Soil moisture based automatic irrigation system to improve water productivity
Automatic irrigation system
Keywords:
Irrigation automation; Software; Soil moisture; Water productivityAbstract
Irrigation automation plays vital role in agricultural water management system. An efficient automatic irrigation system is crucial to improve water productivity. Soil moisture based irrigation is an economical and efficient approach for automation of irrigation system. Herein, an experiment is conducted for irrigation automation based on the soil moisture content and crop growth stage. The experimental findings exhibit that, the proposed model triggers the water supply accurately based on the real-time soil moisture content and maintains soil-water balance as per the crop growth stage requirement.
References
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Angal S and Mali R. 2016. Raspberry pi and arduino based automated irrigation system. International Journal of Science and Research 5(7):1145-1148.
Arnell NW, Van Vuuren DP and Isaac M. 2011. The implications of climate policy for the impacts of climate change on global water resources. Global Environmental Change 21(2):592-603.
Chate BK and Rana JG. 2016. Smart irrigation system using Raspberry Pi. International Research Journal of Engineering and Technology 3(05):247-249.
Chartzoulakis K and Bertaki M. 2015. Sustainable water management in agriculture under climate change. Agriculture and Agricultural Science Procedia 4:88-98.
Dabach S, Lazarovitch N, Šimůnek J and Shani U. 2013. Numerical investigation of irrigation scheduling based on soil water status. Irrigation Science 31(1):27-36.
Provenzano, G., Sinobas, L.R., 2014. Special issue on trends and challenges of sustainable irrigated agriculture. J. Irrig. Drain. Engg. 140: 9–11.
Rodger SH and Gramond E. 1998. JFLAP: An Aid to Studying Theorems in Automata Theory. ACM SIGCSE Bulletin inroads, 30(3), p.302Malge, S. and Bhole, K., 2015, May.
Safriel U, Adeel Z, Niemeijer D, Puigdefabregas J, White R, Lal R, Winsolow M, Ziedler J, Prince S, Archer E and King C. 2006. Dry land systems. In Ecosystems and Human Well-being. Current State and Trends, Vol. 1 (pp. 625-656). Island Press.
Soulis KX and Elmaloglou, S. 2018. Optimum soil water content sensors placement for surface drip irrigation scheduling in layered soils. Computers and electronics in agriculture 152:1-8.
Angal S and Mali R. 2016. Raspberry pi and arduino based automated irrigation system. International Journal of Science and Research 5(7):1145-1148.
Arnell NW, Van Vuuren DP and Isaac M. 2011. The implications of climate policy for the impacts of climate change on global water resources. Global Environmental Change 21(2):592-603.
Chate BK and Rana JG. 2016. Smart irrigation system using Raspberry Pi. International Research Journal of Engineering and Technology 3(05):247-249.
Chartzoulakis K and Bertaki M. 2015. Sustainable water management in agriculture under climate change. Agriculture and Agricultural Science Procedia 4:88-98.
Dabach S, Lazarovitch N, Šimůnek J and Shani U. 2013. Numerical investigation of irrigation scheduling based on soil water status. Irrigation Science 31(1):27-36.
Provenzano, G., Sinobas, L.R., 2014. Special issue on trends and challenges of sustainable irrigated agriculture. J. Irrig. Drain. Engg. 140: 9–11.
Rodger SH and Gramond E. 1998. JFLAP: An Aid to Studying Theorems in Automata Theory. ACM SIGCSE Bulletin inroads, 30(3), p.302Malge, S. and Bhole, K., 2015, May.
Safriel U, Adeel Z, Niemeijer D, Puigdefabregas J, White R, Lal R, Winsolow M, Ziedler J, Prince S, Archer E and King C. 2006. Dry land systems. In Ecosystems and Human Well-being. Current State and Trends, Vol. 1 (pp. 625-656). Island Press.
Soulis KX and Elmaloglou, S. 2018. Optimum soil water content sensors placement for surface drip irrigation scheduling in layered soils. Computers and electronics in agriculture 152:1-8.
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Published
2022-01-05
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