Reference Evapotranspiration Prediction for Smart Irrigation
Samer I. Mohamed
Samer I. Mohamed*, Associate Prof. October University for Modern Sciences and Arts MSA.
Manuscript received on October 05, 2020. | Revised Manuscript received on October 10, 2020. | Manuscript published on October 30, 2020. | PP: 323-333 | Volume-10 Issue-1, October 2020. | Retrieval Number: 100.1/ijeat.A18241010120 | DOI: 10.35940/ijeat.A1824.1010120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: : Irrigation is the most critical process for agriculture, but irrigation is the largest consumer of fresh water and causes the loss of large quantities because of the inaccuracy in crop water estimation. Our proposed system aims to improve irrigation management by estimating the amount of water needed by the crop accurately and reduces the number of meteorological parameters needed for such estimation. Detection of the reference crop evapotranspiration (ETo) is the most critical process in crop water estimation, that is considered through our proposed solution by implementing machine learning models using neural networks and linear regression to predict daily ETo using climate data like temperature, humidity, wind speed, and solar radiation. Comparing our system results with FAO-56 Penman-Monteith ET0 and cropwat8.0 software as benchmark, show that our proposed system is better than the linear regression model, in terms of determination coefficient (R^2)=.9677 and root mean square error(RMSE) =.1809, while the multiple linear regression model achieved determination coefficient (R^2)=.68 and root mean square error(RMSE) =3.01. Our system then used the predicted ETo and Crop coefficient (Kc) from FAO, to estimate crop evapotranspiration (ETc) for precision irrigation target.
Keywords: Evapotranspiration; machine learning; FAO_56; Neural network; predict; linear regression; irrigation.