【Aim】 To address the issue of the high misdetection rate from RGB images taken by the UAV in the monitoring practice of pine dead trees, a new YOLOv5 model based on multi-color space is proposed for recognizing pine dead trees precisely. 【Method】 UAVs were used to collect RGB images of a large-area forest with pine nematode disease. The images were spliced with Pix4Dmapper software, and a dataset detailing pine dead trees in VOC format was established using LabelImg open-source software. Six object detection algorithms based on deep learning, namely Faster R-CNN, YOLOv3, YOLOv4, YOLOv5, SSD, and EfficientDet, were used to train and test the dataset. The optimal object detection algorithm was filtered using precision, recall, average precision (AP), and the F₁ score as evaluation metrics. Then, the collected RGB images were converted into LAB and HSV color space images, and the three color space images were trained with optimal object detection algorithms to obtain the bounding boxes of the target in each color space. These bounding boxes were processed using non-maximum suppression algorithms, and an optimal bounding box was obtained to realize the automatic recognition of dead trees. 【Result】 All six algorithms have achieved good results, of which the YOLOv5 model was the optimal algorithm. Its precision, AP, and F₁ score were the highest among the six algorithms, reaching 97.58%, 82.40%, and 0.85, respectively. After the fusion of the three color space images, the misdetection was reflected in the recall rate, which increased from 74.54% to 98.99%, and the AP, which increased to 98.39%. 【Conclusion】 The YOLOv5 model based on multi-color space can significantly improve the accuracy of detecting dead pine trees from RGB images taken by the UAV. The proposed model is a powerful tool for monitoring dead trees and assisting in controlling pine wood nematode disease.