Integrated IoT and AI System for Real-Time Multi-Nutrient Water Quality Analysis in Agriculture

INTRODUCTION: Drinking water that is clean and safe is important for everyone's health. About 1.4 million deaths worldwide are noted to contaminated drinking water each year. Because contaminated water sources are the primary cause of diarrheal infections, they account for about 505,000 deaths every year. To overcome these challenges, this work proposes an integrated IoT and AI-based solution for real-time, multi-nutrient water quality analysis.

OBJECTIVE: In this paper, our objective is to develop a complete system which is integrated with IoT-based water nutrient analysis system using advanced machine learning models that can predict multiple nutrient levels for better crop. To increase the interpretability, reliability, and security of water quality monitoring system.

MATERIAL/METHOD: For data collection we deployed the IoT sensors in different sources like reservoirs, irrigation canals, and ponds for continuously monitoring the parameters like:- phosphorus (P), potassium (K), pH, Temperature, BOD etc. The data which we have collected from the sensors are securely transmitted to a cloud-based platform using end-to-end encryption protocols. Advance machine learning classifiers ensemble learning algorithms are used to analyze the real-time data to gives multi-nutrient predictions. The dataset was collected from GIETU agricultural fields over 6 months from2024 January to till date. We also used Explainable AI (XAI) techniques interpret properly of the machine learning algorithms.

Result: The performance metrics like accuracy, precision, recall, and F1-score are calculated for predicting the water quality. Our experimental observation reveals that the ensemble classifier RFS (Random Forest + SVM) classifier exhibits well and having an accuracy of 90% as comparison to other models. The hybrid classifier significantly higher than the traditional approaches .As well as we used XAI techniques to increased the interpretability of the classifiers to make effective decision-making for water management. For data security we used encryption and decryption algorithms ensured data integrity and protection against unauthorized access.