Local bird species classification using advanced deep learning architecture

Abstract

The goal of this research is to create a reliable system for classifying local bird species using modern deep learning architectures. The dataset forms the basis for this study and consists of 1,179 photos covering seven different species of birds. A combination of deep learning and transfer learning algorithms, such as "InceptionV3," "VGG19," "VGG16," "MobileNetV2," "CNN01," and "CNN02," are used in the search for precise and effective classification. The process is a painstaking procedure that begins with web crawling to add to the dataset after data collection from web scraping. Accurate annotations for model training are ensured via manual data labeling. The construction and training of convolutional neural network (CNN) models is the study's central focus. Important designs are used, including 'InceptionV3,' which is well-known for its deep and effective design, 'VGG19' and 'VGG16' with uniform architectures, and 'MobileNetV2,' which is especially made for devices with limited resources. Furthermore, flexibility is offered by the generic CNN designs ('CNN01' and 'CNN02'). The experimental findings show that the 'MobileNetV2' design produced the maximum accuracy of 99.58%. This shows how well the model can classify local bird species and make generalizations. The study highlights the importance of transfer learning via using model training to improve productivity and faster convergence. The accuracy obtained is proof of the effectiveness of the selected deep learning architectures for classifying bird species .This work offers a dependable and automated method for identifying local bird species and provides useful details about the use of modern deep learning methods in bird study. The results have effects on protecting nature, environmental monitoring, and the larger field of computer vision in nature research.