PTL-Inception: Integrating Deep Learning and Taxonomy for Desert Plant Classification

Accurate identification of plant species forms the basis of taxonomy, biodiversity assessment, and conservation planning. This requirement is especially urgent in arid ecosystems such as those of Saudi Arabia, where limited rainfall, fragile habitats, and high ecological stress create distinctive but poorly documented flora. In this study, a deep learning framework, termed PTL-Inception, was developed to classify desert plants and to provide reliable taxonomic data that can be integrated into biodiversity and phylogenetic studies. A dataset of ten native species was compiled and expanded through augmentation, and several state-of-the-art architectures were tested. InceptionV3 was found to be the most effective baseline, and the network was further modified by incorporating ten additional layers, transfer learning, and hyperparameter tuning. The proposed model achieved an accuracy of 99.46%, with precision and recall values of 99.46% and 99.44%, respectively. Reliability was confirmed through K-fold validation, while early stopping reduced training time with minimal loss of accuracy. Beyond these computational outcomes, the study demonstrates how deep learning can complement traditional taxonomy by producing consistent species-level identifications. The outputs can be combined with spatial and phylogenetic approaches to explore patterns of diversity, endemism, and adaptation in desert ecosystems, thereby supporting conservation strategies and biodiversity management.