Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice

Abstract

Real-timely monitoring of the crop water status can improve irrigation scheduling to increase water saving and enhance agricultural sustainability, whereas the canopy temperature measured by thermal imaging is an essential indicator for determining the rice water status. The primary goal of this study was to propose a new temperature index based on the temporal variance of the daily temperature and to develop the rice water status estimation model. Two field experiments involving two rice varieties and multi-irrigation regimes were conducted from 2019 to 2020. A thermal imaging camera was used to measure the canopy temperature from 8:00–16:00 at 2-hour intervals across all growth stages. Three plant water parameters, namely plant water content (PWC), canopy water content (CWC), and canopy equivalent water thickness (CEWT), were collected simultaneously. The results showed that canopy temperature and plant water parameters differed obviously among different irrigation treatments. The relative canopy temperature velocity (RCTV) was developed based on the temporal variance of the daily temperature, and the RCTV8–12 performed well in distinguishing different irrigation treatments and quantifying the rice water status. The coefficient of determination (R2) values of the exponential relationships between the optimal RCTV and plant water parameters reached 0.47 (PWC), 0.39 (CWC) and 0.18 (CEWT). The random forest model, which integrates the multi-temperature indices, achieved a good estimation for PWC (R2 = 0.78), CWC (R2 = 0.77), and CEWT (R2 = 0.64) across all growth stages. In summary, combining the multi-temperature indices derived from the thermal infrared imagery and machine learning algorithm can facilitate the non-destructive estimation of the rice water status and improve the precision irrigation schedule.