Development and implementation of a fish counter by using an embedded system

Hernández-Ontiveros, Juan Miguel; Inzunza-González, Everardo; García-Guerrero, E. E.; López-Bonilla, Oscar Roberto; Infante-Prieto, Sergio Omar; Cárdenas-Valdez, José Ricardo; Tlelo‐Cuautle, Esteban

doi:10.1016/j.compag.2017.12.023

Cited by 49 publications

(17 citation statements)

References 17 publications

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“…Hernández-Ontiveros [10] developed a fish counter utilizing an embedded system to process the images, which, in controlled lighting and species conditions, obtained 96% accuracy it's counting, with different species and sizes.…”

Section: Related Workmentioning

confidence: 99%

An Investigation of Parameter Optimization in Fingerling Counting Problems

Oliveira¹,

Pache²,

Rezende

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.

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Section: Related Workmentioning

confidence: 99%

An Investigation of Parameter Optimization in Fingerling Counting Problems

Oliveira¹,

Pache²,

Rezende

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

View full text Add to dashboard Cite

show abstract

“…with Linux as an embedded operating system are quite common and offer appropriate computing performance. The embedded microcontroller technology could be used for various lighting control purposes in various fields, in building and civil engineering [32], in vision research [33,34], in agriculture [35], in smart home [36], etc. In recent years, interesting applications in lighting control and energy-saving are implemented based on Arduino micro-controller, such as the work of Yin et al [37], based on Arduino Mega 2560.…”

Section: Related Workmentioning

confidence: 99%

DALI LED Driver Control System for Lighting Operations Based on Raspberry Pi and Kernel Modules

Adam

2019

Electronics

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Light emitting diodes (LEDs) as an efficient low-consumption lighting technology are being used increasingly in many applications. The move to LED lighting is also changing the way the lighting control systems are designed. Currently, most electronic ballasts and other digital lighting devices implement the Digital Addressable Lighting Interface (DALI) standard. This paper presents a low-cost, low-power effective DALI LED driver controller, based on open-source Raspberry Pi3 microcontroller prototyping platform. The control software is developed as a Linux kernel module under UBUNTU 18.04.2 LTS patched with PREEMPT_RT (Preemptive Real-time) for real-time processing. This dynamically loaded kernel module performs all the processing, communication and control operations of the Raspberry Pi3-based DALI controller with the DALI LED driver and LED luminaire. Software applications written in C and Python were developed for performance testing purposes. The experimental results showed that the proposed system could efficiently and effectively manage DALI LED drivers and perform lighting operations (e.g. dimming). The system can be used for a variety of purposes from personal lighting control needs and experimental research in control of electronic ballasts and other control gears, devices and sensors, to advanced requirements in professional buildings, including energy management, lighting maintenance and usage.

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“…In fish farming, knowing the exact amount of fish in a cage is of paramount importance, as it helps to provide the best conditions for the fish under cultivation, thereby guaranteeing the health and growth of the species [1][2][3][4]. As the fish food and other resources are expensive, the knowledge of the fish individuals is necessary for the optimum utilization of these [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Method to Accurately Estimate Fish Abundance in Offshore Cages

Sthapit

Kim

2020

Applied Sciences

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Due to the lack of reliable methods, manual fish counting is popular on farms. However, this approach is time and labor intensive. Using an echosounder and the echo-integration technique could be a better alternative. The echo-integration method has been widely used in fish abundance estimation in waterbodies because of its simplicity. However, most of the research is concentrated on the open ocean, whereas fish count estimation in farming cages has not been explored much. Using the echo-integration method in a cage offers its own unique sets of problems. Firstly, the echo signal reflected from the cage boundaries should also be taken into account. Secondly, the fish inside a cage behave differently with time, as their mobility pattern is highly dependent on sunlight and water current. In this paper, fish behavior inside an offshore cage over time was extensively studied, and based on that a real-time fish counter system using a commercial echosounder was developed. The experiments demonstrate that our method is simple, user-friendly, and has an estimation error of less than 10%. Since our method accurately estimated fish abundance, the method should be reliable when making fish management decisions.

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Development and implementation of a fish counter by using an embedded system

Cited by 49 publications

References 17 publications

An Investigation of Parameter Optimization in Fingerling Counting Problems

An Investigation of Parameter Optimization in Fingerling Counting Problems

DALI LED Driver Control System for Lighting Operations Based on Raspberry Pi and Kernel Modules

A Method to Accurately Estimate Fish Abundance in Offshore Cages

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