A Deep Learning Approach to Irrigation Management in Smart Agriculture

This paper introduces a pioneering approach to modernize agricultural practices through the integration of Artificial Neural Networks (ANN) into irrigation management. The primary objectives include achieving accurate data analysis for precise irrigation control, implementing real-time data collection and monitoring, and developing a decision support system that leverages ANN insights to enhance crop health and resource efficiency. The utilization of ANN represents a cutting-edge advancement in smart agriculture, harnessing the capabilities of deep learning and attention mechanisms to optimize irrigation strategies. To build the ANN model, training data is collected and utilized to the objective is to train three models: The Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), along with the proposed Artificial Neural Network (ANN) model. This study assesses the efficacy of the suggested model and conducts a comparative analysis with the support provided by CNN and LSTM algorithms. The ANN model emerged as the standout performer across all metrics. With an accuracy of 97.24%, it outperformed both the LSTM (95.68
^%
) and CNN (93.39
^%
), showcasing its superior capability in accurately classifying temperature and moisture data, critical for precise irrigation management. Moreover, the ANN model demonstrated exceptional specificity and sensitivity values at 96.08% and 95.98%, respectively, surpassing both CNN and LSTM models.