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Evaluation of event-based irrigation system control scheme for tomato crops in greenhouses

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  • Pawlowski, A.
  • Sánchez-Molina, J.A.
  • Guzmán, J.L.
  • Rodríguez, F.
  • Dormido, S.

Abstract

This work presents a study and evaluation of an event-based predictive control system for a greenhouse irrigation process. The control system objective is to maintain the desired substrate humidity level, keeping the water usage as low as possible. The event-based control scheme uses a crop transpiration model to determine the volume of water required to compensate for the irrigation system and a water content model to trigger the irrigation system events. First, simulation experiments were performed to analyze the behavior of the designed system and to study the water supply dynamics to the substrate and subsequent drainage and evaporation. Secondly, the proposed event-based controller was evaluated in an experimental greenhouse, located in southeast of Spain, in real operation conditions. The resulting control system is able to adapt the actuation rate to the state of the plant providing the efficient way of water consumption. The obtained results show that the application of proposed event-based approach for the greenhouse irrigation system allows to improve the control performance and to reduce the water usage (about 20% of required water for the same performance obtained for commonly used on/off) being an important issue in intensive agriculture. The improved control performance is obtained due to event-based approach and the inclusion of information about the plant dynamic response for water supply and transpiration effect.

Suggested Citation

  • Pawlowski, A. & Sánchez-Molina, J.A. & Guzmán, J.L. & Rodríguez, F. & Dormido, S., 2017. "Evaluation of event-based irrigation system control scheme for tomato crops in greenhouses," Agricultural Water Management, Elsevier, vol. 183(C), pages 16-25.
    • Handle: RePEc:eee:agiwat:v:183:y:2017:i:c:p:16-25
    DOI: 10.1016/j.agwat.2016.08.008
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    References listed on IDEAS

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    1. Fernandez, M.D. & Gonzalez, A.M. & Carreno, J. & Perez, C. & Bonachela, S., 2007. "Analysis of on-farm irrigation performance in Mediterranean greenhouses," Agricultural Water Management, Elsevier, vol. 89(3), pages 251-260, May.
    2. Van Vosselen, A. & Verplancke, H. & Van Ranst, E., 2005. "Assessing water consumption of banana: traditional versus modelling approach," Agricultural Water Management, Elsevier, vol. 74(3), pages 201-218, June.
    3. Reca, J. & Torrente, C. & López-Luque, R. & Martínez, J., 2016. "Feasibility analysis of a standalone direct pumping photovoltaic system for irrigation in Mediterranean greenhouses," Renewable Energy, Elsevier, vol. 85(C), pages 1143-1154.
    4. Shin, Jong Hwa & Park, Jong Seok & Son, Jung Eek, 2014. "Estimating the actual transpiration rate with compensated levels of accumulated radiation for the efficient irrigation of soilless cultures of paprika plants," Agricultural Water Management, Elsevier, vol. 135(C), pages 9-18.
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    Cited by:

    1. Achour, Yasmine & Ouammi, Ahmed & Zejli, Driss, 2021. "Technological progresses in modern sustainable greenhouses cultivation as the path towards precision agriculture," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    2. Theodora Karanisa & Yasmine Achour & Ahmed Ouammi & Sami Sayadi, 2022. "Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar," Environment Systems and Decisions, Springer, vol. 42(4), pages 521-546, December.
    3. Francis J. Baumont De Oliveira & Scott Ferson & Ronald Dyer, 2021. "A Collaborative Decision Support System Framework for Vertical Farming Business Developments," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(1), pages 1-33, January.
    4. Chiara Bersani & Carmelina Ruggiero & Roberto Sacile & Abdellatif Soussi & Enrico Zero, 2022. "Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0," Energies, MDPI, vol. 15(10), pages 1-30, May.
    5. Showkat Ahmad Bhat & Nen-Fu Huang & Imtiyaz Hussain & Farzana Bibi & Uzair Sajjad & Muhammad Sultan & Abdullah Saad Alsubaie & Khaled H. Mahmoud, 2021. "On the Classification of a Greenhouse Environment for a Rose Crop Based on AI-Based Surrogate Models," Sustainability, MDPI, vol. 13(21), pages 1-18, November.

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