Automatic Nile Tilapia fish classification approach using machine learning techniques

Fouad, Mohamed Mostafa; Zawbaa, Hossam M.; El-Bendary, Nashwa; Hassanien, Aboul Ella

doi:10.1109/his.2013.6920477

Cited by 58 publications

(31 citation statements)

References 15 publications

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“…Fish recognition is very complicated and difficult task but is useful to business and agriculture. Distortion, overlap, noise, distortion, occlusion, and also error in segmentation are among the challenges faced in achieving accurate and reliable fish recognition [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Among the works relevant to this study is one from Mokti and Salam [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Robust features extraction for general fish classification

Alsmadi

Tayfour

Alkhasawneh

et al. 2019

IJECE

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Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family. A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Robust features extraction for general fish classification

Alsmadi

Tayfour

Alkhasawneh

et al. 2019

IJECE

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“…Fouad et al [1] classified tilapia and non-tilapia using image processing techniques and machine learning. They used scale invariant feature transformation [14] and SURF (speeded up robust features) [15] in the feature extraction phase, and a SVM (support vector machine), artificial neural networks, and K-nearest neighbor in the classification stage.…”

Section: A Fish Classificationmentioning

confidence: 99%

“…Fish image classification has been studied since decades [1]- [7]. Generally, the existing methods address…”

Section: Introductionmentioning

confidence: 99%

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Automatic Mackerel Sorting Machine Using Global and Local Features

et al. 2019

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In Japan, blue and chub mackerels are often caught simultaneously, and their market prices are different. Humans need to sort them manually, which requires heavy labor. The demand for automatic sorting machines is increasing. The aim of this paper is to develop an automatic sorting machine of mackerels, which is a challenging task. There are two required functions. First, it needs localization of mackerels on a conveyor belt so that mackerels can be transported to destinations. Second, species classification is needed, but it is difficult due to similar appearance among the species. In this paper, we propose an automatic sorting machine using deep neural networks and a red laser light. Specifically, we irradiate red laser to the abdomen, and the shape of the laser will be circle and ellipse on the blue and chub mackerels, respectively. We take images and use neural networks to locate the whole body and irradiated regions. Then, we classify mackerels using features extracted from the whole body and irradiated regions. Using both features makes the classification accurate and robust. The experimental results show that the proposed classification is superior to the methods using either feature of irradiated or whole body regions. Moreover, we confirmed that the automatic mackerel-sorting machine performs accurately.INDEX TERMS Convolutional neural networks, fish classification, fish localization.

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“…The second type of research has greater industrial application because it is aimed at discriminating individual caught fish. Fouad et al developed a machine-learning method for discriminating Nile tilapia from other Nile-River fishes [17]. Both scale invariant feature transformation (SIFT) [18] and speeded up robust features (SURF) [19] were separately applied to extract distinct features from a whole image of the fish.…”

Section: Related Workmentioning

confidence: 99%

Automatic Discrimination between Scomber japonicus and Scomber australasicus by Geometric and Texture Features

Kitasato

Miyazaki

Sugaya

et al. 2018

Fishes

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This paper proposes a method for automatic discrimination of two mackerel species: Scomber japonicus (chub mackerel) and Scomber australasicus (blue mackerel). Because S. japonicus has a much higher market price than S. australasicus, the two species must be properly sorted before shipment, but their similar appearance makes discrimination difficult. These species can be effectively distinguished using the ratio of the base length between the dorsal fin's first and ninth spines to the fork length. However, manual measurement of this ratio is time-consuming and reduces fish freshness. The proposed technique instead uses image processing to measure these lengths. We were able to successfully discriminate between the two species using the ratio as a geometric feature, in combination with several texture features. We then quantitatively verified the effectiveness of the proposed method and demonstrated that it is highly accurate in classifying mackerel.

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Automatic Nile Tilapia fish classification approach using machine learning techniques

Cited by 58 publications

References 15 publications

Robust features extraction for general fish classification

Robust features extraction for general fish classification

Automatic Mackerel Sorting Machine Using Global and Local Features

Automatic Discrimination between Scomber japonicus and Scomber australasicus by Geometric and Texture Features

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