Citrus huanglongbing detection: A hyperspectral data-driven model integrating feature band selection with machine learning algorithms

Abstract

This study explored rapid detection techniques for citrus Huanglongbing (HLB), a disease that severely impacts global citrus production. The method based on hyperspectral technology combined with machine learning algorithms provides new ideas for rapid HLB identification. Algorithm selection is crucial for processing efficiency and hyperspectral data interpretation. Hyperspectral data from healthy, mild HLB-infected, and macular (not related to HLB) citrus leaves were captured using a hyperspectrometer, with qPCR validation. Three preprocessing methods were selected to preprocess the spectral data. Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) were used to extract feature bands from the hyperspectral data, and the range of the number of filtered feature bands as a percentage of the full band was 22.87%–28.31% and 3.27%–4.17%, respectively. Five distinct algorithms were then employed to construct classification models. Upon evaluation, the SPA-STD-SVM algorithm combination proved most effective, boasting a 97.46% accuracy and a 98.55% recall rate. The results demonstrate that suitable machine learning algorithms can effectively classify the hyperspectral data of citrus leaves in three different states: healthy, mild HLB-infected, and macular. This provides an effective approach for using hyperspectral data to differentiate citrus Huanglongbing.