Resource Management in Agriculture through IoT and Machine Learning

Abstract

In light of agriculture's substantial contribution to global freshwater usage, the imperative for sustainable water resource management is paramount. This study introduces a cutting-edge smart irrigation system, employing IoT technology and machine learning algorithms, to optimize agricultural water productivity. A real-time IoT network, comprising soil moisture sensors and microcontroller gateways, captures field data, which is subsequently transmitted to a cloud-based platform. Data-driven predictive models leveraging Support Vector Machines (SVM), Artificial Neural Networks (ANN), and regression tree and LiNGAM algorithms are constructed to accurately anticipate daily crop water requirements. By seamlessly integrating sensor data with predictive modeling, the system precisely adjusts irrigation levels to align with specific crop demands, initiating automated scheduled irrigation events. This research article focusing on increase in water utalizaton efficiency compared to conventional timer-based irrigation methods by maintain pH value and dissolved oxygen in industrial water. The presented data-centric IoT approach represents a significant advancement in agricultural water management, holding substantial promise for sustainable and precise irrigation practices amid the backdrop of global water scarcity challenges.