Crab Species Classification Using Deep Learning Techniques

Author Reg. ID: TIJER_151441

Published Paper Id: TIJER2403068

Published In: Volume 11 Issue 3, March-2024

Abstract: Crab species classification is based on the deep learning model. Crab species play an important role in undertaking environmental and biological marine research. This helps in the accurate categorization of crab species, this study offers a combined approach of convolutional neural networks (CNN) and artificial neural networks (ANN). Finding the model with the best accuracy in detecting photos of crabs is our main goal. To prepare pictures for input into CNN models, we first pre-process them by removing frames from the dataset. The study examines the efficiency of CNN, a cutting-edge deep learning model, and ANN, a traditional machine learning algorithm, in differentiating between several crab species based on visual data. Utilizing feature extraction, the study looks at how well they do in classifying crab species from photos. Furthermore, the CNN model is created and trained using a dataset that includes the coconut crab and vampire crab. The models are assessed and contrasted according to how well they can categorize photos from a dataset of actual crabs. Then, using the prepared data, we build and train each CNN architecture, including strategies like data augmentation that boost the prediction and strength of the model. Performance assessment makes use of criteria like correctness.

Keywords: Image Classification, Crab Species, Convolutional Neural Network, Artificial Neural Network, Comparative analysis.

Downloads: 00051

Page No: a506-a511

Country: Mumbai, Maharashtra, India

Research Area: Others area

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403068

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403068

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