Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3398
Title: MULTI-CLASS CLASSIFICATION OF INSECTS USING DEEP NEURAL NETWORKS
Authors: Santhiya M
Priyadharshini M
Agshalal Sheeba J
Karpagavalli S
Keywords: Industries
Deep learning
Analytical models
Insects
Computational modeling
Neural networks
Computer architecture
Issue Date: 23-Jan-2023
Publisher: IEEE Xplore
Abstract: Insects are crucial to the functioning of nature. There are more than a million described species of living beings in the modern world. Since the majority of today’s farmers and agriculturalists are newer generations of people, identifying and classifying insects is essential. The classification of insects is a difficult undertaking in the agricultural industry. In the proposed work, multi-class classification of insects using a Convolutional Neural Network architecture, VGG19 had been carried out. In the taxonomic classification of insects, 5 insects fall within insecta class which include butterfly, dragonfly, grasshopper, ladybird, and mosquito data had been collected to train, test, and validate the convolutional neural network, The performance of the model had been analyzed using different parameters and presented.
URI: https://ieeexplore.ieee.org/document/10128549
Appears in Collections:International Conference

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