Advancing precision agriculture: Enhancing irrigation systems with IoT and machine learning

Abstract

Precision farming has revolutionized modern agriculture by focusing on the appropriate use of resources and limits to environmental impacts to produce higher yields from crops. The current research intends to look at the application of its many technological advances, namely the Internet of Things (IoT) and Machine Learning (ML), to better equip irrigation systems in precision agriculture to promote their efficient use, with the aim to develop a Hybrid cuttlefish search optimized radar support vector machine (HCSO-RSVM) model, which will facilitate the precision irrigation process through quantified data driven control actions, from real time data collected from the field through IoT-enabled sensors. The sensor data was pre-processed with Min-Max Normalization, depending on these coordinate signs for uniform input, hence promoting optimal processing and ensuring better system quality and efficiency. The current research represents a development by which irrigation management will be enabled for timely decision making by monitoring changes in environmental factors e.g., water levels, weather, and soil moisture content. The HCSO-RSVM model performance is so positive that it appears to have outperformed classic methods in high metric performance with 97% accuracy, precision turned out to be 94%, recall of 93%- which affirms its reliability in administration of irrigation processes. This present research provides a view of the way forward to improving water use efficiency in sustainable agriculture and food security in global environmental challenges by full integration of IoT in agriculture with intelligent application. The paper therefore, indicated the importance of data-driven models would help to reinforcement increasing precision agriculture and sustainability of agricultural practices as the agricultural landscape advances.