A biometric-based model for fish species classification

Tharwat, Alaa; Hemedan, Ahmed Abdelmonem; Hassanien, Aboul Ella; Gabel, Thomas

doi:10.1016/j.fishres.2018.03.008

Cited by 47 publications

(15 citation statements)

References 19 publications

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“…Because global features such as texture or color features have obtained good results in the classification task in the literature [46][47][48], we extracted and combined several global features from all images, which are summarized in Table 2. Table 2.…”

Section: Description Obtained Featuresmentioning

confidence: 99%

“…On the basis of the choices made by some studies in the literature of similar scope [47,76], every classifier was validated by 10-fold cross-validation by considering that the elements of each class were distributed evenly in each one of the folds. The performance of the models was evaluated by the accuracy, loss, and area under the curve (AUC) average scores [81].…”

Section: Metricsmentioning

confidence: 99%

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Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

López-Vázquez¹,

López-Guede

Marini

et al. 2020

Sensors

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An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

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Section: Description Obtained Featuresmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

López-Vázquez¹,

López-Guede

Marini

et al. 2020

Sensors

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show abstract

“…In general, artificial neural networks (ANNs) have been widely used in many applications such as image processing [29], weather forecasting [30], medicine [31], biology [32–36], robotics [37], and the nonlinear modeling of temporal data [19,31,38,39]. They have also been applied to fish data mainly for species identification [40–45], prediction [46,47], and classification [48,49]. Additionally, they have been used as a forecasting tool for fisheries [50].…”

Section: Discussionmentioning

confidence: 99%

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

Rungruangsak‐Torrissen

Manoonpong

2019

PLoS ONE

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The neural computational model GrowthEstimate is introduced with focusing on new perspectives for the practical estimation of weight specific growth rate (SGR, % day –1 ). It is developed using recurrent neural networks of reservoir computing type, for estimating SGR based on the known data of three key biological factors relating to growth. These factors are: (1) weight (g) for specifying the age of the growth stage; (2) digestive efficiency through the pyloric caecal activity ratio of trypsin to chymotrypsin (T/C ratio) for specifying genetic differences in food utilization and growth potential, basically resulting from food consumption under variations in food quality and environmental conditions; and (3) protein growth efficiency through the condition factor (CF, 100 × g cm –3 ), as higher dietary protein level affecting higher skeletal growth (length) and resulting in lower CF. The computational model was trained using four datasets of different salmonids with size variations. It was evaluated with 15% of each dataset, resulting in an acceptable range of SGR outputs. Additional tests with different species indicated similarity between the estimated SGR outputs and the real SGR values, and the same ranking of wild population growth. The developed model GrowthEstimate is exceptionally useful for the precise and comparable growth estimation of living resources at individual levels, especially in natural ecosystems where the studied individuals, environmental conditions, food availability and consumption rates cannot be controlled. It is a revelation and will help to minimize uncertainty in wild stock assessment process. This will improve our knowledge in nutritional ecology, through the biochemical effects of climate change and environmental impact on the growth performance quality of aquatic living resources in the wild, as well as in aquaculture. The original GrowthEstimate software is available at GitHub repository ( https://github.com/RungruangsakTorrissenManoonpong/GrowthEstimate ). All other relevant data are within the paper. It will be improved for generality for future use, and required co-operations of the biodata collections of different species from different climate zones. Therefore, a co-operation will be available.

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“…As such, hyperspectral analysis differs from conventional analysis of photographs or other digital media (e.g. 8 , 9 ). Matter reflects, absorbs, and emits radiation in varying proportions at different wavelengths, creating what is known as a spectral signature , measured light encoded with information.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral data as a biodiversity screening tool can differentiate among diverse Neotropical fishes

Kolmann

Kalácska

Lucanus³

et al. 2021

Sci Rep

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Hyperspectral data encode information from electromagnetic radiation (i.e., color) of any object in the form of a spectral signature; these data can then be used to distinguish among materials or even map whole landscapes. Although hyperspectral data have been mostly used to study landscape ecology, floral diversity and many other applications in the natural sciences, we propose that spectral signatures can be used for rapid assessment of faunal biodiversity, akin to DNA barcoding and metabarcoding. We demonstrate that spectral signatures of individual, live fish specimens can accurately capture species and clade-level differences in fish coloration, specifically among piranhas and pacus (Family Serrasalmidae), fishes with a long history of taxonomic confusion. We analyzed 47 serrasalmid species and could distinguish spectra among different species and clades, with the method sensitive enough to document changes in fish coloration over ontogeny. Herbivorous pacu spectra were more like one another than they were to piranhas; however, our method also documented interspecific variation in pacus that corresponds to cryptic lineages. While spectra do not serve as an alternative to the collection of curated specimens, hyperspectral data of fishes in the field should help clarify which specimens might be unique or undescribed, complementing existing molecular and morphological techniques.

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A biometric-based model for fish species classification

Cited by 47 publications

References 19 publications

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency

Hyperspectral data as a biodiversity screening tool can differentiate among diverse Neotropical fishes

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