Water Quality Control in Carp Fish Ponds Using Fuzzy Logic
Main Article Content
Keywords
Temperature, Acidity Level, Water Level, Blynk, FuzzyLogic
Abstract
Regularly monitoring pond water quality in fish farming is a crucial practice often neglected, negatively impacting goldfish yields. Addressing this issue, a sophisticated device leveraging fuzzy logic has been engineered to accurately regulate acidity, temperature, and water levels, with real-time data accessible through the Blynk smartphone application. This innovative system employs a trio of sensors—namely an acidity sensor, a DS18B20 temperature sensor, and an HCSR04 ultrasonic sensor—coupled with three output mechanisms: an inlet pump, an outlet pump, and a heater, to ensure precise control. Rigorous testing under various conditions at different times of the day, lasting approximately one hour each, demonstrated the device's capability to adjust water's acidity by about 0.1 units per minute, reflecting the influences of fish activity and water temperature, with a deficient accuracy error of 0.19%. Additionally, the system's effectiveness in maintaining a consistent water level was confirmed, exhibiting a refill rate of 1.2 cm per minute as detected by the sensor. This integrated system is instrumental in safeguarding goldfish health and optimizing their productivity by ensuring water quality remains within the desired acidity, temperature, and volume parameters.
References
[2] Petra Lindholm-Lehto, “REVIEW: Water quality monitoring in recirculating aquaculture systems”, Aqua. Fish & Fisheries.vol. 3, pp. 113–131, 2023 https://DOI: 10.1002/aff2.102
[3] Midory Esmeralda Vigueras-Velazquez, Jos´e Juan Carbajal-Hernandez, Luis Pastor Sanchez-Fern ´ andez, Jos´e Luis Vazquez-Burgos, Juan Antonio Tello-Ballinas, “Weighted fuzzy inference system for water quality management of Chirostoma estor estor culture”, Aquaculture Reports vol. 18, 100487, 2020
[4] Guodong You, Bin Xu, Honglin Su, Shang Zhang, Jinming Pan, Xiaoxin Hou, Jisheng Li and Runsuo Ding, “Article: Evaluation of Aquaculture Water Quality Based on Improved Fuzzy Comprehensive Evaluation Method”, Water, vol.13, 1019, 2021. https://doi.org/10.3390/w13081019
[5] Rafael Apolinar Bórquez López, Luis Rafael Martinez Cordova, Juan Carlos Gil Nuñez, Jose Reyes Gonzalez Galaviz, Jose Cuauhtemoc Ibarra Gamez and Ramon Casillas Hernandez, “Article: Implementation and Evaluation of Open-Source Hardware to Monitor Water Quality in Precision Aquaculture”, Sensors, vol. 20, 6112, 2020. doi:10.3390/s20216112
[6] Ana Silvia Pedrazzani, Camila Prestes dos Santos Tavares, Murilo Quintiliano, Nathieli Cozer, Antonio Ostrensky, “New indices for the diagnosis of fish welfare and their application to the grass carp (Ctenopharyngodon idella) reared in earthen ponds”, Aquaculture Research. Vol. 53, pp. 5825–5845, 2022. https://DOI: 10.1111/are.16105
[7] Daoliang Li, Zhenhu Wang, Suyuan Wu, Zheng Miao, Ling Du, Yanqing Duan, “Automatic recognition methods of fish feeding behavior in aquaculture: A review”, Aquaculture vol. 528, 735508, 2020.
[8] Chao Zhoua, Kai Lina, Daming Xu, Lan Chen, Qiang Guo, Chuanheng Sun, Xinting Yang, “Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture”, Computers and Electronics in Agriculture, vol. 146, pp. 114-124, 2018.
[9] Ehsan Elahi, Zainab Khalid, “Estimating smart energy inputs packages using hybrid optimisation technique to mitigate environmental emissions of commercial fish farms”, Applied Energy vol. 326, 119602, 2022.
[10] Mahmoud A. Elnakeeb, Lydia M. Vasilyeva, Natalia V. Sudakova, Adelya Z. Anokhina, Ahmed G. A. Gewida, Mahmoud Alagawany, Mohammed A. E. Naiel, “Evaluate the Metabolism Responses of Cultured Paddlefish, Polyodon Spathula (Walbaum, 1792), Towards Some Ecological Stressors in the Volga-Caspian Basin using Fuzzy Modeling Control”, Advances in Animal and Veterinary Sciences, vol. 9, iss. 6, pp.773-786, 2021.
[11] J. Gladju, Biju Sam Kamalam, A. Kanagaraj, “Applications of data mining and machine learning framework in aquaculture and fisheries: A review”, Smart Agricultural Technology vol. 2, 100061, 2022.
[12] Hui Li, Yingyi Chen, Wensheng Li, Qingbin Wang, Yanqing Duan, Tao Chen, “An adaptive method for fish growth prediction with empirical knowledge extraction”, biosystems engineering vol.212, pp. 336-346, 2021.
[13] Kasda, Deny Poniman Kosasih, Hari Din Nugraha, Maulana Rachman, “Low-Cost Remote Control Barge Boat to Feeder Fish”, Journal of Mechanical Engineering Research and Developments, Vol. 44, No. 2, pp. 112-121, 2021.
[14] Siqi Zhao, Ming Zhu, Weimin Ding, Sanqin Zhao, Jiabing Gu, “Feed requirement determination of grass carp (Ctenopharyngodon idella) using a hybrid method of bioenergetics factorial model and fuzzy logic control technology under outdoor pond culturing systems”, Aquaculture vol. 521, 734970, 2020.
[15] Jean-Beno^ıt Darodes de Tailly, Jonas Keitel, Matthew A.G. Owen, Jose M. Alcaraz-Calero, Mhairi E. Alexander and Katherine A. Sloman, “Monitoring methods of feeding behaviour to answer key questions in penaeid shrimp feeding”, Reviews in Aquaculture, vol. 13, pp.1828–1843, 2021.
[16] Siqi Zhao, Weimin Ding, Sanqin Zhao, Jiabing Gu, “Adaptive neural fuzzy inference system for feeding decision-making of grass carp (Ctenopharyngodon idellus) in outdoor intensive culturing ponds”, Aquaculture vol. 498, pp. 28-36, 2019.
[17] S.A. Alim, M. Sumaila and I.Y. Ritkangnga, “Design of a Fuzzy Logic Controller for OptimalAfrican Catfish Water Production”, Mekatronika, Vol. 3, Iss.2, pp. 42 – 48, 2021. DOI: https://doi.org/10.15282/mekatronika.v3i2.7352
[18] S. E. Abdallah, W. M. Elmessery, ” An Automatic Feeder with Two Different Control Systems for Intensive Mirror Carp Production” Journal of Agricultural Engineering and Biotechnology, Vol. 2 Iss. 3, pp. 36-48, 2014.
[19] Sergio Antonio Alvarez-Leon, Luis Fernando RosalesVilela, Yvan Jesús García López, “Increase of Productivity in Aquaculture Farming Systems Through the Implementation of Automated Technologies and Engineering Techniques: A Systematic Literature Review”, Proceedings of the First Australian International Conference on Industrial Engineering and Operations Management, Sydney, Australia, December 20-21, 2022.
[20] J. Gladju, Biju Sam Kamalam, A. Kanagaraj, “Applications of data mining and machine learning framework in aquaculture and fisheries: A review”, Smart Agricultural Technology vol. 2, 100061, 2022.
[21] Darwison dan Rian Wahyudi. “Kontrol Kecepatan Robot Hexapod Pemadam Api Menggunakan Metoda Logika Fuzzy”, Jurnal Nasional Teknik Elektro (JNTE) Vol.4 No.2, pp. 227-234, September 2015.
[22] Dian Pancawati* dan Andik Yulianto. “Implementasi Fuzzy Logic Controller Untuk Mengatur Ph Nutrisi Pada Sistem Hidroponik Nutrient Film Technique (NFT)”, Jurnal Nasional Teknik Elektro (JNTE) Vol.5 No.2, pp. 278-289, Juli 2016.
[23] Min-Chie Chiu, Wei-Mon Yan, Showkat Ahmad Bhat, NenFu Huang, “Development of smart aquaculture farm management system using IoT and AI-based surrogate models”, Journal of Agriculture and Food Research vol. 9, 100357, 2022.
[24] Siqi Zhao, Weimin Ding, Sanqin Zhao, Jiabing Gu. “Adaptive neural fuzzy inference system for feeding decisionmaking of grass carp (Ctenopharyngodon idellus) in outdoor intensive culturing ponds”, Jurnal Aguacultureelsevier, Volume 498, 1 January 2019, Pages 28-36.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
The authors declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
Darwison, Universitas Andalas
electrical engineering and ass. professor