AppTree: An Intelligent Platform for Discovering the World of Plants

Document Type : Original Article


University of Tehran, Tehran, Iran


“AppTree” is an intelligent platform to bring researchers, visitors, and all interested people closer to the oldest and most attractive botanical garden at the University of Tehran. AppTree can scan the QR-Barcode of each plant in person by smartphone or search various plants on the website and get all the useful knowledge about them. Also, the ability of AppTree is the recognition of different plants which don’t have labels. The plant recognition part is a machine learning module that can identify more than 100 different species of plants and give the user details about them. This novel platform is based on Android and Web-app and the identification of new plants type is done by machine learning approach. We utilized VGG19, a deep CNN, to classify images and to identify unlabeled plants. The classification accuracy, F1-score, recall, and precision were 98.25, 93.16%, 88.21%, and 94.85%, respectively, on the plant dataset of the University of Tehran. The proposed method was compared with other deep learning architectures such as AlexNet, AlexNetOWTBn, and GoogLeNet on the same dataset and obtained higher performance. Our AppTree platform has achieved considerable success and easily can be extended to use in other botanical gardens.


Main Subjects

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 Yeganeh Madadi received her PhD in Artificial Intelligence in 2020, and her MSc in Computer Science in 2015. She is currently employed as a Full-stack developer and researcher of Artificial Intelligence at the University of Tehran and at the same time works as a University Lecturer in Iran. Her primary research interests are machine learning and computer vision.
Mahmoud Omid is a professor in University of Tehran, Iran. His special field of interest include artificial intelligence and machine vision. His current research interests include computational intelligence and computer vision in the areas of Biosystems Engineering.