Advanced Irrigation Prediction Using a 1-D CNN and Multi-Head Attention Hybrid Framework

Abstract

The increasing global population and the corresponding demand for food production necessitate the efficient use of agricultural resources, particularly water. The proposed study investigates the effectiveness of a deep learning model for classifying irrigated and non-irrigated land. The relevance of this study lies in its potential contributions to sustainable agriculture and resource management, particularly in optimizing irrigation practices and improving crop yields. Utilizing a 1-D CNN-Multi-Head Attention model, the proposed study employed a robust methodology that includes data preprocessing, feature extraction and rigorous training processes to enhance classification accuracy. The model was trained and validated on a well-curated dataset containing labeled data representing both irrigated and non-irrigated regions. Performance evaluation proved the model's efficacy with an accuracy of 97%, precision of 96.9% for irrigated areas and 99% for non-irrigated areas, highlighting its ability to accurately distinguish between the two classes. The results indicate a strong potential for this model in real-world applications, providing actionable insights for agricultural stakeholders. This method contributes to the ongoing efforts to leverage technology for efficient agricultural practices, ultimately aiming for more sustainable land use management.