Edge Centric IoT Framework for Agricultural Water Quality Prediction Leveraging Optimized Deep Neural Networks

Abstract

In modern agriculture, the quality of irrigation water plays a crucial role in determining crop productivity and soil health. With increasing reliance on precision farming methods, there is a growing need for intelligent systems that can continuously monitor and predict water quality to ensure safe and efficient usage. This research presents a novel framework that integrates Internet of Things (IoT) technology with an advanced deep learning model for real-time water quality analysis in agriculture. A network of wireless, low-power sensors is deployed in the field to capture essential water parameters including temperature, pH level, turbidity, salinity, and nutrient concentration. These sensors transmit data in real time to a processing unit where the prediction model operates. To analyze the complex and time-sensitive data, a hybrid deep neural architecture is developed by combining Temporal Convolutional Networks (TCNs) and Long Short-Term Memory (LSTM) networks. TCNs are responsible for extracting local patterns and trends from sequential data, while LSTM units help retain long-term dependencies, making the model suitable for forecasting water quality over time. To further enhance the model’s performance, a metaheuristic optimization method Harris Hawks Optimization (HHO) is implemented. HHO dynamically adjusts the model’s hyperparameters, improving its learning efficiency and predictive accuracy. Experimental evaluation is carried out using both synthetic datasets and real-world water quality data collected from agricultural regions. The proposed model is benchmarked against traditional machine learning and deep learning algorithms, showing significant improvements in accuracy, precision, recall, and F1-score. Additionally, the system is integrated with a mobile-friendly user interface, allowing farmers to receive instant alerts and recommendations based on predicted water quality. This smart, automated, and scalable approach offers a practical solution for resource-efficient farming and contributes to long-term agricultural sustainability.