Hybrid transfer learning model for identification of plant species

Abstract

Plants are an important part of everyone’s life on this planet. Today there are many species of plant are present on earth, classifying them has become a challenge because some plants look similar but are not same and there are many species of plant on earth to remember. Identifying the plant is easy for those who study about plants but for a common human, it is difficult. Thus, this research paper proposes a deep learning model to classify the plant leaf which has the capability to automatically extract features from images. The input is given to two architectures Xception and ResNet50v2 and the features extracted from these architectures are concatenated and given to fully connected network also known as transfer learning. The concatenated network gives comprehensive understanding about the dataset which would help it to perform well. The concatenated model shows an accuracy of 96.38% training accuracy and 89.36% validation accuracy on One-hundred plant species dataset and 95% training accuracy and 91.6% validation accuracy on Leafsnap dataset. The results of proposed model are compared with Xception model and ResNet50v2 model.