Automatic Fish Species Classification Using Deep Convolutional Neural Networks

Iqbal, Muhammad; Wang, Zhijie; Ali, Zain Anwar; Riaz, Shazia

doi:10.1007/s11277-019-06634-1

Cited by 83 publications

(51 citation statements)

References 19 publications

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“…This is justified due to the fact that these problems are usually characterized by having image and video records as input data (non-structured data). Having such data implies the use of automatic information extraction by means of computer vision systems, based on DL, which allow extraction of high-dimensional patterns embedded in image and video data [ 97 , 98 , 99 , 100 , 101 , 102 , 103 ].…”

Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

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Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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“…They were able to classify 24 fish families with 90% accuracy. Iqbal et al [28] used a modified AlexNet [29] model to classify six different fish species with 90% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Automatic Metadata Generation for Fish Specimen Image Collections

Pepper

Greenberg

Bakis

et al. 2021

Preprint

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Metadata are key descriptors of research data, particularly for researchers seeking to apply machine learning (ML) to the vast collections of digitized specimens. Unfortunately, the available metadata is often sparse and, at times, erroneous. Additionally, it is prohibitively expensive to address these limitations through traditional, manual means. This paper reports on research that applies machine-driven approaches to analyzing digitized fish images and extracting various important features from them. The digitized fish specimens are being analyzed as part of the Biology Guided Neural Networks (BGNN) initiative, which is developing a novel class of artificial neural networks using phylogenies and anatomy ontologies. Automatically generated metadata is crucial for identifying the high-quality images needed for the neural network's predictive analytics. Methods that combine ML and image informatics techniques allow us to rapidly enrich the existing metadata associated with the 7,244 images from the Illinois Natural History Survey (INHS) used in our study. Results show we can accurately generate many key metadata properties relevant to the BGNN project, as well as general image quality metrics (e.g. brightness and contrast). Results also show that we can accurately generate bounding boxes and segmentation masks for fish, which are needed for subsequent machine learning analyses. The automatic process outperforms humans in terms of time and accuracy, and provides a novel solution for leveraging digitized specimens in ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories worldwide.

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“…Recently, [23] proposed a Deep CNN for automatic fish species identification based on the AlexNet model using four convolutional layers and two fully connected layers. The proposed model was applied for freshwater fish farming images from six species, resulting after data augmentation by zooming, rotation, and flipping in a total of 1334 images.…”

Section: Related Workmentioning

confidence: 99%

Analysis of color feature extraction techniques for Fish Species Identification

Freitas¹,

Pache²,

Gonçalves³

et al. 2020

Anais Do XVI Workshop De Visão Computacional (WVC 2020)

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Color recognition is an important step for computer vision to be able to recognize objects in the most different environmental conditions. Classifying objects by color using computer vision is a good alternative for different color conditions such as the aquarium. In which it is possible to use resources of a smartphone with real-time image classification applications. This paper presents some experimental results regarding the use of five different feature extraction techniques to the problem of fish species identification. The feature extractors tested are the Bag of Visual Words (BoVW), the Bag of Colors (BoC), the Bag of Features and Colors (BoFC), the Bag of Colored Words (BoCW), and the histograms HSV and RGB color spaces. The experiments were performed using a dataset, which is also a contribution of this work, containing 1120 images from fishes of 28 different species. The feature extractors were tested under three different supervised learning setups based on Decision Trees, K-Nearest Neighbors, and Support Vector Machine. From the attribute extraction techniques described, the best performance was BoC using the Support Vector Machines as a classifier with an FMeasure of 0.90 and AUC of 0.983348 with a dictionary size of 2048.

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Automatic Fish Species Classification Using Deep Convolutional Neural Networks

Cited by 83 publications

References 19 publications

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Automatic Metadata Generation for Fish Specimen Image Collections

Analysis of color feature extraction techniques for Fish Species Identification

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