Using UAVRS and deep learning to conduct resource surveys of threatened Tibetan medicinal plants in the Qinghai-Tibet Plateau

Abstract

Gentiana szechenyii Kanitz. and Gentiana veitchiorum Hemsl. are two wild medicinal plants widely used in Tibetan medicine. In recent decades, their wild populations have declined rapidly due to persistent over-harvesting of their flowers. Because their flowers are small and dense, it is difficult to rely on manual counting under wild conditions. The combination of deep learning and unmanned aerial vehicle remote sensing (UAVRS) is a new method for plant surveys. We trained 12 of the most advanced or widely used YOLO models on a custom dataset to achieve quantitative detection of both plant flowers in UAVRS images. The accuracy, precision and recall of G. szechenyii Flower (GSF) detection can reach 97.00%, 90.40% and 95.40%, respectively (based on YOLOv7). Similarly, those of G. veitchiorum Flower (GVF) detection can reach 93.40%, 91.30% and 90.60%, respectively (based on YOLOv5n), and the highest mAP can reach 94.90% (based on YOLOv5m). Based on the detection results, it is calculated that the total yield of dried GSF and GVF that can be harvested as medicinal materials in study area ranges from 1.88 to 2.10 g·m−2 and 4.42–4.62 g·m−2, respectively. The results show that deep learning and UAVRS can be used for quantitative detection of GSF and GVF, which is helpful for further research and protection of these two plants.

Keywords

• Gentiana szechenyii
• Gentiana veitchiorum
• Unmanned aerial vehicle remote sensing (UAVRS)
• Deep learning
• Medicinal plant