A Decomposition-Driven Deep Learning Model for Intelligent Irrigation Decision-Making

Abstract

Traditional farming methods still used in many regions contribute to lower crop yields despite the availability of ample arable land. Integrating emerging technologies such as Machine Learning (ML) and the Internet of Things (IoT) into agriculture can enable intelligent irrigation and smart farming. These technologies support real-time monitoring of environmental parameters and help optimize irrigation schedules using soil moisture, temperature, and humidity data. IoT devices and ML algorithms can predict irrigation needs, reduce water wastage, and enhance crop yields. Therefore, this study proposes a hybrid model that combines a Convolutional Neural Network (CNN) with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for irrigation classification and water conservation in agricultural fields. CEEMDAN decomposes the input features into multiple frequency components, which are then fed to the CNN to improve learning performance. To evaluate the proposed approach, the study developed six models, including standalone and hybrid models. The proposed model achieved the highest accuracy (98%), precision (0.95), recall (1), and F1-score (0.97) compared with the benchmark models. Furthermore, the confusion matrix reveals minimal misclassification, while the ROC curve, with an area under the curve (AUC) of 0.995, confirms excellent discriminative capability. These findings demonstrate the effectiveness and robustness of the proposed model for improving irrigation decision-making and promoting sustainable water management in agriculture.