IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v239y2020ics0378377420306880.html
   My bibliography  Save this article

Neural computing modelling of the crop water stress index

Author

Listed:
  • Kumar, Navsal
  • Adeloye, Adebayo J.
  • Shankar, Vijay
  • Rustum, Rabee

Abstract

In this study, two artificial neural network models viz. supervised Feed-Forward Back Propagation (FF-BP) and unsupervised Kohonen Self-Organizing Map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature. Field experiments were conducted on Indian mustard to observe the crop canopy temperature under different levels of irrigation treatment during the 2017 and 2018 cropping seasons. The empirical CWSI was computed using well-watered and non-transpiring baseline canopy temperatures. The K-SOM and FF-BP CWSI predictions were compared with the empirical CWSI estimates and both performed satisfactorily. Of the two, however, the K-SOM was better with R2 (coefficient of determination) of 0.97 and 0.96 for model development and validation, respectively; corresponding values for FF-BP were 0.86 and 0.75. The results of the study suggest that neural network modelling offers significant potential for reliable prediction of the CWSI, which can be utilized in irrigation scheduling and crop stress management.

Suggested Citation

  • Kumar, Navsal & Adeloye, Adebayo J. & Shankar, Vijay & Rustum, Rabee, 2020. "Neural computing modelling of the crop water stress index," Agricultural Water Management, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:agiwat:v:239:y:2020:i:c:s0378377420306880
    DOI: 10.1016/j.agwat.2020.106259
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377420306880
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2020.106259?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. Gontia, N.K. & Tiwari, K.N., 2008. "Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry," Agricultural Water Management, Elsevier, vol. 95(10), pages 1144-1152, October.
    2. King, B.A. & Shellie, K.C., 2016. "Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index," Agricultural Water Management, Elsevier, vol. 167(C), pages 38-52.
    3. Yuan, Guofu & Luo, Yi & Sun, Xiaomin & Tang, Dengyin, 2004. "Evaluation of a crop water stress index for detecting water stress in winter wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 64(1), pages 29-40, January.
    4. Méndez-Barroso, Luis A. & Garatuza-Payán, Jaime & Vivoni, Enrique R., 2008. "Quantifying water stress on wheat using remote sensing in the Yaqui Valley, Sonora, Mexico," Agricultural Water Management, Elsevier, vol. 95(6), pages 725-736, June.
    5. Emekli, Yasar & Bastug, Ruhi & Buyuktas, Dursun & Emekli, Nefise Yasemin, 2007. "Evaluation of a crop water stress index for irrigation scheduling of bermudagrass," Agricultural Water Management, Elsevier, vol. 90(3), pages 205-212, June.
    6. Pou, Alícia & Diago, Maria P. & Medrano, Hipólito & Baluja, Javier & Tardaguila, Javier, 2014. "Validation of thermal indices for water status identification in grapevine," Agricultural Water Management, Elsevier, vol. 134(C), pages 60-72.
    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. Gu, Nan & Zhang, Jianyun & Wang, Guoqing & Liu, Cuishan & Wang, Zhenlong & Lü, Haishen, 2022. "An atmospheric and soil thermal-based wheat crop coefficient method using additive crop growth models," Agricultural Water Management, Elsevier, vol. 269(C).
    2. Melo, Leonardo Leite de & Melo, Verônica Gaspar Martins Leite de & Marques, Patrícia Angélica Alves & Frizzone, Jose Antônio & Coelho, Rubens Duarte & Romero, Roseli Aparecida Francelin & Barros, Timó, 2022. "Deep learning for identification of water deficits in sugarcane based on thermal images," Agricultural Water Management, Elsevier, vol. 272(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. Erdem, Yesim & Arin, Levent & Erdem, Tolga & Polat, Serdar & Deveci, Murat & Okursoy, Hakan & Gültas, Hüseyin T., 2010. "Crop water stress index for assessing irrigation scheduling of drip irrigated broccoli (Brassica oleracea L. var. italica)," Agricultural Water Management, Elsevier, vol. 98(1), pages 148-156, December.
    2. Candogan, Burak Nazmi & Sincik, Mehmet & Buyukcangaz, Hakan & Demirtas, Cigdem & Goksoy, Abdurrahim Tanju & Yazgan, Senih, 2013. "Yield, quality and crop water stress index relationships for deficit-irrigated soybean [Glycine max (L.) Merr.] in sub-humid climatic conditions," Agricultural Water Management, Elsevier, vol. 118(C), pages 113-121.
    3. O'Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D. & Howell, Terry A., 2012. "A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum," Agricultural Water Management, Elsevier, vol. 107(C), pages 122-132.
    4. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Gleason, Sean, 2018. "Comparison of three crop water stress index models with sap flow measurements in maize," Agricultural Water Management, Elsevier, vol. 203(C), pages 366-375.
    5. Levin, Alexander D., 2019. "Re-evaluating pressure chamber methods of water status determination in field-grown grapevine (Vitis spp.)," Agricultural Water Management, Elsevier, vol. 221(C), pages 422-429.
    6. Al-Kayssi, A.W. & Shihab, R.M. & Mustafa, S.H., 2011. "Impact of soil water stress on Nigellone oil content of black cumin seeds grown in calcareous-gypsifereous soils," Agricultural Water Management, Elsevier, vol. 100(1), pages 46-57.
    7. Katimbo, Abia & Rudnick, Daran R. & DeJonge, Kendall C. & Lo, Tsz Him & Qiao, Xin & Franz, Trenton E. & Nakabuye, Hope Njuki & Duan, Jiaming, 2022. "Crop water stress index computation approaches and their sensitivity to soil water dynamics," Agricultural Water Management, Elsevier, vol. 266(C).
    8. Krista C. Shellie & Bradley A. King, 2020. "Application of a Daily Crop Water Stress Index to Deficit Irrigate Malbec Grapevine under Semi-Arid Conditions," Agriculture, MDPI, vol. 10(11), pages 1-17, October.
    9. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    10. Zhang, Xiaoyu & Zhang, Xiying & Liu, Xiuwei & Shao, Liwei & Sun, Hongyong & Chen, Suying, 2015. "Incorporating root distribution factor to evaluate soil water status for winter wheat," Agricultural Water Management, Elsevier, vol. 153(C), pages 32-41.
    11. Ramírez-Cuesta, J.M. & Ortuño, M.F. & Gonzalez-Dugo, V. & Zarco-Tejada, P.J. & Parra, M. & Rubio-Asensio, J.S. & Intrigliolo, D.S., 2022. "Assessment of peach trees water status and leaf gas exchange using on-the-ground versus airborne-based thermal imagery," Agricultural Water Management, Elsevier, vol. 267(C).
    12. Widmoser, P., 2010. "An alternative to define canopy surface temperature bounds," Agricultural Water Management, Elsevier, vol. 97(2), pages 224-230, February.
    13. King, B.A. & Tarkalson, D.D. & Sharma, V. & Bjorneberg, D.L., 2021. "Thermal Crop Water Stress Index Base Line Temperatures for Sugarbeet in Arid Western U.S," Agricultural Water Management, Elsevier, vol. 243(C).
    14. Zhang, Liyuan & Zhang, Huihui & Zhu, Qingzhen & Niu, Yaxiao, 2023. "Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors, and variation of critical value," Agricultural Water Management, Elsevier, vol. 285(C).
    15. Roei Grimberg & Meir Teitel & Shay Ozer & Asher Levi & Avi Levy, 2022. "Estimation of Greenhouse Tomato Foliage Temperature Using DNN and ML Models," Agriculture, MDPI, vol. 12(7), pages 1-12, July.
    16. Pou, Alícia & Diago, Maria P. & Medrano, Hipólito & Baluja, Javier & Tardaguila, Javier, 2014. "Validation of thermal indices for water status identification in grapevine," Agricultural Water Management, Elsevier, vol. 134(C), pages 60-72.
    17. Shi, Jianchu & Wu, Xun & Wang, Xiaoyu & Zhang, Mo & Han, Le & Zhang, Wenjing & Liu, Wen & Zuo, Qiang & Wu, Xiaoguang & Zhang, Hongfei & Ben-Gal, Alon, 2020. "Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation," Agricultural Water Management, Elsevier, vol. 230(C).
    18. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2021. "The mean value of gaussian distribution of excess green index: A new crop water stress indicator," Agricultural Water Management, Elsevier, vol. 251(C).
    19. Fang, Q.X. & Ma, L. & Green, T.R. & Yu, Q. & Wang, T.D. & Ahuja, L.R., 2010. "Water resources and water use efficiency in the North China Plain: Current status and agronomic management options," Agricultural Water Management, Elsevier, vol. 97(8), pages 1102-1116, August.
    20. Ezenne, G.I. & Jupp, Louise & Mantel, S.K. & Tanner, J.L., 2019. "Current and potential capabilities of UAS for crop water productivity in precision agriculture," Agricultural Water Management, Elsevier, vol. 218(C), pages 158-164.

    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:239:y:2020:i:c:s0378377420306880. 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.