Plant Taxonomy In Hainan Based On Deep Convolutional Neural Network And Transfer Learning

Plant play a very important role in the protection of the ecological balance. Compared to manual identification of plants, automated plant identification enable experts to process significantly greater numbers of plants with higher efficiencies in shorter periods of time. In this study, we propose an effective deep Convolutional Neural Network (CNN)-based model that is capable of automatically identifying and classifying plant species in Hainan by studying the details of their leaves. We apply transfer learning based on CNN to fine-tune the pre-trained models. Further, the optimal values of associated hyperparameters that maximize the accuracy of the proposed method are determined. Finally, experiments are carried out on two available botanical datasets: the Flavia dataset with 32 classes and the HNPlant dataset with 10 classes. The results demonstrate that the highest classification accuracies exhibited by the proposed CNN-based model on the Flavia and HNPlant datasets are 89% and 95%, respectively, thus establishing their effectiveness.