Construction of a small sample dataset and identification of Pitaya trees (Selenicereus) based on UAV image on close-range acquisition

Rapid and accurate crop information extraction is important for detailed agricultural management and efficient yield estimation. However, the natural environment of the Karst Plateau in southwest China is fragile, the ground surface is broken, and the weather is complex and cloudy, making it difficult to obtain high-quality crop samples for crop information extraction in this complex environment. We obtained images of Pitaya trees from plateau mountain environments using DJI Mavic 2 Pro UAV, constructed a small UAV close-range acquisition sample dataset, which included initial, supplementary, and augmented datasets, covering samples in complex natural scenes such as twining vines, weed and tree cover, blurred images, and shadows. We studied the influence of complex scenes on the extraction accuracy of Pitaya trees using the U-Net model to accurately delineate Pitaya trees in complex UAV images. The results show: (1) the U-Net model trained by the augmented dataset had the highest recognition precision of 99.20% for Pitaya trees, F1-score of 96.66%, and Kappa coefficient of 0.91. (2) The number of samples and the complexity of land types had strong impact on the recognition accuracy. From 200 to 21,593 samples, the accuracy of the recognition results, F1-score and Kappa coefficient increased by 17.47%, 17.95%, and 0.26%, respectively. Moreover, the missed detection rate significantly decreased (18.27% to 0.80%), the false alarm rate (5.36% to 1.04%). (3) When the sample types were increased from 1 to 10, the learning of sample features by the U-Net model, including shadows, blurred images, and twining vines, was strengthened. This enhanced the robustness and generalization ability of the model. The small sample dataset in this study meets the requirements of identifying and extracting information for Pitaya trees from the background of the rugged terrain and complex features in plateau and mountain areas.