Cloud-enabled IoT smart irrigation with big data analytics and renewable energy for sustainable agriculture in arid regions

Engineering notes

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Chinese explanation / 中文解读

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Original abstract

Solar-powered smart irrigation systems play a crucial role in promoting efficient water use, particularly in water-scarce regions. By enabling targeted irrigation based on continuous monitoring, these systems help conserve water and reduce energy consumption. However, challenges such as high installation costs, technical complexity, and limited accessibility continue to hinder their large-scale adoption. This manuscript presents a cloud-enabled IoT-based smart irrigation system integrated with big data analytics and renewable energy to enhance sustainable agriculture in hot arid regions, specifically representative of South Asian arid zones (Rajasthan, India), the Middle East and North Africa (MENA) region, and the sub-Saharan Sahel region. The primary objective is to reduce operational costs at irrigation pumping stations through data-driven optimization. Input data are obtained from the Pump Sensor Data Dataset. The proposed framework SPINN–WBO combines a Statistical-Physics Informed Neural Network (SPINN) for water demand forecasting with a Wolf-Bird Optimizer (WBO) for efficient water distribution. The model is implemented in MATLAB and evaluated using standard performance metrics. Experimental results demonstrate that the SPINN–WBO approach reduces water consumption to 20–100 KL over 30 days, minimizes operational costs to $7,080, and enhances overall energy efficiency. Furthermore, SPINN–WBO outperforms conventional models such as artificial neural network (ANN), deep neural network (DNN), and support vector machine (SVM). Overall, this study contributes to advancing sustainable and climate-resilient agriculture, supporting the goals of SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), and SDG 13 (Climate Action).

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