Deep convolutional neural networks to monitor coralligenous reefs: Operationalizing biodiversity and ecological assessment

• Published in: Ecological informatics Vol. 59; p. 101110
• Main Authors: Marre, Guilhem, De Almeida Braga, Cedric, Ienco, Dino, Luque, Sandra, Holon, Florian, Deter, Julie
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2020; Elsevier
• Subjects: Biodiversity; Computer Science; Convolutional neural networks; Coralligenous reefs; Deep learning; Image classification; Image Processing; Life Sciences; Monitoring; Species recognition; Deep learning; Coralligenous reefs; Convolutional neural networks; Species recognition; Monitoring; Image classification
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2020.101110

Monitoring the ecological status of natural habitats is crucial to the conservation process, as it enables the implementation of efficient conservation policies. Nowadays, it is increasingly possible to automate species identification, given the availability of very large image databases and state-of-the-art computational power which makes the training of automated machine learning-based classification models an increasingly viable tool for monitoring marine habitats. Coralligenous reefs are an underwater habitat of particular importance, found in the Mediterranean. This habitat is of a similar biocomplexity to coral reefs. They have been monitored in French waters since 2010 using manually annotated photo quadrats (RECOR monitoring network). Based on the large database of annotations accumulated therein, we have trained convolutional neural networks to automatically recognise coralligenous species using the data gathered from photo quadrats. Previous studies conducted on similar habitats performed well, but were only able to consider a limited number of classes, resulting in a very coarse description of these often-complex habitats. We therefore designed a custom network based on off-the-shelf architectures which is able to discriminate between 61 classes with 72.59% accuracy. Our results showed that confusion errors were for the most part taxonomically coherent, showing accuracy performances of 84.47% when the task was simplified to 15 major categories, thereby outperforming the human accuracy previously recorded in a similar study. In light of this, we built a semi-automated tool to reject unsure results and reduce error risk, for when a higher level of accuracy is required. Finally, we used our model to assess the biodiversity and ecological status of coralligenous reefs with the Coralligenous Assemblage Index (CAI) and the Shannon Index. Our results showed that whilst the prediction of the CAI was only moderately accurate (pearson correlation between observed and predicted CAI = 0.61), the prediction of Shannon Index was more accurate (pearson correlation = 0.74). In conclusion, it will be argued that the approach outlined by this study offers a cost and time-effective tool for the analysis of coralligenous assemblages which is suitable for integration into a large-scale monitoring network of this habitat. •Cost and time-effective tool for the analysis of coralligenous assemblages•Multi-scale image analysis increases classification performances•CNNs outperform a trained taxonomist on a 15 coralligenous-class problem•Semi-automated classification reduces the error rate while reducing processing time