A Deep Learning and Cloud-Enabled Smart Irrigation System for Water Usage Optimization in Agriculture
Keywords:
Agriculture, Cloud computing, CNN, Deep learning, IoT, LSTM, Precision farming, Smart irrigation, Water managementAbstract
Water scarcity is one of the most pressing challenges affecting global agriculture today. Traditional irrigation practices, often manual or schedule-based, lead to significant water wastage, inefficient crop management, and increased operational costs. With growing concerns over climate change, rising population and the urgent need for sustainable resource utilization, optimizing water usage in agriculture has become critical. This study proposes a cloud-enabled, deep learning-based smart irrigation system designed to optimize water use without compromising crop yield. Leveraging a hybrid model combining Convolutional Neural Networks (CNNs) for spatial data analysis (e.g., NDVI imagery) and Long Short-Term Memory (LSTM) networks for time-series environmental data prediction, the system forecasts precise irrigation schedules tailored to real-time conditions.
Data from publicly available sources, including Kaggle Smart Irrigation Datasets, NOAA climate archives, and Sentinel-2 satellite imagery, were integrated into the model. A cloud computing platform Amazon Web Services (AWS) was employed to ensure scalable, real-time data processing and remote decision-making capabilities. Performance evaluations using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² scores demonstrated a significant improvement in water-saving efficiency compared to traditional threshold-based irrigation systems.
Results indicate that the proposed system achieved approximately 38% water savings, outperforming existing methods that averaged around 20% savings. Moreover, model predictions maintained high accuracy across different climatic conditions, suggesting strong generalization capabilities. By integrating advanced AI models with cloud-based infrastructures, this approach promotes sustainable agriculture practices, enhances food security, and provides an affordable solution scalable from small farms to large agricultural enterprises.
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