AUTOMATIC FISH SPECIES IDENTIFICATION USING DEEP LEARNING TECHNIQUE

Abstract

In recent years, the growing global population has led to an increased demand for animal protein, including fish and other aquatic products. Aquaculture has emerged as a primary method for meeting this demand. There is a need for reliable and accurate methods to identify fish species. However, the accurate identification of fish species remains a challenge as there are various fish species endemic to different regions. This research focuses on addressing this challenge by developing a system for automatic fish species identification using deep learning technique, with a specific emphasis on convolutional neural network (CNN). To accomplish the objective of the research, fish species images were collected from Lake Hawassa. The collected dataset was certified by domain experts from the Centre for Aquaculture Research and Education (CARE) at Hawassa University. A custom dataset was prepared, consisting of a total of 6000 images of six fish species: Oreochromis niloticus, Clarias garipienus, LabeoBarbus intermedius, Barbus paludinosis, Garra quadrimaculata, and Aplocheilichthys. The proposed system for fish species identification implements a preprocessing module that involves image resizing and pixel value normalization to ensure uniformity and enhance training performance. Data augmentation techniques were utilized to generate diverse training examples. For classification, convolutional neural network (CNN) is employed, either trained using Convolutional neural network (CNN) architectures or utilizing pre-trained models such as Inceptionv3, VGG16, and ResNet50. Evaluation metrics were employed with two different dataset ratios: 70/30 and 80/20 and also three pre-trained models were used for comparison. The results demonstrate that our proposed model 70/30 ratio outperforms the pre-trained models in terms of training, testing accuracy, as well as loss. Our model achieved a training accuracy of 100%, validation accuracy of 99.7% and a testing accuracy of 99.5%, indicating better learning and classification capabilities. Additionally, the model achieved a recall, precision and f1 score of 100%. This research contributes to the field of fish species identification. By leveraging deep learning techniques, Particularly CNN, our model achieves better accuracy in automatic fish species identification. It reduces reliance on expert skills, addresses unresolved problems, and contributes to the progress of accurate fish species identification