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Deep learning-based classification of nitrate and nitrite concentrations from water samples using colorimetric test strip images.

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Title: Deep learning-based classification of nitrate and nitrite concentrations from water samples using colorimetric test strip images.
Authors: Roman, Muhammad1 (AUTHOR) muhammad.roman@sdstate.edu, Sher, Mazhar1 (AUTHOR) mazhar.sher@sdstate.edu, Kuruppuarachchi, Chamika1 (AUTHOR) Chamika.Kuruppuarachchi@sdstate.edu, Ali, Arshid2 (AUTHOR) arshid.ali@sdstate.edu, Pack, Chulwoo2 (AUTHOR) chulwoo.pack@sdstate.edu, Zahid, Azlan3 (AUTHOR) azlan.zahid@ag.tamu.edu, Nafchi, Ali Mirzakhani1,4 (AUTHOR) Ali.Nafchi@sdstate.edu
Source: Environmental Monitoring & Assessment. May2026, Vol. 198 Issue 5, p1-19. 19p.
Subject Terms: *Nitrates, *Nitrites, *Convolutional neural networks, *Image recognition (Computer vision), *Water quality monitoring, *Computer vision, *Deep learning
Abstract: Accurate monitoring of nitrate and nitrite concentrations in water is essential for sustainable agriculture, safeguarding public health, and protecting aquatic ecosystems from nutrient pollution. Traditional methods for detecting nitrate and nitrite in water samples are precise but costly, complex, and time-consuming, limiting their practicality for frequent on-site testing. This research proposes deep learning-based computer vision techniques to classify nitrate and nitrite concentrations using images of colorimetric test strips. An RGB IMX219 camera was used to acquire images of colorimetric test strips under standardized, controlled illumination conditions to ensure consistent image quality. A total of 1938 nitrate images and 1190 nitrite images were collected before augmentation. After preprocessing and training-only data augmentation, both classical machine learning baselines based on hand-crafted color and texture features and deep learning models—including a multilayer perceptron (MLP) and convolutional neural networks (AlexNet, VGG16, ResNet18, and GoogLeNet)—were trained and evaluated using an independent test set and stratified fivefold cross-validation. For nitrate classification, ResNet18 and GoogLeNet achieved near-perfect 100% test accuracy, with mean cross-validation accuracy of 99.97% ± 0.04%, substantially outperforming classical baseline models based on hand-crafted color and texture features, which achieved at most 83.5% test accuracy. For nitrite classification, GoogLeNet achieved the strongest overall performance, with a test accuracy of 97.48% and a fivefold cross-validation accuracy of 95.22% ± 1.17%, substantially outperforming the best classical baseline model, which achieved a maximum test accuracy of 83.19%. These results demonstrate that deep CNN-based feature learning provides a significant performance advantage over simpler methods under controlled imaging conditions, supporting the suitability of the proposed system for rapid, image-based water quality assessment and motivating future evaluation under broader real-world deployment scenarios. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193884663
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Deep learning-based classification of nitrate and nitrite concentrations from water samples using colorimetric test strip images.
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  Data: <searchLink fieldCode="AR" term="%22Roman%2C+Muhammad%22">Roman, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> muhammad.roman@sdstate.edu</i><br /><searchLink fieldCode="AR" term="%22Sher%2C+Mazhar%22">Sher, Mazhar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mazhar.sher@sdstate.edu</i><br /><searchLink fieldCode="AR" term="%22Kuruppuarachchi%2C+Chamika%22">Kuruppuarachchi, Chamika</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Chamika.Kuruppuarachchi@sdstate.edu</i><br /><searchLink fieldCode="AR" term="%22Ali%2C+Arshid%22">Ali, Arshid</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> arshid.ali@sdstate.edu</i><br /><searchLink fieldCode="AR" term="%22Pack%2C+Chulwoo%22">Pack, Chulwoo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> chulwoo.pack@sdstate.edu</i><br /><searchLink fieldCode="AR" term="%22Zahid%2C+Azlan%22">Zahid, Azlan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> azlan.zahid@ag.tamu.edu</i><br /><searchLink fieldCode="AR" term="%22Nafchi%2C+Ali+Mirzakhani%22">Nafchi, Ali Mirzakhani</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> Ali.Nafchi@sdstate.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Monitoring+%26+Assessment%22">Environmental Monitoring & Assessment</searchLink>. May2026, Vol. 198 Issue 5, p1-19. 19p.
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  Data: Accurate monitoring of nitrate and nitrite concentrations in water is essential for sustainable agriculture, safeguarding public health, and protecting aquatic ecosystems from nutrient pollution. Traditional methods for detecting nitrate and nitrite in water samples are precise but costly, complex, and time-consuming, limiting their practicality for frequent on-site testing. This research proposes deep learning-based computer vision techniques to classify nitrate and nitrite concentrations using images of colorimetric test strips. An RGB IMX219 camera was used to acquire images of colorimetric test strips under standardized, controlled illumination conditions to ensure consistent image quality. A total of 1938 nitrate images and 1190 nitrite images were collected before augmentation. After preprocessing and training-only data augmentation, both classical machine learning baselines based on hand-crafted color and texture features and deep learning models—including a multilayer perceptron (MLP) and convolutional neural networks (AlexNet, VGG16, ResNet18, and GoogLeNet)—were trained and evaluated using an independent test set and stratified fivefold cross-validation. For nitrate classification, ResNet18 and GoogLeNet achieved near-perfect 100% test accuracy, with mean cross-validation accuracy of 99.97% ± 0.04%, substantially outperforming classical baseline models based on hand-crafted color and texture features, which achieved at most 83.5% test accuracy. For nitrite classification, GoogLeNet achieved the strongest overall performance, with a test accuracy of 97.48% and a fivefold cross-validation accuracy of 95.22% ± 1.17%, substantially outperforming the best classical baseline model, which achieved a maximum test accuracy of 83.19%. These results demonstrate that deep CNN-based feature learning provides a significant performance advantage over simpler methods under controlled imaging conditions, supporting the suitability of the proposed system for rapid, image-based water quality assessment and motivating future evaluation under broader real-world deployment scenarios. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193884663
RecordInfo BibRecord:
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        Value: 10.1007/s10661-026-15297-y
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1
    Subjects:
      – SubjectFull: Nitrates
        Type: general
      – SubjectFull: Nitrites
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Image recognition (Computer vision)
        Type: general
      – SubjectFull: Water quality monitoring
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      – SubjectFull: Computer vision
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: Deep learning-based classification of nitrate and nitrite concentrations from water samples using colorimetric test strip images.
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            NameFull: Roman, Muhammad
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            NameFull: Kuruppuarachchi, Chamika
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 01676369
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              Value: 198
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              Value: 5
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            – TitleFull: Environmental Monitoring & Assessment
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