Modern information technologies in irrigation water quality assessment
Keywords:
Agriculture, coding, digitalization, environmental threats, information technologies, irrigation, monitoring, plant care, rule-based artificial intelligence, water quality, web developmentSynopsis
Comprehensive evaluation of irrigation water quality is an important precondition for sustainable crop production on irrigated lands. Neglecting water quality monitoring can cause soil health deterioration, fertility loss, toxic effects on plants, reduced yield quality, and a decline in cropland productivity.
Comprehensive control of irrigation water quality, especially when the source is doubtful, requires time, specialized knowledge, and qualified labor. Raw laboratory results from water sample analysis are often difficult for non-specialists to interpret. Therefore, scientific expertise is required to calculate agronomic indicators and interpret them correctly. However, such expertise is not always available or affordable. As a result, the gap between scientific water quality assessment and practical implementation remains wide.
The main aim of this study is to introduce WaterQ AI, a web-based platform for rapid, scientifically grounded, and comprehensive analysis of irrigation water quality. The application was developed using a modern web development stack and scientific logic for the calculation and interpretation of 16 agronomically important irrigation water parameters using only pH and ion concentrations as input data. The platform covers indicators related to salinity, sodicity, alkalinity, water permeability, toxic ion effects, and the balance of major cations and anions (such as sodium adsorption ratio, magnesium ratio, Kelly's ratio, etc.). Its web-based format makes the tool accessible without installation, supports use on different devices, and facilitates application in research, education, consulting, and farm-level decision-making.
The comparison of WaterQ AI calculations with manual expert calculations showed an extremely small discrepancy of less than 0.15%, confirming the reliability of the implemented logic. The platform provides rapid, scalable, consistent, and reproducible evaluation of irrigation water quality indicators, reducing the time and cost required for assessment. It transforms laboratory data into agronomically meaningful conclusions and identifies risks of secondary salinization, alkalization, soil structure degradation, reduced infiltration, and crop toxicity. Nevertheless, the application should be regarded as a decision-support tool, not a replacement for professional expertise in agricultural melioration. Human expertise remains essential in edge cases, when final measures must consider soil texture, drainage, irrigation method, crop sensitivity, climate, and local agricultural standards.
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Text Identifier of this Chapter
Chapter 5
Published
December 4, 2025
Copyright (c) 2025 Pavlo Lykhovyd, Volodymyr Silchenko, Lesia Kononenko, Vira Savchenko, Alla Karnaushenko, Yuliia Hlavatska, Hryhorii Savelenko, Nataliia Sysolina, Iryna Sysolina, Kateryna Vasylkovska, Olha Andriienko, Valentyna Malakhovska, Ilona Androshchuk, Oleksii Vasylkovskyi, Serhii Moroz
How to Cite
Lykhovyd, P., Karashchuk, G., & Kulidzhanov, E. (2025). Modern information technologies in irrigation water quality assessment. In P. Lykhovyd, V. Silchenko, L. Kononenko, V. Savchenko, A. Karnaushenko, Y. Hlavatska, H. Savelenko, N. Sysolina, I. Sysolina, K. Vasylkovska, O. Andriienko, V. Malakhovska, I. Androshchuk, O. Vasylkovskyi, S. Moroz, G. Karashchuk, & E. Kulidzhanov, In Press. Innovative technological solutions in agriculture. Scientific Route OÜ®. https://monograph.route.ee/rout/catalog/book/innovative_technological_solutions_in_agriculture/chapter/207
