Automated Identification and Classification of Plant Species in Heterogeneous Plant Areas Using Unmanned Aerial Vehicle-Collected RGB Images and Transfer Learning

Abstract

Biodiversity regulates agroecosystem processes, ensuring stability. Preserving and restoring biodiversity is vital for sustainable agricultural production. Species identification and classification in plant communities are key in biodiversity studies. Remote sensing supports species identification. However, accurately identifying plant species in heterogeneous plant areas presents challenges in dataset acquisition, preparation, and model selection for image classification. This study presents a method that combines object-based supervised machine learning for dataset preparation and a pre-trained transfer learning model (EfficientNetV2) for precise plant species classification in heterogeneous areas. The methodology is based on the multi-resolution segmentation of the UAV RGB orthophoto of the plant community into multiple canopy objects, and on the classification of the plants in the orthophoto using the K-nearest neighbor (KNN) supervised machine learning algorithm. Individual plant species canopies are extracted with the ArcGIS training dataset. A pre-trained transfer learning model is then applied for classification. Test results show that the EfficientNetV2 achieves an impressive 99% classification accuracy for seven plant species. A comparative study contrasts the EfficientNetV2 model with other widely used transfer learning models: ResNet50, Xception, DenseNet121, InceptionV3, and MobileNetV2.