| Summary: | Rapid and accurate detection of <i>Camellia oleifera</i> fruit is beneficial to improve the picking efficiency. However, detection faces new challenges because of the complex field environment. A <i>Camellia oleifera</i> fruit detection method based on YOLOv7 network and multiple data augmentation was proposed to detect <i>Camellia oleifera</i> fruit in complex field scenes. Firstly, the images of <i>Camellia oleifera</i> fruit were collected in the field to establish training and test sets. Detection performance was then compared among YOLOv7, YOLOv5s, YOLOv3-spp and Faster R-CNN networks. The YOLOv7 network with the best performance was selected. A DA-YOLOv7 model was established via the YOLOv7 network combined with various data augmentation methods. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability in complex scenes, with mAP, Precision, Recall, F1 score and average detection time of 96.03%, 94.76%, 95.54%, 95.15% and 0.025 s per image, respectively. Therefore, YOLOv7 combined with data augmentation can be used to detect <i>Camellia oleifera</i> fruit in complex scenes. This study provides a theoretical reference for the detection and harvesting of crops under complex conditions.
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