Bird Image Retrieval & Recognition Using a Deep Learning Platform

Bird watching is a popular hobby among nature enthusiasts that has been enjoyed for centuries. With the advancement of technology, it is now possible to use deep learning platforms to identify and track different species of birds. The use of deep learning platforms in bird watching enables the collection of vast amounts of data on bird behaviour and population trends. This data can be used to gain insights into bird migration patterns, breeding habits, and other important aspects of bird behaviour. A CNN that was trained to locate and identify features in bird images. The network used a region of interest to improve the accuracy of object recognition and balanced the distribution of bird species to avoid bias. Additionally, the outputs of previous and more current layers were mixed using the skip connection method to enhance feature extraction. The learned parameters were then utilised to recognise birds in photos submitted by mobile users after the SoftMax algorithm was used to create a probability distribution of bird feature values. The proposed CNN model performed better in terms of accuracy and average sensitivity and specificity when compared to earlier methods., and accuracy of 93.79%, 96.11%, and 95.37%, respectively, for the test dataset.