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It is widely acknowledged that traditional agricultural practices must effectively address the increasing global demand for food while facing water scarcity and climate change challenges. The imperative for environmentally sustainable agricultural approaches has never been more urgent. In response, IoT-based Smart Agriculture has emerged as a promising solution. Smart Agriculture can significantly bolster agricultural development by integrating renewable energy sources, particularly in arid regions with abundant sunlight. Real-time control systems utilizing big data acquisition and processing are pivotal in this advancement. This study introduces a cloud-based smart irrigation system to connect numerous small-scale smart farms and centralize pertinent data. The system optimizes irrigation water usage through comprehensive big data collection, storage, and analysis. Leveraging the insights from this data can facilitate informed decision-making regarding water management, thereby fostering conservation efforts, particularly in arid regions. Additionally, this research explores weather prediction services to enhance irrigation control, particularly during intermittent rainy periods, within a real-world testbed powered by solar energy. The testbed incorporates a sophisticated big data management system. It showcases a Smart Farm prototype leveraging the Internet of Things, embedded systems, low-cost Wireless Sensor Networks, NI CompactRIO controller, and Cloud Computing. Encouragingly, the results demonstrate tangible improvements in water conservation. Furthermore, the deployment methodology outlined in this study provides a clear roadmap that can be readily adapted for similar research endeavors. © 2023 Elsevier Inc. All rights reserved.
Et-taibi et al. (Tue,) studied this question.