Zoeken
Zoeken kan via de modus 'eenvoudig zoeken' (één veld) of uitgebreid via 'geavanceerd zoeken' (meerdere velden). Zo kan je bv. zoeken op een combinatie van een auteursnaam (auteur), een jaartal (jaar) en een documenttype.
Boekenmand
Nuttige resultaten kan je aanvinken en toevoegen aan een mandje. De inhoud hiervan kan je exporteren of afdrukken (naar bv. PDF).
RSS
Op de hoogte blijven van nieuw toegevoegde publicaties binnen uw interessegebied? Dit kan door een RSS-feed (?) te maken van jouw zoekopdracht.
| [ meld een fout in dit record ] | mandje (0): toevoegen | toon |
 |
| Cephalopod species identification using integrated analysis of machine learning and deep learning approaches Tan, H.Y.; Goh, Z.Y.; Loh, K-H.; Then, A.Y.-H.; Omar, H.; Chang, S.-W. (2021). Cephalopod species identification using integrated analysis of machine learning and deep learning approaches. PeerJ 9: e11825. https://dx.doi.org/10.7717/peerj.11825
In: PeerJ. PeerJ: Corte Madera & London. ISSN 2167-8359; e-ISSN 2167-8359
|   |
| Beschikbaar in | Auteurs |
| |
| Trefwoorden |
Marien/Kust |
| Author keywords |
|
| Auteurs | Top | |
|
|
| Abstract |
BackgroundDespite the high commercial fisheries value and ecological importance as prey item for higher marine predators, very limited taxonomic work has been done on cephalopods in Malaysia. Due to the soft-bodied nature of cephalopods, the identification of cephalopod species based on the beak hard parts can be more reliable and useful than conventional body morphology. Since the traditional method for species classification was time-consuming, this study aimed to develop an automated identification model that can identify cephalopod species based on beak images. MethodsA total of 174 samples of seven cephalopod species were collected from the west coast of Peninsular Malaysia. Both upper and lower beaks were extracted from the samples and the left lateral views of upper and lower beak images were acquired. Three types of traditional morphometric features were extracted namely grey histogram of oriented gradient (HOG), colour HOG, and morphological shape descriptor (MSD). In addition, deep features were extracted by using three pre-trained convolutional neural networks (CNN) models which are VGG19, InceptionV3, and Resnet50. Eight machine learning approaches were used in the classification step and compared for model performance. ResultsThe results showed that the Artificial Neural Network (ANN) model achieved the best testing accuracy of 91.14%, using the deep features extracted from the VGG19 model from lower beak images. The results indicated that the deep features were more accurate than the traditional features in highlighting morphometric differences from the beak images of cephalopod species. In addition, the use of lower beaks of cephalopod species provided better results compared to the upper beaks, suggesting that the lower beaks possess more significant morphological differences between the studied cephalopod species. Future works should include more cephalopod species and sample size to enhance the identification accuracy and comprehensiveness of the developed model. |
| Top | Auteurs |
