IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v183y2017icp107-115.html
   My bibliography  Save this article

Leaf area index estimation for a greenhouse transpiration model using external climate conditions based on genetics algorithms, back-propagation neural networks and nonlinear autoregressive exogenous models

Author

Listed:
  • Wang, H.
  • Sánchez-Molina, J.A.
  • Li, M.
  • Berenguel, M.
  • Yang, X.T.
  • Bienvenido, J.F.

Abstract

A precise transpiration prediction model thus becomes an important tool for greenhouse automatic irrigation management. Moreover, leaf is an organ of transpiration, and leaf area index is a basic variable to estimate this water lost, but it is still a weak spot in the crop growth estimation. In this paper, two different leaf area index models are established and compared with the evolution of the real crop determined with an electronic planimeter: (1) Considering the temperature and photosynthetically active radiation (PAR) as the main impact factors over crop growth, a TEP-LAI model based on product of thermal effectiveness and PAR is built to estimate the leaf area index dynamics; and (2) TOM-LAI model based on a tomato growth model is also used to estimate the leaf area index as an explicit function of the number of leaves and vines. Finally, the results of both simulation models (TEP-LAI and TOM-LAI) are compared with the measured values. Moreover, a crop transpiration model is established using the empirical data sampled in a multi-span greenhouse in Almeria (Spain). In this greenhouse, a microlysimeter (two different weight scales) was used to obtain the transpiration and the drainage values. Thus, the data collected is used to obtain a model of the estimated water lost by transpiration, that it is based on Back Propagation-Neural Network was optimized using genetic algorithm and Nonlinear Auto-regressive model with Exogenous Inputs model. Once described the different models, the estimated values of leaf area index are compared satisfactorily with the measured ones. TEP-LAI is the model chosen to be introduced as input of the final transpiration model. As expected, the transpiration estimation with inside conditions generates better results, but the outside climate based model shows that it could be used as an irrigation predictor with data from cheaper outside meteorological stations.

Suggested Citation

  • Wang, H. & Sánchez-Molina, J.A. & Li, M. & Berenguel, M. & Yang, X.T. & Bienvenido, J.F., 2017. "Leaf area index estimation for a greenhouse transpiration model using external climate conditions based on genetics algorithms, back-propagation neural networks and nonlinear autoregressive exogenous ," Agricultural Water Management, Elsevier, vol. 183(C), pages 107-115.
    • Handle: RePEc:eee:agiwat:v:183:y:2017:i:c:p:107-115
    DOI: 10.1016/j.agwat.2016.11.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377416304668
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2016.11.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gallardo, M. & Thompson, R.B. & Rodríguez, J.S. & Rodríguez, F. & Fernández, M.D. & Sánchez, J.A. & Magán, J.J., 2009. "Simulation of transpiration, drainage, N uptake, nitrate leaching, and N uptake concentration in tomato grown in open substrate," Agricultural Water Management, Elsevier, vol. 96(12), pages 1773-1784, December.
    2. Kheradmanda, Saeid & Nematollahi, Omid & Ayoobia, Ahmad Reza, 2016. "Clearness index predicting using an integrated artificial neural network (ANN) approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1357-1365.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. He, Zhihao & Li, Manning & Cai, Zelin & Zhao, Rongsheng & Hong, Tingting & Yang, Zhi & Zhang, Zhi, 2021. "Optimal irrigation and fertilizer amounts based on multi-level fuzzy comprehensive evaluation of yield, growth and fruit quality on cherry tomato," Agricultural Water Management, Elsevier, vol. 243(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pedro Garcia-Caparros & Juana Isabel Contreras & Rafael Baeza & Maria Luz Segura & Maria Teresa Lao, 2017. "Integral Management of Irrigation Water in Intensive Horticultural Systems of Almería," Sustainability, MDPI, vol. 9(12), pages 1-21, December.
    2. Liang, Hao & Lv, Haofeng & Batchelor, William D. & Lian, Xiaojuan & Wang, Zhengxiang & Lin, Shan & Hu, Kelin, 2020. "Simulating nitrate and DON leaching to optimize water and N management practices for greenhouse vegetable production systems," Agricultural Water Management, Elsevier, vol. 241(C).
    3. Martins, Guilherme Santos & Giesbrecht, Mateus, 2021. "Clearness index forecasting: A comparative study between a stochastic realization method and a machine learning algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 787-805.
    4. Sun, Yuan & Zhang, Jing & Wang, Hongyuan & Wang, Ligang & Li, Hu, 2019. "Identifying optimal water and nitrogen inputs for high efficiency and low environment impacts of a greenhouse summer cucumber with a model method," Agricultural Water Management, Elsevier, vol. 212(C), pages 23-34.
    5. Sánchez-Molina, J.A. & Rodríguez, F. & Guzmán, J.L. & Ramírez-Arias, J.A., 2015. "Water content virtual sensor for tomatoes in coconut coir substrate for irrigation control design," Agricultural Water Management, Elsevier, vol. 151(C), pages 114-125.
    6. Firozjaei, Mohammad Karimi & Nematollahi, Omid & Mijani, Naeim & Shorabeh, Saman Nadizadeh & Firozjaei, Hamzeh Karimi & Toomanian, Ara, 2019. "An integrated GIS-based Ordered Weighted Averaging analysis for solar energy evaluation in Iran: Current conditions and future planning," Renewable Energy, Elsevier, vol. 136(C), pages 1130-1146.
    7. Mehreen Gul & Yash Kotak & Tariq Muneer & Stoyanka Ivanova, 2018. "Enhancement of Albedo for Solar Energy Gain with Particular Emphasis on Overcast Skies," Energies, MDPI, vol. 11(11), pages 1-17, October.
    8. Martins, Guilherme Santos & Giesbrecht, Mateus, 2023. "Hybrid approaches based on Singular Spectrum Analysis and k- Nearest Neighbors for clearness index forecasting," Renewable Energy, Elsevier, vol. 219(P1).
    9. Pérez-Castro, A. & Sánchez-Molina, J.A. & Castilla, M. & Sánchez-Moreno, J. & Moreno-Úbeda, J.C. & Magán, J.J., 2017. "cFertigUAL: A fertigation management app for greenhouse vegetable crops," Agricultural Water Management, Elsevier, vol. 183(C), pages 186-193.
    10. Blok, Chris & Voogt, Wim & Barbagli, Tommaso, 2023. "Reducing nutrient imbalance in recirculating drainage solution of stone wool grown tomato," Agricultural Water Management, Elsevier, vol. 285(C).
    11. Ibrahim, Thamir k. & Mohammed, Mohammed Kamil & Awad, Omar I. & Rahman, M.M. & Najafi, G. & Basrawi, Firdaus & Abd Alla, Ahmed N. & Mamat, Rizalman, 2017. "The optimum performance of the combined cycle power plant: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 459-474.
    12. Puccinelli, Martina & Carmassi, Giulia & Pardossi, Alberto & Incrocci, Luca, 2023. "Wild edible plant species grown hydroponically with crop drainage water in a Mediterranean climate: Crop yield, leaf quality, and use of water and nutrients," Agricultural Water Management, Elsevier, vol. 282(C).
    13. Massa, D. & Incrocci, L. & Maggini, R. & Carmassi, G. & Campiotti, C.A. & Pardossi, A., 2010. "Strategies to decrease water drainage and nitrate emission from soilless cultures of greenhouse tomato," Agricultural Water Management, Elsevier, vol. 97(7), pages 971-980, July.
    14. Cedeño, J. & Magán, J.J. & Thompson, R.B. & Fernández, M.D. & Gallardo, M., 2023. "Reducing nutrient loss in drainage from tomato grown in free-draining substrate in greenhouses using dynamic nutrient management," Agricultural Water Management, Elsevier, vol. 287(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:183:y:2017:i:c:p:107-115. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.