In this study, an individual mangrove object detection model called YOLOv5-ECA based on deep learning is proposed to automatically identify and locate individual mangroves with high accuracy aiming at the challenges of small and dense individual mangroves in drone images, resulting in low automation and efficiency for detecting them. First, the open-source software LabelImg is used to mark the target tree on the selected drone image, which is applied to construct the individual mangrove dataset. Then, the YOLOv5 is used as the basic object detection model to maximize and enhance the target tree, and achieving this is based on the characteristics of dense distribution and small size of objects. The efficient channel attention (ECA) mechanism enhances the CSPDarknet53 backbone network to avoid dimensionality reduction while enhancing feature expression capabilities. Furthermore, the enhanced SoftPool pooling operation is introduced into the SPP module to retain more detailed feature information. Finally, the ACON adaptive activation function determines whether the neuron is activated. The results demonstrate that the constructed dataset is used to train the network before and after improvement, and the accuracy, recall, and mean average precision (mAP)@0.5 parameters are compared. The results of different models are slightly different, but they all tend to converge. The proposed YOLOv5-ECA's average detection accuracy is 3.2 percentage points higher than YOLOv5 and 5.19 percentage points higher than YOLOv4, and its training loss is also lower. The deep learning-based YOLOv5-ECA model can quickly, accurately, and automatically detect individual mangroves and significantly enhance the ability to identify and locate them.