Abstract
This study proposes a water quality detection system that combines image processing and Convolutional Neural Network (CNN) models to accurately identify and classify water quality based on visual features. The quality of water has significant impacts on a variety of areas, such as the survival of aquatic creatures, agricultural irrigation, and human health. It is also a crucial factor in global economic development. Therefore, there is a great importance to develop a monitoring system that is simple, quick, low-cost, and reliable. There are certain drawbacks associated with using automatic water quality sensors, including their high cost and the challenges involved in maintaining them. Water colour is a crucial indicator of water quality in lakes or ponds, as it reflects the unique characteristics of the water. The proposed system captures images of water bodies and extracts features such as color, texture, and turbidity, using image processing techniques. These features are used as input to the trained CNN models for water quality prediction and classification. The proposed system is evaluated using a real-world water quality dataset, and the results demonstrate that the proposed system achieves great precision in water quality detection. The system's ability to detect visual anomalies in water quality can provide early warning signals for potential health and environmental hazards.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Seetha M (2018) Accuracy assessment of rough set based SVM Technique for spatial image classification. Int J Knowl Learn 12(3):269–274, Scopus 1741-1009
Palsodkar P, Palsodkar P, Gokhale A, Dorge P, Gurjar A (2022) Fuel Larceny and leakage indication system using IoT. In: Mukherjee S, Muppalaneni NB, Bhattacharya S, Pradhan AK (eds) Intelligent systems for social good. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-19-0770-8_7
Agarwal S (2022) IoT applications for health care. In: Mukherjee S, Muppalaneni NB, Bhattacharya S, Pradhan AK (eds) Intelligent systems for social good. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-19-0770-8_8
Aryafar A, Gholami R, Rooki R, Ardejani F (2012) Heavy metal pollution assessment using support vector machine in the Shur River, Sarcheshmeh copper mine, Iran. Environ Earth Sci 67. https://doi.org/10.1007/s12665-012-1565-7
Bhuvaneswari Amma NG, Akshay Madhavaraj R (2023) Malware analysis using machine learning tools and techniques in IT Industry. In: Sarveshwaran V, Chen JIZ, Pelusi D (eds) Artificial intelligence and cyber security in Industry 4.0. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-99-2115-7_8
Mukherjee S, Nath SS, Singh GK, Banerjee S (2022) FACEIFY: intelligent system for text to image translation. In: Mukherjee S, Muppalaneni NB, Bhattacharya S, Pradhan AK (eds) Intelligent systems for social good. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-19-0770-8_5
Gunjan VK, Singh N, Shaik F, Roy S (2022) Detection of lung cancer in CT scans using grey wolf optimization algorithm and recurrent neural network. Heal Technol 12(6):1197–1210
Gaddam DKR, Ansari MD, Vuppala S, Gunjan VK, Sati MM (2022) Human facial emotion detection using deep learning. In: ICDSMLA 2020: Proceedings of the 2nd international conference on data science, machine learning and applications. Springer Singapore, pp 1417–1427
Ahmed M, Ansari MD, Singh N, Gunjan VK, BV SK, Khan M (2022). Rating-based recommender system based on textual reviews using IOT smart devices. Mob Inf Syst 2022
Das P, Saif S (2023) Intrusion detection in IoT-based healthcare using ML and DL approaches: a case study. In: Sarveshwaran V, Chen JIZ, Pelusi D (eds) Artificial intelligence and cyber security in Industry 4.0. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-99-2115-7_12
Fantin Irudaya Raj E, Appadurai M (2022). Internet of Things-based smart transportation system for smart cities. In: Mukherjee S, Muppalaneni NB, Bhattacharya S, Pradhan AK (eds) Intelligent systems for social good. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-19-0770-8_4
Margatama L (2021). Reducing energy consumption in green cloud computing. Helix—Sci Explorer | Peer Reviewed Bimonthly Int J 11(2):6–15
Ojha N, Kumar A, Tyagi N, Ranjan P, Vaish A (2023) Use of machine learning in forensics and computer security. In: Sarveshwaran V, Chen JIZ, Pelusi D (eds) Artificial intelligence and cyber security in Industry 4.0. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-99-2115-7_9
Manasa K, Leo Joseph LMI (2023) IoT Security Vulnerabilities and Defensive Measures in Industry 4.0. In: Sarveshwaran V, Chen JIZ, Pelusi D (eds) Artificial intelligence and cyber security in Industry 4.0. Advanced technologies and societal change. Springer, Singapore. https://doi.org/10.1007/978-981-99-2115-7_4
Singh N, Gunjan VK, Nasralla MM (2022) A parametrized comparative analysis of performance between proposed adaptive and personalized tutoring system “seis tutor” with existing online tutoring system. IEEE Access 10:39376–39386
Singh HK, Geete SS, Tiwari MS, Rajurkar VJ (2020) Review of limit equilibrium methods for stability analysis of dump slope. Helix—Sci Explorer | Peer Reviewed Bimonthly Int J 10(01):89–97
Gunjan VK, Vijayalata Y, Valli S, Kumar S, Mohamed MO, Saravanan V (2022) Machine learning and cloud-based knowledge graphs to recognize suicidal mental tendencies. Comput Intell Neurosci 2022
Uudeberg K, Aavaste A, Kõks K-L, Ansper A, Uusõue M, Kangro K, Ansko I, Ligi M, Toming K, Reinart A (2020) Optical water type guided approach to estimate optical water quality parameters. Remote Sens 12:931. https://doi.org/10.3390/rs12060931
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patil, J.S., Mailaram, A., Basa, P.N.K., Sai Sravya, A., Yadam, B. (2024). Integrating Image Processing and Convolution Neural Networks for Water Quality Detection. In: Kumar, A., Mozar, S. (eds) Proceedings of the 6th International Conference on Communications and Cyber Physical Engineering . ICCCE 2024. Lecture Notes in Electrical Engineering, vol 1096. Springer, Singapore. https://doi.org/10.1007/978-981-99-7137-4_77
Download citation
DOI: https://doi.org/10.1007/978-981-99-7137-4_77
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7136-7
Online ISBN: 978-981-99-7137-4
eBook Packages: EngineeringEngineering (R0)