Abstract: A near-real-time recognition model was constructed to rapidly and timely identify the rice growth stages using sequential spectral images. The key stages of rice growth were identified, including tillering, jointing, heading, milking, and maturity. An optimal combination of features was also obtained. The rice experimental field was carried out in the Tianhe District, Guangzhou City, Guangdong Province, China. Unmanned aerial vehicles (UAVs) were involved in capturing spectral images of the field. The drone imaging system was equipped with one visible light and five multispectral cameras, each of which had a 2-million-pixel resolution for the high-definition captures. Detailed spectral data was obtained to precisely identify the different rice growth stages. A significant challenge was the variation in the RGB values under different lighting conditions. The reason was that the accurate segmentation of the rice canopy was hindered by using color as the primary feature. A novel spectral image segmentation was introduced to combine the Relative Vegetation Index using an iterative self-organizing data analysis. The complete area of the rice canopy was precisely extracted for the accurate identification of the growth stage. Then, the dataset was simplified to reduce the dimensions and computational complexity, in order to enhance the performance and efficiency of recognition. Among them, the expression of features was analyzed during different rice growth stages, where the reflectance data was from green, red, blue, red-edge, and near-infrared bands. Eight vegetation indices were considered as the candidate features, including the normalized difference red edge index (NDRE), visual atmospherically resistant index (VARI), and red edge chlorophyll index (RECI). Minimum Redundancy Feature Selection was used to efficiently rank and select the most relevant and least redundant features, in order to avoid computational overload and potential impact on the model. The optimal combination of features was identified as the NDRE, VARI, and RECI, thus forming the crux of the feature engineering. Finally, this combination was integrated into the Extreme Gradient Boosting to construct an advanced recognition model of the rice growth stage. The improved model with the Extreme Gradient Boosting also exhibited exceptional performance with minimal confusion among the five growth stages. The accuracy rates of the model were 98.08%, 100.00%, 99.68%, 97.50%, and 99.29%, respectively, for the tillering, jointing, heading, milking, and maturity stages of rice. An overall recognition accuracy of 98.77% was achieved. Additionally, the improved model scored a Kappa coefficient of 0.984 and a macro-F1 score of 0.989 1. The XGBoost improved the overall recognition accuracy, Kappa coefficient, and F1 score by 1.59 percentage points, 0.020, and 0.015, respectively, compared with the SVM. While the improvements were 1.23 percentage points, 0.015, and 0.011, respectively, compared with RF. The finding can provide a reliable, near-real-time, and highly accurate identification of rice growth stages. This improved model can also offer essential support for decision-making in agricultural practices, particularly in crop production and field management for the high crop yield in precision agriculture.