A Network based Approach for Automated Identification of Calanoid Copepods using Deep Learning
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Abstract
In recent years, deep learning techniques have played an important role in the biological field. The present study proposes a convolutional neural network approach for identification of calanoid copepods Temora discaudata and Canthocalanus pauper. T. discaudata and C. pauper classifies with the image dataset to develop for classifying the species. Nowadays, ecological science is improved by advances in Artificial Intelligence (AI) for the classification of different species images. The digital image technique includes augmentation, pre-processing, segmentation, and classification, and is implemented based on deep learning algorithms to improve classification of species with different features with Convolutional Neural Networks (CNNs). This study proposed pre processing with the size of 64×64 and 224×224 of the calanoid species, then augmentation followed by classification. Also, image processing is focused to implement the original image to the binary mask for yielding better accuracy. During the classification of the T. discaudata and C. pauper images, the macro average and weighted average are calculated for finding 90% and 93% accuracy, respectively of the training model. The conventional method of identification of calanoid copepods is tedious, while, the CNN can automatically predict the species features from the data set. Finally, the experiment analyzed the T. discaudata and C. pauper datasets in the technical aspect of digital image processing techniques in Artificial Intelligence.
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