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

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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.

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A Machine Learning Approach to Monitor Water Quality in Aquaculture

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