Spider mites are important pests that cause severe economic damage to cotton. They feed on underside of leaves, piercing the chloroplast-containing cells, resulting in foliar damage and yield reduction. This chapter proposed a two-stage classification approach for mite-infestation detection based on machine learning methods. Two cotton fields were selected for study, and the UAV imagery collection and concurrent ground investigation were conducted on July 20–21, 2017. Mosaicking and geo-registration were performed on the collected multispectral imagery. A support vector machine (SVM) was used for scene classification, and a transferred convolutional neural network (CNN) was applied for mite-infestation identification. Experimental results showed that our method outperformed others in terms of accuracy, which demonstrated that our approach has potential in mite-infestation detection using UAV multispectral imagery.