An Intelligent Plant and Animal Identification Mobile Application for Increased Biodiversity Awareness and Safety using Machine Learning


Austin Xiao1, Ang Li2 and Wyatt Bodle3, 1USA, 2California State Polytechnic University, USA, 3California Baptist University, USA


Dangerous animal encounters have steadily increased over time and consumption of deadly plants is an important issue [12]. Our paper introduces a new mobile application that addresses the critical need for accurate animal and plant identification and classification to help mitigate safety risks for humans. With up to five million animal attacks reported every year in the United States alone, and over 100,000 cases of toxic plant exposure there is a need and a responsibility to increase awareness of the risks associated with animal and plant ignorance. Our proposed app utilizes innovative classification technologies, offering our users a swift and simple identification of both select plant and animal species. The app will relay information about the potential dangers and general facts about the classified animal [14]. This will help our users to understand the environment they live in and to best prepare themselves against it. Some challenges with this proposal are curating a broad and efficient dataset, there are estimated to be eight million eukaryotic species which is unattainable for one dataset. We then had to decide which valuable information would be best to present without providing unwanted distractions in our user interface. We utilized Google Firebase to ensure secure authentication and data storage while using TensorFlow Lite to power the image classification. We then integrated all of this into flutter to create a friendly user interface and application that can run on both iOS and Android [15]. Once our app was complete, we ran two experiments, one to test the accuracy of our classifications in plants and animals and another to test the effect of lower resolution images on classification accuracy. The experiments shed light on challenges and potential improvements for the application to help improve its efficiency as a tool for users to enhance their awareness, safety and understanding of the environment they live in.


Wildlife Identification, Plant and Animal Classification, Mobile Application, Biodiversity Awareness, Safety Technology

Full Text  Volume 14, Number 4