Urban Planter: A Web App for Automatic Classification of Urban Plants

Abstract

Plant classification requires an expert because subtle differences in leaves or petal forms might differentiate between different species. On the contrary, some species are characterized by high variability in appearance. This paper introduces a web app for assisting people in identifying plants for discovering the best growing methods. The uploaded picture is submitted to the back-end server, and a pre-trained neural network classifies it to one of the predefined classes. The classification label and confidence are displayed to the end user on the front-end page. The application focuses on the house and garden plant species that can be grown mainly in a desert climate and are not covered by existing datasets. For training a model, we collected the Urban Planter dataset. The installation code of the alpha version and the demo video of the app can be found on https://github.com/UrbanPlanter/urbanplanterapp.