Automatic identification of native trees using MobileNetV2 model - Pagudpud, Melidiossa V.; Rustia, Reynold A.; Marzo, Wilyn S.; Carig, Joel G. (Institute of Advanced Engineering and Science, 2025)
In protecting our biodiversity, knowledge of tree species is vital. However, not all people are familiar with the trees present in the community which can affect their ability to fully protect the trees. In this premise that the researchers decided to conduct this study to support the sustainable forest management project in the Province of Quirino through the creation of a model of automatic identification of native trees, using the leaves of the trees, found within the Quirino Forest landscape. The model aims to help residents with accessible tools for tree identification which can be used in the conservation efforts within the province. Transfer learning for deep learning, one of the latest advancements in image processing, shows potential for tree identification because the method dodges the labor intensive feature engineering. Using the Quirino Province native trees leaf/leaflet images dataset, which was annotated by foresters, the MobileNetV2 convolutional neural network was evaluated systemically in this paper. The result shows that the best model version to classify the native trees based on their leaves or leaflets is the one produced using 800 training steps which yields an overall accuracy of 89.61%. The result attained for the tree identification indicates that the proposed technique might be an appropriate tool to assist humans in the identification of native trees found within the landscape of Quirino and can provide reliable technical support for sustainable forest management.