Accurate and dynamic monitoring of economic crop planting information is an urgent need for agricultural fine management. In order to realize the fine classification of different fruit tree varieties, this paper proposes a fine classification method of citrus fruit trees based on UAV hyperspectral images and the SULOV_XGBoost algorithm in liutang Mocott citrus experimental base in Guilin City. Firstly, multidimensional data sets were constructed by deep mining spectral information from different citrus tree varieties. Then, the SULOV_XGBoost algorithm was used to optimize features, and the XGBoost algorithm was used for the fine classification of citrus fruit varieties. Finally, the accuracy of classification results was compared with that of RF and SVM. The results show that: (1) The proposed SULOV_XGBoost algorithm can effectively classify the different varieties of fruit trees and crops in scenes with small feature gaps, and the overall classification effect is better than the traditional machine learning methods (RF and SVM). (2) The fusion characteristics of the first-order differential inflection point value and the original band value play a great role in improving the precision of fine classification; the combination of different wavelength bands can also significantly improve the fine classification results of citrus fruit trees. (3) SVM has better classification performance and strong anti-interference ability under high ground object discrimination. The research results can provide new ideas and methods for fine classification of different varieties of crops in the same species, and also provide a reference for the precise survey of crop planting information, fine management and layout, adjustment and dynamic monitoring of agricultural industrial structure.