Key points are not available for this paper at this time.
People from all around the world face problems in the identification of fish species and users need to have access to scientific expertise to do so and, the situation is not different for Mauritians. Thus, in this project, an innovative smartphone application has been developed for the identification of fish species that are commonly found in the lagoons and coastal areas, including estuaries and the outer reef zones of Mauritius. Our dataset consists of 1520 images with 40 images for each of the 38 fish species that was studied. Eighty-percent of the data was used for training, ten percent was used for validation and the remaining ten percent was used for testing. All the images were first converted to the grayscale format before the application of a Gaussian blur to remove noise. A thresholding operation was then performed on the images in order to subtract the fish from the background. This enabled us to draw a contour around the fish from which several features were extracted. A number of classifiers such as kNN, Support Vector Machines, neural networks, decision trees and random forest were used to find the best performing one. In our case, we found that the kNN algorithm achieved the highest accuracy of 96%. Another model for the recognition was created using the TensorFlow framework which produced an accuracy of 98%. Thus, the results demonstrate the effectiveness of the software in fish identification and in the future, we intend to increase the number of fish species in our dataset and to tackle challenging issues such as partial occlusions and pose variations through the use of more powerful deep learning architectures.
Pudaruth et al. (Thu,) studied this question.