Development of species recognition models using Google teachable machine on shorebirds and waterbirds

Abstract

Species identification is an essential ability in every conservation initiative. An efficient and robust computer vision method was attested with an available online tool with Google's Teachable Machine. This pilot study on developing a species recognition app was to create and evaluate the usability and accuracy of using Teachable Machine for species identification at Teluk Air Tawar, Kuala Muda (TAT-KM), Malaysia. The accuracy of the created models was evaluated and compared with training images based on the web-mining (Google Images Repository) compared to actual photos taken at the same site. Model A (Google Image) had an average accuracy of 55.30%, while Model B (actual photos) was 99.42%. Regarding success rate at accuracy over 77%, 27 out of 49 test images (55.10%) were reported in Model A, while Model B had a 100% success rate. This approach can replace traditional methods of bird species recognition to handle large amounts of data.

Keywords

• Machine learning
• Google teachable machine
• species classification
• shorebirds
• important bird area