Real-Time Detection of Rice Growth Phase Transition for Panicle Nitrogen Application Timing Assessment

Nitrogen (N) topdressing at the early reproductive phase (ER) is beneficial for rice yield. However, the ER overlaps with the late vegetative phase (LV) and is, thus, difficult to be recognized by human observation. Therefore, this study aimed to establish a high-temporal-resolution approach to determine the LV and ER via hyperspectral proximal sensing. Firstly, this research measured the leaf cover area (LCA), leaf dry weight (LDW), chlorophyll content (SPAD), leaf N content (LNC), and leaf N accumulation (LNA) to investigate the physical and physiological changes of the rice plant during growth phase transition. It could be summarized that the LCA would be maximally extended before ER, the leaf growth would be retarded after LV, and leaves turned from green to yellowish-green resulting from N translocation. These phenomena were expected to be detected by the hyperspectral sensor. In order to capture the variation of spectral information while eliminating redundant hyperspectral wavelengths, feature extraction (FE) and feature selection (FS) were conducted to reduce the data dimension. Meanwhile, the implications of the features were also inferenced. Three principal components, which correlated with the rice plant’s physical and physiological traits, were extracted for subsequent modeling. On the aspect of FS, 402, 432, 579, and 696 nm were selected as the predictors. The 402 nm wavelength significantly correlated with leaf cover area to some extent (p < 0.09), and 432 nm had no significant correlation with all of the measured plant traits (p > 0.10). The 579 nm and 696 nm wavelengths were negatively correlated with SPAD and LNC (p < 0.001). In addition, 696 nm was also negatively correlated with LNA (p < 0.05). Finally, the logistic regression, random forest (RF), and support vector machine (SVM) algorithms were adopted to solve the binary classification problem. The result showed that the feature extraction-based logistic regression (FE-logistic) and support vector machine (FE-SVM) were competent for growth phase discrimination (accuracy > 0.80). Nonetheless, taking the detrimental effects of applying N at LV into consideration, the feature extraction-based support vector machine (FE-SVM) was more appropriate for the timing assessment of panicle fertilizer application (sensitivity > 0.90; specificity > 0.80; precision > 0.80).