Huanglongbing (HLB) is a disease that significantly affects citrus crops and production, even when various care measures are taken to cope with it, as it is a terminal disease for them. This article presents a methodology that facilitates the detection of HLB-infected trees in Valencia orange orchards using computer vision techniques. To evaluate the methodology, multispectral photographs were taken of 10 fields located in the state of Tamaulipas and around Ciudad Victoria. A total of 141 images were adjusted and regions of interest were extracted: 58 images of healthy trees and 83 of diseased trees. Twenty vegetation indices were applied for the identification of the classes Background, Healthy Trees, and Diseased Trees. Subsequently, the techniques of Correlation Analysis (CA), Principal Component Analysis (PCA), AutoEncoder (AE), CA with PCA (CA-PCA), CA with AE (CA-AE), Minimum Redundancy Maximum Relevance (mRMR), and Transformers (T) were used to develop the 7 datasets for training the classifiers Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), and Ensemble Learning (EL) composed of AdaBoost with DecisionTree, XGB, and RandomForest. The classifier that achieved the best separation between the classes Background, Diseased Trees, and Healthy Trees was EL using the CA-AE set, which was composed of the reduction of vegetation indices through CA and AE, with an Accuracy of 70%, Precision of 69%, Recall of 69%, F1-score of 69%, and Cohen’s Kappa of 55%.