AUTHOR=Sun Weiwei , He Qijin , Liu Jiahong , Xiao Xiao , Wu Yaxin , Zhou Sijia , Ma Selimai , Wang Rongwan 

TITLE=Dynamic monitoring of maize grain quality based on remote sensing data

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1177477

DOI=10.3389/fpls.2023.1177477

ISSN=1664-462X

ABSTRACT=Remote sensing data has been widely used to monitor grain protein content, while monitoring of grain starch and oil content is still rare. In this study, the field experiment with different sowing time i.e. 8th June, 18th June, 28th June and 8th July was conducted in 2018-2020. The scalable annual and interannual quality prediction model for summer maize in different growth period was established using hierarchical linear model (HLM) which combined with hyperspectral and meteorological data. Compared with the multiple linear regression (MLR) using VIs, the prediction accuracy of HLM was obviously improved with highest R2, RMSE and MAE values of 0.90, 0.10, and 0.08 (grain starch content, GSC); 0.87, 0.10 and 0.08 (GPC); 0.74, 0.13 and 0.10 (grain oil content, GOC) respectively. In addition, the combination of tasseling, grain-filling and maturity stages further improved the predictive power for GSC (R2 = 0.96); the combination of grain-filling and maturity stages further improved the predictive power for GPC (R2 = 0.90); and the prediction accuracy developed in the combination of jointing and tasseling stage for GOC (R2 = 0.85). The results also showed that meteorological factors, especially precipitation, had a great influence on grain quality monitoring. Our study provided a new idea for crop quality monitoring by remote sensing.