Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing
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. In this...
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MDPI AG
2023-06-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/11/2138 |
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| author | Faezeh Behzadi Pour Lorena Parra Jaime Lloret Saman Abdanan Mehdizadeh |
| author_facet | Faezeh Behzadi Pour Lorena Parra Jaime Lloret Saman Abdanan Mehdizadeh |
| author_sort | Faezeh Behzadi Pour |
| collection | DOAJ |
| description | 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. In this paper, the speed of fish and their physical characteristics (maximum and minimum diameter, equivalent diameter, center of surface, and velocity of fish) were investigated by using a programmed online video-recording system. At first, using the spatial coordinates obtained from YOLOv2, the speed of the fish was calculated, and the morphological characteristics of the fish were also recorded using this program during two stages of feeding and normal conditions (when the fish are not in feeding condition). Statistical analysis was performed between the measured parameters due to the high correlation between the parameters, and the classification system with high accuracy was able to provide an accurate prediction of the fish in both normal and feeding conditions. In the next step, an artificial neural network (ANN) prediction model (with three neurons; four input, one hidden layer, and one output) was presented to plan the system online. The model has the lowest error (1.4 and 0.14, respectively) and the highest coefficient of explanation (0.95 and 0.94, respectively) in two modes, normal and feeding, which are presented by the ANN system for planning the online system. The high accuracy and low error of the system, in addition to having a high efficiency for continuous and online monitoring of live fish, can have a high economic benefit for fish breeders due to the simplicity of its equipment, and it can also check and diagnose the condition of fish in time and prevent economic damage. |
| first_indexed | 2024-03-11T02:53:38Z |
| format | Article |
| id | doaj.art-3fd555bba631432f8e469acae2eea358 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T02:53:38Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-3fd555bba631432f8e469acae2eea3582023-11-18T08:48:07ZengMDPI AGWater2073-44412023-06-011511213810.3390/w15112138Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video ProcessingFaezeh Behzadi Pour0Lorena Parra1Jaime Lloret2Saman Abdanan Mehdizadeh3Department of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, IranInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, 46730 Grau de Gandia, SpainInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, 46730 Grau de Gandia, SpainDepartment of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani 6341773637, IranAcquiring 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. In this paper, the speed of fish and their physical characteristics (maximum and minimum diameter, equivalent diameter, center of surface, and velocity of fish) were investigated by using a programmed online video-recording system. At first, using the spatial coordinates obtained from YOLOv2, the speed of the fish was calculated, and the morphological characteristics of the fish were also recorded using this program during two stages of feeding and normal conditions (when the fish are not in feeding condition). Statistical analysis was performed between the measured parameters due to the high correlation between the parameters, and the classification system with high accuracy was able to provide an accurate prediction of the fish in both normal and feeding conditions. In the next step, an artificial neural network (ANN) prediction model (with three neurons; four input, one hidden layer, and one output) was presented to plan the system online. The model has the lowest error (1.4 and 0.14, respectively) and the highest coefficient of explanation (0.95 and 0.94, respectively) in two modes, normal and feeding, which are presented by the ANN system for planning the online system. The high accuracy and low error of the system, in addition to having a high efficiency for continuous and online monitoring of live fish, can have a high economic benefit for fish breeders due to the simplicity of its equipment, and it can also check and diagnose the condition of fish in time and prevent economic damage.https://www.mdpi.com/2073-4441/15/11/2138fish speedocean observatoryfish velocityYOLOv2MATLABobject recognition |
| spellingShingle | Faezeh Behzadi Pour Lorena Parra Jaime Lloret Saman Abdanan Mehdizadeh Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing Water fish speed ocean observatory fish velocity YOLOv2 MATLAB object recognition |
| title | Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing |
| title_full | Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing |
| title_fullStr | Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing |
| title_full_unstemmed | Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing |
| title_short | Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing |
| title_sort | measuring and evaluating the speed and the physical characteristics of fishes based on video processing |
| topic | fish speed ocean observatory fish velocity YOLOv2 MATLAB object recognition |
| url | https://www.mdpi.com/2073-4441/15/11/2138 |
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