• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.3
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• pp.328-334
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• 1999

A fish pump makes very important roles in an automation system of an aquaculture farm, thus it has been used widely in order to transfer fishes from one place to the other place automatically. In spite of its significant roles, the efforts for developing performance and promoting efficiency of the fish pump are not sufficient yet. In this paper, a method which makes the fish pump automation system is suggested. Automation of the fish pump can be accomplished by using variable voltage and variable frequency inverter system including induction motors. Especially, very simple logic to generate Pulse width Modulation(PWM) wave to control induction motor efficiently and three steps speed control method to regulate liquid quantity of the fish pump simply are suggested. Owing to the simplifies speed control and PWM wave generation technique, a cheaper microprocessor, 80C196KC, than a digital signal Processor(DSP) can be used to operate control algorithm in induction motor systems for real time control Also, a new idea of remote control for the simplifies novel inverter system by Programmable logic Controller(PLC) without special output unit, digital to analog converter(D/A), is suggested in this paper. Consequently the function of reliability, availability and serviceability of the fish pump system are developed. It will be expected to contribute expanding of application of the fish pump in aquaculture farms because the system can reduce energy consumption and some difficulties according to manual operation prominently.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.347-353
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• 2023

Measuring the growth of fish in fish farms still uses a laborious method. This method requires a lot of labor and causes stress to the fish, which has a negative impact on mortality. To solve this problem, we propose the Fish Growth Regression System (FGRS), a system to automate the growth of fish. FGRS consists of two modules. The first is a module that detects fish based on Yolo v8, and the second consists of a module that predicts the growth of fish using fish image data and a CNN-based neural network model. As a result of the simulation, the average prediction error before learning was 134.2 days, but after learning, the average error decreased to 39.8 days. It is expected that the system proposed in this paper can be used to predict the growing date and use the growth prediction of fish to contribute to automation in fish farms, resulting in a significant reduction in labor and cost savings.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.49-55
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• 2024

This paper proposes PredFeed Net, a neural network model that mimics the food distribution of fish farming experts. Unlike existing food distribution automation systems, PredFeed Net predicts food distribution by learning the food distribution patterns of experts. This has the advantage of being able to learn using only existing environmental data and food distribution records from food distribution experts, without the need to experiment by changing food distribution variables according to the environment in an actual aquarium. After completing training, PredFeed Net predicts the next food ration based on the current environment or fish condition. Prediction of feed ration is a necessary element for automating feed ration, and feed ration automation contributes to the development of modern fish farming such as smart aquaculture and aquaponics systems.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.