Abstract
The metabolism and growth of vegetation are highly dependent on the changes in soil water content. Irrigation scheduling and application of water at the right time and rate are a key aspect for precision irrigation. In this study, the Long Short-Term Memory (LSTM) Neural Network model was studied to predict irrigation prescriptions for 1, 3, 6, 12 and 24 h in advance. Training data for LSTM were collected from a precision irrigation study conducted in Alabama, USA. The prediction estimation of irrigation prescription used soil matric potential data measured within two contrasting soil types. Performance of the LSTM models were evaluated by comparing neural network parameters and prediction capability by soil type. The optimal learning algorithm for each case was also determined. The LSTM Neural Network showed good prediction capabilities for both soil types, with \(R^{2}\) ranging between 0.82 and 0.98 for one hour ahead prescription and getting smaller as prediction time increases. The irrigation rate prediction was verified by actual observations that demonstrate the suitability of the machine learning technique as a decision-support tool for irrigation scheduling.
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Acknowledgements
We want to acknowledge the farmer Jim Lewey for his collaboration. Funding for this project was provided by the Natural Resources Conservation Service - Alabama Soil and Water Conservation Committee, the Alabama Association of Conservation Districts and the Alabama Agricultural Experimental Station. A-F. Jimenez expresses his gratitude to the Department of Boyacá and Minciencias – Colombia for the support through the scholarship program No. 733 - 2015 for the Ph.D. at the Universidad Nacional de Colombia and also to the Universidad de Los Llanos, Villavicencio, Colombia.
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Jimenez, AF., Ortiz, B.V., Bondesan, L. et al. Long Short-Term Memory Neural Network for irrigation management: a case study from Southern Alabama, USA. Precision Agric 22, 475–492 (2021). https://doi.org/10.1007/s11119-020-09753-z
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DOI: https://doi.org/10.1007/s11119-020-09753-z