Abstract:

Aims: Under the influence of global climate change and human activities, plant living environment, their living conditions, and even entire vegetation ecosystems are undergoing dynamic changes. Conducting field investigations and monitoring on plants are the foundation for the scientific community and relevant administrative departments to assess regional plant populations, speculate their development trends, and develop appropriate conservation strategies. However, field plant surveys have long relied on expert identification, requiring significant human, material and financial resources, and finding suitable experts is often challenging. Intelligent identification has been shown to achieve accurate, species-level identification of plants, and has great potential to improve the efficiency of field surveys while reducing the workload of experts. 

Methods: In this study, we focused on the eastern part of Eurasia grasslands in Nei Mongol as a case study and employed unmanned aerial vehicle (UAVs) along with image recognition technology to develop a plant image recognition model for grasslands. Pre-experiments conducted in Hulunbeier revealed that images taken by drones at a vertical angle of 90° yielded the highest number of identifiable species for model construction. Based on the drone-captured plant images from Hulunbeier, Xilinhot, and Erdos, we used an SSD-MobileNetV2-FPN architecture to train a drone-based plant object detection model. This model detected and extracted plant detection model. Further image recognition training using the MobileNetV3 architecture allowed us to construct an intelligent recognition model capable of identifying 70 common grassland plant species belonging to 54 genera across 22 families. 

Results: Using the reserved 10,734 images of 70 species for evaluation, the model correctly identified 9,513 images, achieving a Top1 recognition accuracy of 88.6%. When combined with UAV-based image acquisition, this model can survey approximately 300 1 m × 1 m quadrats in 80 min, significantly improving the efficiency of plant field investigation. 

Conclusion: This study provides a new tool for intelligent surveys and long-term site-based monitoring of grassland plant diversity and ecosystems. It reduces the dependence on experts for plant identification, providing a practical tool for biodiversity and ecosystem monitoring at the grassroots level. Additionally, this model offers a template for similar efforts in other regions and vegetation types.

Key words: unmanned aerial vehicle (UAV), artificial intelligence (AI), species recognition, grassland plants, biodiversity