Artificial intelligence-enabled monitoring of pesticide contamination in agricultural water systems: a comprehensive review of intelligent sensing technologies for water quality surveillance and environmental risk assessment
9 affiliations
- 1Department of Chemical Engineering, College of Engineering, King Faisal University Al Ahsa 31982 Saudi Arabia
- 2Department of Software Engineering, Konya Technical University Konya 42250 Türkiye
- 3Department of Soil Science & Plant Nutrition, Selçuk University Konya 42079 Türkiye
- 4Department of Civil Engineering, College of Engineering, Taif University P.O. Box 11099 Taif City 21974 Saudi Arabia
- 5Department of Mechanical Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) 26600 Malaysia
- 6Center for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology 32093 Hawally Kuwait
- 7Department of Botany, Bacha Khan University Charsadda Khyber Pakhtunkhwa 24420 Pakistan
- 8Key Laboratory of Fruit Breeding Technology of the Ministry of Agriculture, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences Zhengzhou 450009 China
- 9Department of Chemistry, Quaid-I-Azam University Islamabad Islamabad 45320 Pakistan
Abstract
Pesticide contamination in agricultural water systems threatens ecosystem health, water security, and sustainable agriculture. Conventional laboratory analyses are highly accurate but remain expensive, labor-intensive, and unsuitable for continuous field monitoring. This review summarizes recent advances in artificial intelligence (AI)-enabled sensing technologies for the real-time detection of pesticides in irrigation water, rivers, reservoirs, drainage canals, and groundwater. It covers electrochemical sensors, biosensors, hyperspectral imaging, fluorescence spectroscopy, optical sensing, and microfluidic platforms integrated with Internet of Things (IoT) networks, edge computing, and cloud analytics. Particular emphasis is placed on machine learning, deep learning, explainable AI, federated learning, graph neural networks, and multimodal data fusion for contaminant detection, prediction, and risk assessment. The distinctive contribution of this review is providing a pesticide-specific source-to-decision framework that links hydrological transport and chemical transformation to matrix-conditioned sensor responses, AI-assisted signal interpretation, validation maturity, and field deployment, thereby identifying where intelligent analytics can provide a meaningful advantage for pesticide surveillance in agricultural water systems.
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BibTeX
@article{khan2026artificial,
title = {{Artificial intelligence-enabled monitoring of pesticide contamination in agricultural water systems: a comprehensive review of intelligent sensing technologies for water quality surveillance and environmental risk assessment}},
author = {Hayat Khan and Sani Isnain and Muhammad Anas and Abdullah H. Alshahri and Najam U. Saqib and R. Nawaz and Javed Iqbal and Waseem Ahmed Khattak and Khizar Hayat Khan},
journal = {RSC advances},
year = {2026},
month = nov,
doi = {10.1039/d6ra07513d},
url = {https://europepmc.org/article/PMC/PMC13730392},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial intelligence-enabled monitoring of pesticide contamination in agricultural water systems: a comprehensive review of intelligent sensing technologies for water quality surveillance and environmental risk assessment
AU - Hayat Khan
AU - Sani Isnain
AU - Muhammad Anas
AU - Abdullah H. Alshahri
AU - Najam U. Saqib
AU - R. Nawaz
AU - Javed Iqbal
AU - Waseem Ahmed Khattak
AU - Khizar Hayat Khan
PY - 2026
DA - 2026/11/03
JO - RSC advances
DO - 10.1039/d6ra07513d
UR - https://europepmc.org/article/PMC/PMC13730392
AB - Pesticide contamination in agricultural water systems threatens ecosystem health, water security, and sustainable agriculture. Conventional laboratory analyses are highly accurate but remain expensive, labor-intensive, and unsuitable for continuous field monitoring. This review summarizes recent advances in artificial intelligence (AI)-enabled sensing technologies for the real-time detection of pesticides in irrigation water, rivers, reservoirs, drainage canals, and groundwater. It covers electrochemical sensors, biosensors, hyperspectral imaging, fluorescence spectroscopy, optical sensing, and microfluidic platforms integrated with Internet of Things (IoT) networks, edge computing, and cloud analytics. Particular emphasis is placed on machine learning, deep learning, explainable AI, federated learning, graph neural networks, and multimodal data fusion for contaminant detection, prediction, and risk assessment. The distinctive contribution of this review is providing a pesticide-specific source-to-decision framework that links hydrological transport and chemical transformation to matrix-conditioned sensor responses, AI-assisted signal interpretation, validation maturity, and field deployment, thereby identifying where intelligent analytics can provide a meaningful advantage for pesticide surveillance in agricultural water systems.
SN - 2046-2069
ER -