Development of an android app for estimating the water quality parameters in fish pond

Heidari, Shahin; Mirzaee‐Ghaleh, Esmaeil; Rabbani, Hekmat; Vesali, Farshad

doi:10.1007/s11356-021-12974-y

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…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].…”

Section: Related Workmentioning

confidence: 99%

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Measuring and Evaluating the Speed and the Physical Characteristics of Fishes Based on Video Processing

Pour¹,

Parra

Lloret

et al. 2023

Water

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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.

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Section: Related Workmentioning

confidence: 99%

“…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.…”

Section: Related Workmentioning

confidence: 99%