Neural Network Based Smart Irrigation System with Edge Computing Control for Optimizing Water Use

Abstract

Efficient irrigation is critical in agriculture, particularly in regions with erratic rainfall. As global water scarcity intensifies, optimizing irrigation processes is essential to ensure sustainable food production. This study proposes a novel smart irrigation system leveraging neural networks and edge computing to enhance water use efficiency and crop yields. The dataset comprises environmental variables, including pH, water level, temperature, and humidity, sourced from reputable open repositories. Preprocessing steps included handling anomalies, encoding categorical variables, and feature standardization. A neural network with optimized architecture was trained using 70% of the data, validated with 15%, and tested on the remaining 15%. The system achieved a testing accuracy of 91.33%, with precision, recall, F1-score, and AUC metrics exceeding industry benchmarks (AUC: Base = 0.99, Ideal = 0.97, Dry = 0.98). The model was deployed on an NVIDIA Jetson Nano using Docker, demonstrating real-time prediction capabilities with minimal latency. The smart irrigation system automates water pump operations based on soil conditions, providing practical benefits such as reduced water waste and improved crop health. With its adaptable design and scalability, this system represents a step forward in sustainable agriculture, contributing to global efforts to address food security challenges.