LAUSR

ABSTRACT Harvest time is an important factor affecting grain yield and postharvest quality. The estimation of crop maturity greatly supports farmers in the optimization of the harvest time. This study proposed a method for predicting paddy rice maturity (J816 and 5Y4 varieties) using color features and the random forest (RF) regression algorithm. Paddy panicle images were obtained using a flatbed scanner during the maturation period. To estimate paddy rice maturity, 22 color features representing the greenness of crop leaves were extracted from the paddy panicle images. Stepwise regression was used to select superior features as inputs to the RF regression model. The coefficient of determination (R2) and root mean square error (RMSE) values of the model were 0.93 and 1.18% for J816 and 0.94 and 1.60% for 5Y4, respectively. The results indicate that the proposed method in this study is a promising technique for the estimation of paddy rice maturity.