) of 1.01% and 1.04%, respectively, while the models based on combined spectral and textural variables have RMSE of 1.04% 0.844% with only one (ripening stage) or two (ripening and filling stages) growth stages involved. The combination of spectral and textural variables of UAV-based hyperspectral images is expected to simplify data acquisition and enhance estimation accuracy in remote sensing of rice AC.

Keywords: UAV-based hyperspectral images; amylose content; rice; spectral variables; textural measures.