A New and Improved YOLO Model for Individual Litchi Crown Detection with High-Resolution Satellite RGB Images

The accurate detection of individual litchi crowns is important for precision management and yield estimation. This study aims to improve the YOLOv8n model for accurate detection of litchi crowns in high-resolution satellite images. For this purpose, three typical litchi orchards were selected for this study. High-resolution satellite RGB images of these orchards were collected, and individual crowns were visually interpreted. On the basis of these data, this study first improved the YOLOv8 model by fusing a priori knowledge with the task alignment learning (TAL) module, implementing efficient local attention (ELA), and employing a receptive field block (RFB) module, resulting in an improved model called the CAR-YOLO model. An ablation experiment was subsequently used to analyze the effects of the above strategies on the improvement of YOLOv8n model. Finally, the proposed CAR-YOLO model was compared with Fast-RCNN, YOLOv5n, YOLOv8n, YOLOv10n and YOLO v11n. The results showed that all of the improvement strategies used in this study enhanced the performance of the original model. Among all of the models, the CAR-YOLO model exhibited the best performance in terms of litchi crown detection, with AP50 values varying from 0.7069 to 0.8121 and F1 scores varying from 0.6908 to 0.7761 for different orchards. The other models resulted in AP50 values ranging from 0.4860 to 0.7895 and F1 score values ranging from 0.5265 to 0.7628. As demonstrated by these results, this study provides useful support for the precise management and planting inventory of litchi.