Optimizing Water Resource Utilization in agriculture Using Temporal Residual Convolutional Networks of Weather parameters
Keywords:
Weather Prediction, Deep Learning, Irrigation Optimization, Temporal Residual Convolutional Network, Precision AgricultureAbstract
Accurate weather prediction is crucial for optimizing water irrigation systems, ensuring efficient resource utilization, and improving agricultural productivity. This research explores various deep learning algorithms, including Convolutional Neural Networks (CNN), Temporal Convolutional Networks (TCN), TabNet, Fully Convolutional Neural Networks (FCNN), and a novel Temporal Residual Convolutional Network (TRCN), to enhance predictive accuracy in irrigation forecasting. The data includes key weather variables such as temperature, humidity, rainfall, and wind speed, which were pre-processed to handle missing values and normalize features for optimal model performance. Experimental results revealed that traditional FCNN models performed suboptimally, while CNN, TabNet, and TCN demonstrated significant improvements in accuracy and F1-score. The proposed TRCN model outperformed all other models, achieving an accuracy of 0.99 and an F1-score of 0.97. These findings highlight the effectiveness of deep learning models in weather-based irrigation prediction, with TRCN offering superior predictive capabilities. This research advances precision agriculture by integrating deep learning techniques to enhance irrigation management and promote water conservation.
Downloads
Metrics
References
Fitsum T. Teshome, Haimanote K. Bayabil, Bruce Schaffer, Yiannis Ampatzidis, Gerrit Hoogenboom, Improving soil moisture prediction with deep learning and machine learning models, Computers and Electronics in Agriculture, Volume 226, 2024, 109414, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2024.109414.
Bouali Et-taibi, Mohamed Riduan Abid, El-Mahjoub Boufounas, Abdennabi Morchid, Safae Bourhnane, Tareq Abu Hamed, Driss Benhaddou, Enhancing water management in smart agriculture: A cloud and IoT-Based smart irrigation system, Results in Engineering, Volume 22, 2024, 102283, ISSN 2590-1230, https://doi.org/10.1016/j.rineng.2024.102283.
R. Kanmani, S. Muthulakshmi, K. S. Subitcha, M. Sriranjani, R. Radhapoorani and N. Suagnya, "Modern Irrigation System using Convolutional Neural Network," 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2021, pp. 592-597, doi: 10.1109/ICACCS51430.2021.9441917.
R. Dhyani, N. Manne, J. Garg, D. Motwani, A. K. Shrivastava and M. Sharma, "A Smart Irrigation System Powered by IoT and Machine Learning for Optimal Water Management," 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2024, pp. 1801-1805, doi: 10.1109/ICACITE60783.2024.10617429.
A. Kumar, H. K. Upadhay and J. N. M. Iqubal, "SMART FARMING SYSTEM: Enhancing Water Efficiency, Automated Care, and Disease Detection Using IoT and CNN," 2024 Advances in Science and Engineering Technology International Conferences (ASET), Abu Dhabi, United Arab Emirates, 2024, pp. 1-7, doi: 10.1109/ASET60340.2024.10708639.
Naseeb Singh, Kethavath Ajaykumar, L.K. Dhruw, B.U. Choudhury, Optimization of irrigation timing for sprinkler irrigation system using convolutional neural network-based mobile application for sustainable agriculture, Smart Agricultural Technology, Volume 5, 2023, 100305, ISSN 2772-3755, https://doi.org/10.1016/j.atech.2023.100305.
Xiujie Qiao, Tian Peng, Na Sun, Chu Zhang, Qianlong Liu, Yue Zhang, Yuhan Wang, Muhammad Shahzad Nazir, Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation and multi-step runoff prediction, Expert Systems with Applications, Volume 229, Part B, 2023, 120616, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120616.
Ehteram, M., Afshari Nia, M., Panahi, F. et al. Gaussian mutation–orca predation algorithm–deep residual shrinkage network (DRSN)–temporal convolutional network (TCN)–random forest model: an advanced machine learning model for predicting monthly rainfall and filtering irrelevant data. Environ Sci Eur 36, 13 (2024). https://doi.org/10.1186/s12302-024-00841-9.
Ye, Z., Yin, S., Cao, Y. et al. AI-driven optimization of agricultural water management for enhanced sustainability. Sci Rep 14, 25721 (2024). https://doi.org/10.1038/s41598-024-76915-8.
Xiujie Qiao, Tian Peng, Na Sun, Chu Zhang, Qianlong Liu, Yue Zhang, Yuhan Wang, Muhammad Shahzad Nazir, Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation and multi-step runoff prediction, Expert Systems with Applications, Volume 229, Part B, 2023, 120616, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.120616.
Shah, C.; Du, Q.; Xu, Y. Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification. Remote Sens. 2022, 14, 716.
Taravat, A.; Wagner, M.P.; Bonifacio, R.; Petit, D. Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection. Remote Sens. 2021, 13, 722.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
