A Fine Classification Method of Citrus Fruit Trees Based on UAV
Hyperspectral Images and SULOV_XGBoost Algorithm
XIAO Bin1, 2, HE Hong-chang1, DOU Shi-qing1*, FAN Dong-lin1, FU Bo-lin1, ZHANG Jie1, XIONG Yuan-kang1, SHI Jin-ke1
1. College of Surveying and Mapping Geographic Information, Guilin University of Technology, Guilin 541006, China
2. Pearl River Water Resources Research Institute, Pearl River Water Resources Commission, Guangzhou 510610, China
Abstract：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.
Key words：Citrus fruit trees; UAV hyperspectral; SULOV_XGBoost; Fine classification
肖 斌，何宏昌，窦世卿，范冬林，付波霖，张 洁，熊远康，史今科. UAV高光谱影像联合SULOV_XGBoost算法的柑橘果树精细分类方法[J]. 光谱学与光谱分析, 2024, 44(02): 548-557.
XIAO Bin, HE Hong-chang, DOU Shi-qing, FAN Dong-lin, FU Bo-lin, ZHANG Jie, XIONG Yuan-kang, SHI Jin-ke. A Fine Classification Method of Citrus Fruit Trees Based on UAV
Hyperspectral Images and SULOV_XGBoost Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 548-557.
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