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Behzadi Pour, F.; Parra, L.; Lloret, J.; Abdanan Mehdizadeh, S. Video Processing for Physical Characteristics of Fishes. Encyclopedia. Available online: https://encyclopedia.pub/entry/45784 (accessed on 23 July 2024).
Behzadi Pour F, Parra L, Lloret J, Abdanan Mehdizadeh S. Video Processing for Physical Characteristics of Fishes. Encyclopedia. Available at: https://encyclopedia.pub/entry/45784. Accessed July 23, 2024.
Behzadi Pour, Faezeh, Lorena Parra, Jaime Lloret, Saman Abdanan Mehdizadeh. "Video Processing for Physical Characteristics of Fishes" Encyclopedia, https://encyclopedia.pub/entry/45784 (accessed July 23, 2024).
Behzadi Pour, F., Parra, L., Lloret, J., & Abdanan Mehdizadeh, S. (2023, June 19). Video Processing for Physical Characteristics of Fishes. In Encyclopedia. https://encyclopedia.pub/entry/45784
Behzadi Pour, Faezeh, et al. "Video Processing for Physical Characteristics of Fishes." Encyclopedia. Web. 19 June, 2023.
Video Processing for Physical Characteristics of Fishes
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This entry is adapted from the peer-reviewed paper 10.3390/w15112138

Acquiring the morphological parameters of fish with the traditional method (depending on human and non-automatic factors) not only causes serious problems, such as disease transmission, mortality due to stress, and carelessness and error, but it is also time-consuming and has low efficiency. 

fish speed ocean observatory fish velocity

1. Introduction

Fish is one of the most important sources of animal proteins, micronutrients, and minerals needed by humans all over the world. Compared to red meat, fish meat has more nutritional value and is healthier in terms of quality [1]. The attention of different countries to the aquaculture industry to supply food to the human population has grown significantly with the increase in population and the need to supply food [1]. As aquaculture production has steadily increased its share of total marine-based global food production, it is expected to grow further in the future [2]. Fish breeders resist physically removing fish from ponds to collect data; therefore, the importance of collecting high-precision data on the morphological conditions of live fish without the need to physically relocate them has become a critical need for fisheries and aquatic management [3]. The current problem is that measuring the physical characteristics of fish based on traditional methods supposes stressful conditions, which might cause a decline in fish performance as well as being a highly time-demanding activity.
By using machine vision technology, in addition to solving the existing problems, fish biometric parameters have been checked with high precision. This will improve fish nutrition, disease prevention, the continuous monitoring of fish, and time management in fish farming, including the timely supply of fish to the market so more profit can be obtained [4]. Studying the biological parameters of salmon with the traditional method (relying on human and non-automatic factors) has low efficiency due to its serious problems, such as the transmission of diseases, the death of aquatic animals due to the stress caused to them, and inaccuracies, in addition to being time-consuming.
Considering the high cost of fish farms (especially the cost of feed) and the growing need of this industry for new technologies to increase the efficiency of breeding, there is a need for a system that can continuously determine the morphological and movement characteristics of fish during the breeding process. If fish movement and growth are not accurately monitored the proper measures cannot be taken. Most tracking systems are based on deep learning [5], particle filters [6], adaptive filters [7][8], and others [9][10][11].

2. Physical Characteristics of Fishes

It is necessary to implement more sophisticated systems to be able to track fish behavior for a long time, with minimal loss of identity across frames. Several authors have proposed systems to solve these challenges: complex models of object recognition, color label recognition systems, object recognition labels [12], a set of variables for unique recognition, time complexity algorithms in contour recognition, and separating the head and body of the fish [13][14][15]. The unique data of fish swimming speed can be a key element in understanding the behavioral dynamics of fish and their interaction with the environment, stress, and hunger level, as well as measuring aquatic welfare issues related to new waves and currents in breeding sites [16][17][18]. Although many methods can be used to measure the characteristics of aquatic animals, the speed and the health of aquatic animals, as well as increasing economic profit, are the challenges facing us today.
In the past, morphometric studies were based on a series of traditional measurements around the body and head axis. A new morphological measurement system was developed by Strauss and Bocastin called the image processing system, which determined diversity using morphological traits, due to the limitations and weaknesses of the old measurement systems [19]. The image processing system does not have the weaknesses and disadvantages of traditional morphometry methods and covers the whole body regularly, creating a good model of the actual shape of the samples, unlike the traditional method [20]. In the study, live-tagged fish in an experimental farm was used to measure the swimming speed of Atlantic salmon based on conventional acoustic telemetry and Doppler analysis. The results showed that the actual and average swimming speed ranges were 880 mm/s with a deviation of 590 mm/s, and 1080 mm/s with a deviation of 590 mm/s, respectively, and the body length per second was 1.4 and 1.6, respectively [21]. Another study, using low-cost sensors to monitor water quality and fish behavior in aquaculture tanks during the feeding process, reported that the proposed system can measure water quality parameters, tank condition, feed fall, and fish swimming depth and speed. They reported that the work is quite economical, with the cost of the sensors and the proposed node less than EUR 90 [22]. Analysis of the biometric parameters and the growth curve of salmon was performed by analyzing digital images under laboratory conditions. According to the results presented, the accuracy of the system in estimating the biometric parameters of salmon is higher than 90%, and the capability of the system was obtained at 98% for the estimation of food required by fish in the growth process [23]. In the research, several fish were photographed in a chamber to calculate the circumference, area, and equivalent diameter of the fish to investigate the morphometric changes in Salmonidae fish (Oncorhynchus mykiss (Walbaum, 1792)) using the image processing method (IPM). Based on the reported results, final weight, average final length, and final height increased significantly with increasing feeding. Additionally, the final area, final circumference, and final diameter increased with increasing feeding relative to body weight. Therefore, the results showed that image processing has sufficient accuracy and high speed to determine the fish length and other growth parameters, and it is cost-effective from an economic point of view [24].
Ref. [25] determined some quality parameters of fish pond water by processing the images taken using a smartphone camera and artificial neural network, and the coefficient of explanation of the best models presented for PH, TDS (total dissolved substances), EC (electrical conductivity), and Turb (turbidity) was reported as 0.913, 0.993, 0.994, and 0.958, respectively, and the RMSE values were 0.054, 1.835, 3.766, and 0.266, respectively. Ref. [15] used two convolutional networks to track zebrafish groups of up to 100 fish with over 99.95% accuracy via video in a low-noise environment. Ref. [26] developed a Kalman-filter-based bird tracking algorithm for chick movement detection using low-resolution video. According to the results, they reported that YOLO provides 99.9% accuracy in detecting chickens in low-quality videos. Ref. [27] estimates and diagnoses orange fruit in an orchard using YOLO models. They reported the Precision, Recall, F1-score, and MAP of the YOLO-v4 as the best model for orange detection using test images, with values of 91.23%, 92.8%, 92%, and 90.8%, respectively. Ref. [28] presented an algorithm for image processing to estimate the average weight of ornamental fish, and among the mathematical models used, the power model of the weight–the surface area of fish with an R2 higher than 0.956 as a mathematical relationship for weight estimation. Ref. [29] investigated the application of computer vision and support vector regression methods to predict the weight of live broiler chickens. According to the results, they reported that the RMSE, MAPE, and R2 values of the SVR algorithm were 67.88, 8.63%, and 0.98, respectively. So, machine vision along with SVR could promisingly estimate the weight of live broiler chickens. Ref. [30] used an automatic image processing system to measure the morphological parameters (appearance), including total length, weight, thickness, and width, of flatfish and stated that the maximum error was 2%. Ref. [31] measured the length of tuna at depths of 2, 4, 6, 8, 10, and 12 using a stereo-video system and reported that the accuracy of the image processing system is equal to 5% for depths less than 5.5 m. Ref. [32] used image processing technology to estimate the density of salmon in a fish breeding pond and reported that there is no significant difference between the actual density and the density obtained from the mathematical model at the level of 5%. Ref. [4] presented an agent-based simulator of underwater sensors for measuring the number of fish. The novel ABS-Fish Count simulator defines and assesses different strategies for measuring fish from a set of underwater sonar sensors. Ref. [33], with the help of machine vision technology (imaging and image processing), digitized two types of fish based on species, size, and weight. They measured seven fish variables: length, height, area, circumference, equivalent diameter, largest diameter, and smallest diameter. The results showed that there is no significant difference between the actual weight and the measured weight at a 95% confidence level, that the proposed algorithm distinguished two types of fish with 100% accuracy, and that there was no significant difference in the length obtained and the manually measured fish length using image processing. Sometimes destructive methods are used to measure the morphological characteristics of fish, causing damage and stress to the fish, which not only causes economic loss but is also very time-consuming and expensive. Ref. [34] used an electro shocker for sampling in research to measure the meristic, morphometric, age structure, and growth of barb fish (Barbus grypus (Heckel, 1843)).

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Subjects: Agriculture, Dairy & Animal Science
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Faezeh Behzadi Pour
,
Lorena Parra
,
Jaime Lloret
,
Saman Abdanan Mehdizadeh
View Times: 249
Revisions: 2 times (View History)
Update Date: 21 Jun 2023
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