A Novel Deep Learning Architecture for Irrigation Prediction Using ID-CNN-Bidirectional GRU- Fusion Model

Irrigation management plays a crucial role in sustainable agriculture by optimizing water usage and minimizing resource wastage, especially in the face of global challenges like water scarcity and climate change. Traditional irrigation prediction models often lead to inefficient water use, either by under-irrigating or over-irrigating, thus affecting crop yield. This study proposes a novel hybrid deep learning (DL) model that combines a onedimensional convolutional neural network (1D-CNN) and Bidirectional Gated Recurrent Units (Bi-GRU) to enhance
irrigation prediction accuracy. The 1D-CNN excels at spatial feature extraction, enabling the model to detect localized
patterns in environmental and soil moisture data, while the Bi-GRU captures temporal dependencies by processing
sequential data in both forward and backward directions. This hybrid approach addresses the shortcomings of
conventional models by effectively learning both spatial and temporal relationships within the data, leading to more
accurate and adaptive irrigation predictions. The model is trained and evaluated using the Irrigation Scheduling for
Smart Agriculture dataset, which includes various environmental and soil moisture parameters. The proposed hybrid model achieved an accuracy of 97.29%, outperforming traditional models and demonstrating its potential to optimize irrigation management. This study presents a scalable and adaptive solution for intelligent irrigation systems, offering a promising approach to reduce water wastage, enhance crop yield, and contribute to sustainable agricultural practices. The novel combination of CNN and Bi-GRU provides a significant advancement over existing techniques, making it a valuable contribution to the field of smart agriculture.