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Proximal Absorbance Calibration Method Using an Embedded Blank Reference RGB Sensor for Determination of Ion Concentrations

Author

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  • Jung-Kyu Lee

    (Department of Biosystems Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Ye-Hun Lee

    (Department of Environmental and Biological Chemistry, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Dong-Hoon Lee

    (Department of Biosystems Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea)

Abstract

Accurate analyses and management of ion concentrations are crucial in precision agriculture. Modern technology-based methods are non-destructive and do not require sample preparation, enabling fast and accurate analysis; however, they have limitations when processing multiple samples. In this study, a multi-ion analysis system was developed for the prompt and accurate analysis of concentrations of important ions such as NO 3 − , H n PO 4 − , K + , Ca 2+ , and Mg 2+ . The RGB sensitivity control was automated through calibrations by applying a reference slot-based error rate across six slots between sample measurements, facilitating sample-to-sample comparisons and enabling accurate concentration analysis. By analyzing the correlation between each ion concentration and the proximal absorbance-based concentration prediction in the simultaneous analysis system, the accuracy was verified by achieving a coefficient of determination exceeding 0.99 for most ions. This system minimizes possible deviations between slots by using an automatic calibration algorithm, thereby facilitating the simultaneous analysis of multiple samples. This is important for saving time and cost and can help in real-time nutrient analysis and monitoring in agriculture.

Suggested Citation

  • Jung-Kyu Lee & Ye-Hun Lee & Dong-Hoon Lee, 2024. "Proximal Absorbance Calibration Method Using an Embedded Blank Reference RGB Sensor for Determination of Ion Concentrations," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2171-:d:1532129
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    References listed on IDEAS

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    1. E. M. B. M. Karunathilake & Anh Tuan Le & Seong Heo & Yong Suk Chung & Sheikh Mansoor, 2023. "The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-26, August.
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