A powerful method for measuring fish size of small-scale fishery catches using ImageJ

Andrialovanirina, Nicolas; Ponton, Dominique; Behivoke, Faustinato; Mahafina, Jamal; Léopold, Marc

doi:10.1016/j.fishres.2019.105425

Cited by 26 publications

(27 citation statements)

References 18 publications

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“…The distribution of bubbles is directly proportional to the speed of the airflow rate. [23,39]. By using ImageJ software, the diameter distribution of bubbles can be seen well [24].…”

Section: Discussionmentioning

confidence: 98%

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Effect of Lens Aperture for Analysis of Bubble Image Size Microbubble Generator Aeration System

Taukhid

Trijuno

Karim

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Investigates Microbubble characterization is an essential component of any experimental investigation of new microbubble generator design. The use of microbubble generators as aeration systems often experiences blockages. That is caused by the broad cross-section of nozzles and air holes with small diameters. The microbubble generator was redesigned by increasing the cross-sectional area and diameter of the air hole. The method of investigating the bubble process uses image capture with three aperture speeds of the lens of 1/125f, 1/160f, and 1/200f with a combined airflow rate of 0.5 Lpm, 1.5 Lpm, and 2.5 Lpm. The result was characterized with multiple lens apertures with 1/160f bubble results ranging from 5.4 - 94.3 μm at all airflow rates.

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“…The distribution of bubbles is directly proportional to the speed of the airflow rate. [23,39]. By using ImageJ software, the diameter distribution of bubbles can be seen well [24].…”

Section: Discussionmentioning

confidence: 98%

“…Bubble size measurement is an important parameter to know MBG [22]. The size of air bubbles has been measured in many studies [23]. A microbubble size of 50 -150 µm was used in the experiment with the camera phantom [24].…”

Section: Introductionmentioning

confidence: 99%

Effect of Lens Aperture for Analysis of Bubble Image Size Microbubble Generator Aeration System

Taukhid

Trijuno

Karim

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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show abstract

“…Figure 3 shows the shape of PMs. The originally obtained images should be processed by de-hoisting, cropping, and threshold segmentation with ImageJ to count the number of PMs ( Amandine et al, 2019 ; Stolze et al, 2019 ; Andrialovanirina et al, 2020 ; Peterson et al, 2020 ). After converting to grayscale with ImageJ, as shown in Figure 4 , import grayscale image into Fractalyse software to calculate the fractal dimension.…”

Section: Cumulative Probability Model Validationmentioning

confidence: 99%

Research on a Fractal Dimension Calculation Method for a Nano-Polymer Microspheres Dispersed System

et al. 2021

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Polymer microspheres (PMs) are a kind of self-similar volume expansion particle, and their fractal dimension varies with hydration swelling. However, there is no unique fractal dimension calculation method for their characteristics. A new model is established in this paper, which is particular to calculate the fractal dimension of PMs. We carried out swelling hydration experiments and scanning electron microscope (SEM) experiments to verify the new model. The new model and the box-counting model were used to calculate the fractal dimensions of PMs based on the hydration experiment results. Then, a comparison of the calculation results of the two methods was used to verify the validity of the model. Finally, according to the new model calculation results, the fractal dimension characteristics of PMs were analyzed. The research results indicate that the new model successfully correlates the cumulative probability of the PMs dispersed system with the fractal dimension and makes fractal dimension calculation of PMs more accurate and convenient. Based on the experiment results, the new model was used to calculate the fractal dimension of PMs and the box-counting model, and its findings were all 2.638 at initial state hydration and 2.739 and 2.741 at hydration time as of day 1. This result verifies the correctness of the new model. According to the hydration swelling experiments and the new model calculation results, the fractal dimension is linear correlated to the average particle size of PMs and the standard deviation average particle size. This means the fractal dimension of PMs represents the space occupancy ability and space occupancy effectiveness.

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“…Ocean researchers are interested in using underwater videos and images for fish population analysis, fish species classification and size measurement etc. [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

FishResNet: Automatic Fish Classification Approach in Underwater Scenario

Mathur

Goel

2021

SN COMPUT. SCI.

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Fish species classification in underwater images is an emerging research area for scientists and researchers in the field of image processing. Fish species classification in underwater images is an important task for fish survey i.e. to audit ecological balance, monitoring fish population and preserving endangered species. But the phenomenon of light scattering and absorption in ocean water leads to hazy, dull and low contrast images making fish classification a tedious and tough task. Convolutional Neural Networks (CNNs) can be the solution for fish species classification problem but the scarcity of ample fish images leads to the serious issue of training a neural network from scratch. To overcome the issue of limited dataset the present paper proposes a transfer learning based fish species classification method for underwater images. ResNet-50 network has been used for transfer learning as it reduces the vanishing gradient problem to minimum by using residual blocks and thus improving the accuracies. Training only last few layers of ResNet-50 network with transfer learning increases the classification accuracy despite of scarce dataset. The proposed method has been tested on two datasets comprising of 27, 370 (i.e. large dataset) and 600 images (i.e. small dataset) without any data augmentation. Experimental results depict that the proposed network achieves a validation accuracy of 98.44% for large dataset and 84.92% for smaller dataset. With the performance analysis, it is observed that this transfer learning based approach led to better results by providing high precision, recall and F1score values of 0.94, 0.85 and 0.89, respectively.

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A powerful method for measuring fish size of small-scale fishery catches using ImageJ

Cited by 26 publications

References 18 publications

Effect of Lens Aperture for Analysis of Bubble Image Size Microbubble Generator Aeration System

Effect of Lens Aperture for Analysis of Bubble Image Size Microbubble Generator Aeration System

Research on a Fractal Dimension Calculation Method for a Nano-Polymer Microspheres Dispersed System

FishResNet: Automatic Fish Classification Approach in Underwater Scenario

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