Classification of bird species using deep learning

Abstract

Many bird species are rare to find these days, and even when they are, it might be difficult to classify them. For example, birds have different sizes, shapes, and colors depending on the situation, and they also have different perspectives as anthropoid observers. Without a doubt, the images show altered conditions that should be recorded using image recognition software for bird species. It is also a calm way for people to identify the birds in the pictures. This study presents a variety of image processing methods for identifying various bird species. The goal of the study is to identify the kind of bird using 11 classes of bird’s photos. Several algorithms and techniques have been developed to reliably identify and categorize photos according on whether or not birds are present. Eleven different class types were used in this experiment, including deep learningbased models including the Black Drango, Common Myna, Common Tailor Bird, Crow, Dove, Greater Coucal, Pigeon, Sparrow, Kingfisher, Magpie, and Heron. To predict and identify bird species, five models—MobileNetV2, DenseNet169, InceptionV3, VGG16, and VGG19—are used. Lastly, two distinct performance assessments are used to evaluate the technique's outcomes. The first accuracy set, assessment of performance for bird species conditions, employs four probable final results: TP, TN, FP, and FN. The accuracy of every kind of bird in error scenarios is then examined using these models. With the 94.92% accuracy rate of the MobileNetV2 technique, my proposed solution paves the way for autonomously recognition of various species in birds. To create a web prototype, the MobileNetV2 network is ultimately used for classification to identify different species of birds