Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 10, June 2022

AI_Birder: An Intelligent Mobile Application to Automate Bird Classification
using Artificial Intelligence and Deep Learning


Charles Tian1 and Yu Sun2, 1USA, 2California State Polytechnic University, USA


Birds are everywhere around us and are easy to spot. However, for many beginner birders, identifying the birds is a hard task [8]. There are many apps that help the birder to identify the birds, but they are often too complicated and require good internet to give a result. A better app is needed so that birders can identify birds while not depending on internet connection.

My app, AI_Bider, is mainly built in android studio using flutter and firebase, and the AI engine is coded with TensorFlow and trained with images from the internet [9]. To test my AI engine, I made six different prototypes, each having a different number of times that the code will train from the dataset of pictures. I then selected 5 birds that are in my dataset and found 5 pictures on the internet for each of them, which I then uploaded to the app. My app will then give me 3 bird species that most closely resemble the image, as well as the app’s confidence in its choices, which are listed as percentages. I recorded down the percentages of accuracy for each picture. After taking the average percentage of all the models, I selected the most successful model, which had an average percent of accuracy of 79%.


Machine Learning, AI platform, Computer vision