Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques

Petrellis, Nikos

doi:10.3390/app11104416

Cited by 35 publications

(29 citation statements)

References 29 publications

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“…Yu et al [32] segment fish images and measure fish morphological features using Mask R-CNN. Petrellis [33] employs image processing and deep learning to calculate a small set of geometric features from in-the-wild images of fish. Hao et al [34]provide an excellent review of fish measurement efforts that utilize machine vision.…”

Section: Fish Image Analysismentioning

confidence: 99%

Computational metadata generation methods for biological specimen image collections

et al. 2022

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Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the available metadata is often sparse and, at times, erroneous. This paper extends previous research with the Illinois Natural History Survey (INHS) collection (7,244 specimen images) using computational approaches to analyze image quality, and then automatically generate 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of Wisconsin Zoological Museum (UWZM) collection (4,155 specimen images). Further, we enhance our computational methods in four ways: 1) augmenting the training set, 2) applying contrast enhancement, 3) upscaling small objects, and 4) refining of our processing logic. Together these new methods improved our overall error rates from 4.6% to 1.1%. These enhancements also allowed Computational Metadata Generation Methods us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with 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 world-wide by generating accurate and valuable metadata for those repositories.

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Section: Fish Image Analysismentioning

confidence: 99%

Computational metadata generation methods for biological specimen image collections

et al. 2022

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“…The types of algorithms from Deep Learning include Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LTSM), Self-Organizing Maps (SOM), and so on. The 18 steps of the algorithm [11] can be seen in Figure 2. There are two models of deep learning [15], namely:…”

Section: Deep Learningmentioning

confidence: 99%

“…According to Nikos Petrelis, this research has used the Image Processing method to detect the fish's shape, length, and pattern [11]. In this Image Processing method, there are several ways to process in the form of images with the OpenCV library shown in Figure 3 and Figure 4.…”

Section: Figure 2 18 Steps Of Algorithmmentioning

confidence: 99%

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Cultivation of Flowerhorn Species in Search of Superior Quality Seeds using IoT and Open CV

Ngarianto¹,

Purwanto²,

Andrean³

2022

IJETAE

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The Flowerhorn is a type of ornamental fish that many fish hobbyists are attracted to because of the uniqueness of its head and the beauty of the color of the scales. And has a high selling value. The treatment requires equipment: An aquarium, oxygen aerator pump, water filter, water pH meter, UV lamp, and fish feed. In keeping Flowerhorn fish in the aquarium, you must pay attention to the acidity of the water (pH) and the feeding schedule according to the needs of the Flowerhorn fish age.Flowerhorn fish is one of the most popular ornamental fish. However, it is a little difficult to find superior seeds. To get superior quality Flowerhorn fish, IoT technology is needed to monitor the development of Flowerhorn fish using a Deep Learning approach, namely the Convex Hull Algorithms from OpenCV. OpenCV is a method that can detect objects using a camera, and the results of the image storage will be saved to a computer database that can process objects that have been tracked. Using this IoT and OpenCV system, you can feed fish, adjust water pH automatically, and identify all Flowerhorn fish in the aquarium using a camera. And can distinguish the color pattern and size of the forehead of the ideal Flowerhorn fish. So, Flowerhorn fish develop optimally and have a high selling value.

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“…Supervised learning is commonly used for classification and regression, where using data as a sample after trained by machine learning model which have the same target values [21]. From the theory of machine learning as well as its advantages, there are several implements in aquaculture recently such as biomass fish detection [22], size estimates [23][24][25], weight estimates [26][27][28], count [29][30][31][32], fish recognition [33][34][35][36][37][38], age detection [39,40], sex identification [34,[41][42][43], fish species classification [44][45][46][47][48][49][50], feeding behavior [51,52], group behavior [53], abnormal behavior [54,55], univariate prediction [38,[56][57][58][59], multivariate prediction [60][61][62], with the high accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

Kim

2021

Electronics

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Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight, grading, disease detection, and species classification. This review provides and overview of the development of smart aquaculture and intelligent technology. We summarized and collected 100 articles about machine learning in smart aquaculture from nearly 10 years about the methodology, results as well as the recent technology that should be used for development of smart aquaculture. We hope that this review will give readers interested in this field useful information.

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Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques

Cited by 35 publications

References 29 publications

Computational metadata generation methods for biological specimen image collections

Computational metadata generation methods for biological specimen image collections

Cultivation of Flowerhorn Species in Search of Superior Quality Seeds using IoT and Open CV

Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision

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