Comparative Analysis of the Accuracy of Thirteen Photo-based Applications in the Identification of Trees Species in Northern Ghana

Abstract

Photo-based tree identification applications have gained prominence for their convenience and speed of identifying tree species. However, the accuracy of these applications remains a critical concern. This study assessed the accuracy of thirteen photo-based tree identification applications in the identification of twenty-two tree species in the savannah ecological zone of Ghana using their leaf samples. Three photographs were taken from well-developed and diseased-free leaves from each tree, in their natural environment, using a Samsung Galaxy Tab S4 phone with inbuilt camera. The photographs were uploaded into the respective Applications’ platforms for processing and identification. This study used 858 photographs, from 22 plant species using 13 photo-based applications. The number of times an application was able to identify the species were recorded and analysed using Microsoft Excel. The results showed that mean identification accuracy was 65.03%. Seven of the applications were able to identify the tree species up to 72.73 - 95.45% accuracy. The remaining eight had an accuracy range of 9.90 -50.09%. LeafSnap, PlantNet and PlantID had the same and the highest accuracy rate of 95.45% corresponding to 21 of the plant species identified. These were closely followed by Plant ID, PictureThis, Google Lens and Nature ID with accuracy rates of 86.36%, 81.81%, 77.27% and 72.73%, respectively. Plant-X had the least accuracy of 9.09%. The study recommended applications with identification accuracy above 70% for used in identifying species in the Savanna Zone of Ghana. The results of this study have significant implications for the identification and management of tree species as non-taxonomists, non-botanists and non-naturalists will be able to work effectively. Further studies, consideration up to the family level, are needed to further improve the accuracy of these applications. The study also recommends the inclusion of other plant organs such as the fruits, flowers and bark in future research works. It is also recommended that other Photo-based tree identification applications be studied, and the results compared with findings of this study to assist select the best applications for identifying trees in the Savanna Zone of Northern Ghana.