A Machine Learning Approach to Monitor Water Quality in Aquaculture

doi:10.23940/ijpe.20.12.p1.18451852

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (12): 1845-1852.doi: 10.23940/ijpe.20.12.p1.18451852

• Orginal Article • Next Articles

A Machine Learning Approach to Monitor Water Quality in Aquaculture

Kaire Anupama^{a, *}, Y. Chalapathi Rao^b, Vijaya Kumar Gurrala^b

^aEmbedded Systems, VNR VJIET, Hyderabad, Telengana, 500090, India;
^bDepartment of ECE, VNR VJIET, Hyderabad, Telengana, 500090, India

Submitted on ; Revised on ; Accepted on
Contact: * Corresponding author. E-mail address: anupamakaire1995@gmail.com
About author:
Kaire Anupama is an M.Tech scholar in Embedded systems at VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. She received the B.Tech degree in ECE from Jyothismathi Institute of Technology and Science, Nusthulapur, karimnagar, Telangana. Her research includes Embedded systems and Machine Learning.
Dr. Y Chalapathi Rao received the B.Tech. degree in ECE from Nagarjuna University, Guntur, India, M.Tech. degree in VLSI System Design from JNTU, Kakinada, Andhra Pradesh. And Ph.D. degree in Wireless Communications from Acharya Nagarjuna University (ANU), Guntur, AP, India. Presently he is working as an Associate Professor in ECE department at VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. His research interests are focused on Wireless Sensor Networks, Wireless Communications, Signal Processing, VLSI, and embedded systems design.
Vijaya Kumar Gurrala, Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India.He is working as an Assistant Professor in the Department of Electronics and Communication Engineering in VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. He received his M.Tech in DECS from JNTUK, Kakinada, India in 2009 and pursuing Ph.D in Department of Electronics and Communication Engineering at JNTUK, Kakinada, India. His research interests are Signal Processing, Bio Medical Signal Processing especially EEG signal processing, Communications. He guided various projects at UG and PG level in the domain of signal processing and communications. He has completed UGC mini project of an amount Rs.4,40,000/-.

Abstract

Abstract: Aquaculture plays a crucial part in the coastal region, India. Aquaculture depends on the water quality categorized into physical, chemical, and biological parameters. Over a huge number of a farmer's life relies on aquaculture. This paper shows the monitoring of water quality based on Machine Learning and IoT. Based on the change in parameters like pH, water level, temperature, foul smell, and turbidity, the water quality is monitored and the growth of fisheries in water is determined. This paper presents the remote monitoring of parameters on screen as well as on an Android App after storing them into the cloud and also helps to find the accuracy of the water quality based on the machine learning algorithm.

Key words: Aquaculture, water quality parameters, cloud, Android App, machine learning

Kaire Anupama, Y. Chalapathi Rao, Vijaya Kumar Gurrala. A Machine Learning Approach to Monitor Water Quality in Aquaculture [J]. Int J Performability Eng, 2020, 16(12): 1845-1852.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Y. Ma and W. Ding, “Design of Intelligent Monitoring System for Aquaculture Water Dissolved Oxygen,” inProceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 414-418, Chongqing, 2018
2. M. Cordova-Rozas, J. Aucapuri-Lecarnaque,P. Shiguihara-Juárez, “A Cloud Monitoring System for Aquaculture using IoT,” inProceedings of the 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON), pp. 1-4, Lima, Peru, 2019
3. X. D. Su, L. Sutarlie,X. J. Loh, “Sensors, Biosensors, and Analytical Technologies for Aquaculture Water Quality,”Proceedings of Science Partner Journal, pp. 15, January 2020
4. “Aquaculture Development and Coordination Programme,” (http://www.fao.org/3/l8156e/l8156e07.htm, accessed FAO 1978
5. “Freshwater Aquaculture, is a Part of National Cooperative Extension Resource,”
6 (https://freshwater-aquaculture.extension.org/water-quality-in-aquaculture/, accessecd August 2019)
6. M. Lafont, S. Dupont, P. Cousin, A. Vallauri,C. Dupont, “Back to the Future: IoT to Improve Aquaculture: Real-Time Monitoring and Algorithmic Prediction of Water Parameters for Aquaculture Needs,” inProceedings of the 2019 Global IoT Summit (GIoTS), pp. 1-6, Aarhus, Denmark, 2019
7. Z. Shareef and S. R. N. Reddy, “Design and Wireless Sensor Network Analysis of Water Quality Monitoring System for Aquaculture,” inProceedings of the 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 405-408, Erode, India, March 2019
8. C. Deng, Y. Gao, J. Gu, X. Miao,S. S. Li, “Research on the Growth Model of Aquaculture Organisms based on Neural Network Expert System,” inProceedings of the 2010 Sixth International Conference on Natural Computation, pp. 1812-1815, Yantai, 2010
9. “Skretting is an Innovative Program about Nutrition Needs,” (https://www.skretting.com/en/faq/why-is-aquaculture-important/, acessed 2020)
10. A. Fredheim and T. Reve, “Future Prospects of Marine Aquaculture,”OCEANS 2018 MTS/IEEE Charleston, pp. 1-8, Charleston, SC, 2018
11. “Wikipedia, for Analysis of Aqucculture,” (https://en.wikipedia.org/wiki/Fish_farming, accessed August 2020)
12. V. E. Kostin, A. A. Silaev,A. V. Savchic, “Information-Measuring System for Monitoring and Control Aquaculture of Pond Farm,” inProceedings of the 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), pp. 1-4, Vladivostok, Russia, 2019
13. P. Whig and S. N. Ahmad, “Modelling and Simulation of Economical Water Quality Monitoring Device,” Journal of Aquaculture Marine Biology, Vol. 4, No. 6, pp. 00103, December 2016
14. K. Preetham, B. C. Mallikarjun, K. Umesha, F. M. Mahesh,S. Neethan, “Aquaculture Monitoring and Control System: An IoT based Approach,” International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com
15. G. A.Defe and A. Z. C. Antonio, “Multi-Parameter Water Quality Monitoring Device for Grouper Aquaculture,” inProceedings of the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1-5, Baguio City, Philippines, 2018
16. “Spotlight is a Communication Group Used for Different Technologies and Areas,” (https://www.spotlightmetal.com/machine-learning--definition-and-application-examples-a-746226/, accessed August 2018)
17. “Analytics Vidhya Provides a Community based Knowledge Portal,” (https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/, accessed September 2017)
18. K. Raghu Sita Rama Raju and G. Harish Kumar Varma, “Knowledge based Real Time Monitoring System for Aquaculture using IoT,” in Proceedings of the2017 IEEE 7th International Advance Computing Conference, pp. 318-321, 2017

[1]	Ashu Mehta, Navdeep Kaur, and Amandeep Kaur. A Review of Software Fault Prediction Techniques in Class Imbalance Scenarios [J]. Int J Performability Eng, 2025, 21(3): 123-130.
[2]	Vikas, Charu Wahi, Bharat Bhushan Sagar, and Manisha Manjul. Trust Management in WSN using ML for Detection of DDoS Attacks [J]. Int J Performability Eng, 2025, 21(3): 157-167.
[3]	Sonia Sharma and Rajendra Kumar Bharti. Intelligent Job Allocation and Adaptive Migration in Cloud Environments using a Dynamic Dual-Threshold Strategy [J]. Int J Performability Eng, 2025, 21(3): 168-177.
[4]	Arpna Saxena and Sangeeta Mittal. CluSHAPify: Synergizing Clustering and SHAP Value Interpretations for Improved Reconnaissance Attack Detection in IIoT Networks [J]. Int J Performability Eng, 2025, 21(1): 36-47.
[5]	Seema Kalonia and Amrita Upadhyay. Comparative Analysis of Machine Learning Model and PSO Optimized CNN-RNN for Software Fault Prediction [J]. Int J Performability Eng, 2025, 21(1): 48-55.
[6]	Vikas Kumar, Charu Wahi, Bharat Bhushan Sagar, and Manisha Manjul. Ensemble Learning Based Intrusion Detection for Wireless Sensor Network Environment [J]. Int J Performability Eng, 2024, 20(9): 541-551.
[7]	Kalyani H. Deshmukh, Gajendra R. Bamnote, and Pratik K Agrawal. A Novel Approach for Drought Monitoring and Evaluation using Time Series Analysis and Deep Learning [J]. Int J Performability Eng, 2024, 20(8): 498-509.
[8]	Saurabh Saxena, and Chetna Gupta. Optimizing Bug Resolution: A Data-Driven Developer Recommendation System [J]. Int J Performability Eng, 2024, 20(8): 510-519.
[9]	Lakshya Vaswani, Sai Sri Harsha, Subham Jaiswal, and Aju D. Unravelling Complexity: Investigating the Effectiveness of SHAP Algorithm for Improving Explainability in Network Intrusion System Across Machine and Deep Learning Models [J]. Int J Performability Eng, 2024, 20(7): 421-431.
[10]	Meenakshi Chawla and Meenakshi Pareek. A Hybrid Deep Learning Perspective for Software Effort Estimation [J]. Int J Performability Eng, 2024, 20(7): 442-450.
[11]	Ajeet Kumar Sharma and Rakesh Kumar. IoT Malware Detection and Dynamic Analysis of MQTT Simulated Network [J]. Int J Performability Eng, 2024, 20(7): 451-459.
[12]	Abhishek Gupta and Jaspreet Singh. Data-Driven Security Framework for VANET using Firefly and ANN [J]. Int J Performability Eng, 2024, 20(6): 344-354.
[13]	Vikas Verma, Arun Malik, and Isha Batra. Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach [J]. Int J Performability Eng, 2024, 20(5): 312-318.
[14]	Harshita Batra and Leema Nelson. ESD: E-mail Spam Detection using Cybersecurity-Driven Header Analysis and Machine Learning based Content Analysis [J]. Int J Performability Eng, 2024, 20(4): 205-213.
[15]	Neha Kashyap, Sapna Sinha, and Vineet Kansal. A Hybrid Lightweight Method of ABE with SHA1 Algorithm for Securing the IoT Data on Cloud [J]. Int J Performability Eng, 2024, 20(3): 131-138.

A Machine Learning Approach to Monitor Water Quality in Aquaculture

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended 0