Butterfly and Moth Species Detection and Classification Using Deep Learning

Yadav, Deepak (2023) Butterfly and Moth Species Detection and Classification Using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Butterflies bring to mind images of meadows, colorful flower filled landscapes and lively summer gardens. Aside from their appeal, areas rich in butterflies and moths indicate an ecosystem that supports a wide variety of invertebrate populations. This research delves into the classification of images specifically focusing on identifying 100 species of butterflies and moths. The dataset used consists of 12,594 training images 500 validation images and 500 test images all sized at 224 x 224 pixels. By employing four models including pretrained models such as EfficientNetB0, ResNet50, VGG19 Models and a custom CNN model named ButterflyNet. ResNet50 stands out with a test accuracy of 95% closely followed by EfficientNetB0 at 93.60% VGG19 Model at 92.80% and ButterflyNet at 85.40%. Moreover incorporating an interactive Streamlit UI enhances accessibility by allowing users to conduct real time tests. In conclusion ResNet50 emerges as the model while ButterflyNet shows promising potential. Future efforts should explore tuning techniques, ensemble methods and continuous model optimization to contribute to the evolving field of image classification and its crucial role, in biodiversity conservation through technological advancements.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Uncontrolled Keywords:	ButterflyNet Model; CNN; deep learning; image processing
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science S Agriculture > SF Animal culture Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Computer vision
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	26 May 2025 09:15
Last Modified:	26 May 2025 09:15
URI:	https://norma.ncirl.ie/id/eprint/7641

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