IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v310y2025ics0378377425000794.html
   My bibliography  Save this article

Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton

Author

Listed:
  • Jin, Kaijun
  • Zhang, Jihong
  • Liu, Ningning
  • Li, Miao
  • Ma, Zhanli
  • Wang, Zhenhua
  • Zhang, Jinzhu
  • Yin, Feihu

Abstract

Thermal imaging combined with deep learning algorithms offers an efficient and non-invasive method for monitoring crop water status, facilitating precise irrigation management over large agricultural areas. This study introduces a method for identifying the moisture state of cotton using an enhanced MobileVit deep learning algorithm. This approach incorporates the Efficient Channel Attention (ECA) mechanism into the Fusion component of the MobileVit model, optimizes the first convolution in the Fusion component by replacing it with Depthwise Separable Convolution (DsConv), and substitutes the Local representation with the MobileOne block. These enhancements aim to improve model performance while maintaining its compact size. A dataset of thermal images of cotton canopies representing three different water states was developed for this study. Ablation studies were performed to evaluate the effect of each modification. Grad-CAM was utilized to illustrate the final layer features of the proposed algorithm. Various deep learning models were also trained, tested, and validated, allowing for a comparative analysis of the proposed model against traditional deep learning models in identifying cotton moisture states. The results show that the F1-score of the proposed model reaches 0.9677, achieving a recognition speed of 50.370 ms while maintaining a size of 4.94 M, outperforming other classical deep learning models. The findings of this study provide technical support for the development of future precision irrigation systems. The relevant code and datasets will be made available on GitHub (https://github.com/kingcuzamu/identifying-cotton-water-state) upon publication.

Suggested Citation

  • Jin, Kaijun & Zhang, Jihong & Liu, Ningning & Li, Miao & Ma, Zhanli & Wang, Zhenhua & Zhang, Jinzhu & Yin, Feihu, 2025. "Improved MobileVit deep learning algorithm based on thermal images to identify the water state in cotton," Agricultural Water Management, Elsevier, vol. 310(C).
  • Handle: RePEc:eee:agiwat:v:310:y:2025:i:c:s0378377425000794
    DOI: 10.1016/j.agwat.2025.109365
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377425000794
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2025.109365?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Jin, Kaijun & Zhang, Jihong & Wang, Zhenhua & Zhang, Jinzhu & Liu, Ningning & Li, Miao & Ma, Zhanli, 2024. "Application of deep learning based on thermal images to identify the water stress in cotton under film-mulched drip irrigation," Agricultural Water Management, Elsevier, vol. 299(C).
    2. García-Tejero, I.F. & Rubio, A.E. & Viñuela, I. & Hernández, A & Gutiérrez-Gordillo, S & Rodríguez-Pleguezuelo, C.R. & Durán-Zuazo, V.H., 2018. "Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies," Agricultural Water Management, Elsevier, vol. 208(C), pages 176-186.
    3. Zong, Rui & Wang, Zhenhua & Zhang, Jinzhu & Li, Wenhao, 2021. "The response of photosynthetic capacity and yield of cotton to various mulching practices under drip irrigation in Northwest China," Agricultural Water Management, Elsevier, vol. 249(C).
    4. 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).
    5. J. S. Famiglietti, 2014. "The global groundwater crisis," Nature Climate Change, Nature, vol. 4(11), pages 945-948, November.
    Full references (including those not matched with items on IDEAS)

    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. Jin, Kaijun & Zhang, Jihong & Wang, Zhenhua & Zhang, Jinzhu & Liu, Ningning & Li, Miao & Ma, Zhanli, 2024. "Application of deep learning based on thermal images to identify the water stress in cotton under film-mulched drip irrigation," Agricultural Water Management, Elsevier, vol. 299(C).
    2. Jonathan O. Hernandez, 2022. "Ecophysiological Effects of Groundwater Drawdown on Phreatophytes: Research Trends during the Last Three Decades," Land, MDPI, vol. 11(11), pages 1-18, November.
    3. Zappa, Luca & Dari, Jacopo & Modanesi, Sara & Quast, Raphael & Brocca, Luca & De Lannoy, Gabrielle & Massari, Christian & Quintana-Seguí, Pere & Barella-Ortiz, Anais & Dorigo, Wouter, 2024. "Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture," Agricultural Water Management, Elsevier, vol. 295(C).
    4. Deng, Juntao & Pan, Shijia & Zhou, Mingu & Gao, Wen & Yan, Yuncai & Niu, Zijie & Han, Wenting, 2023. "Optimum sampling window size and vegetation index selection for low-altitude multispectral estimation of root soil moisture content for Xuxiang Kiwifruit," Agricultural Water Management, Elsevier, vol. 282(C).
    5. Xiukang Wang, 2022. "Managing Land Carrying Capacity: Key to Achieving Sustainable Production Systems for Food Security," Land, MDPI, vol. 11(4), pages 1-21, March.
    6. Madhumita Sahoo & Aman Kasot & Anirban Dhar & Amlanjyoti Kar, 2018. "On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1225-1244, March.
    7. Zhai, Yijie & Bai, Yueyang & Shen, Xiaoxu & Zhang, Tianzuo & Jia, Yuke & Ren, Ke & Zhou, Xinying & Cheng, Ziyue & Hong, Jinglan, 2023. "Provincial water availability footprint evaluation and transfer analysis of China’s grain products: A life cycle perspective," Agricultural Water Management, Elsevier, vol. 276(C).
    8. Zhiwen Song & Lei Zhao & Junguo Bi & Qingyun Tang & Guodong Wang & Yuxiang Li, 2024. "Classification of Degradable Mulch Films and Their Promotional Effects and Limitations on Agricultural Production," Agriculture, MDPI, vol. 14(8), pages 1-19, July.
    9. Schmitt, Rafael Jan Pablo & Rosa, Lorenzo, 2024. "Dams for hydropower and irrigation: Trends, challenges, and alternatives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    10. Anna Boser & Kelly Caylor & Ashley Larsen & Madeleine Pascolini-Campbell & John T. Reager & Tamma Carleton, 2024. "Field-scale crop water consumption estimates reveal potential water savings in California agriculture," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    11. Simon A. Schroeter & Alice May Orme & Katharina Lehmann & Robert Lehmann & Narendrakumar M. Chaudhari & Kirsten Küsel & He Wang & Anke Hildebrandt & Kai Uwe Totsche & Susan Trumbore & Gerd Gleixner, 2025. "Hydroclimatic extremes threaten groundwater quality and stability," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    12. Gao, Jia & Li, Lin & Ding, Risheng & Kang, Shaozhong & Du, Taisheng & Tong, Ling & Kang, Jian & Xu, Wanli & Tang, Guangmu, 2025. "Grain yield and water productivity of maize under deficit irrigation and salt stress: Evidences from field experiment and literatures," Agricultural Water Management, Elsevier, vol. 307(C).
    13. Xin Deng & Lingzhi Zhang & Rong Xu & Miao Zeng & Qiang He & Dingde Xu & Yanbin Qi, 2022. "Do Cooperatives Affect Groundwater Protection? Evidence from Rural China," Agriculture, MDPI, vol. 12(7), pages 1-14, July.
    14. Ellen M. Bruno & Richard J. Sexton, 2020. "The Gains from Agricultural Groundwater Trade and the Potential for Market Power: Theory and Application," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(3), pages 884-910, May.
    15. Peng Qi & Guangxin Zhang & Yi Jun Xu & Zhikun Xia & Ming Wang, 2019. "Response of Water Resources to Future Climate Change in a High-Latitude River Basin," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    16. Bagstad, Kenneth J. & Ancona, Zachary H. & Hass, Julie & Glynn, Pierre D. & Wentland, Scott & Vardon, Michael & Fay, John, 2020. "Integrating physical and economic data into experimental water accounts for the United States: Lessons and opportunities," Ecosystem Services, Elsevier, vol. 45(C).
    17. Wegmann, Johannes & Mußhoff, Oliver, 2019. "Groundwater management institutions in the face of rapid urbanization – Results of a framed field experiment in Bengaluru, India," Ecological Economics, Elsevier, vol. 166(C), pages 1-1.
    18. Mehrabi, Zia & Delzeit, Ruth & Ignaciuk, Adriana & Levers, Christian & Braich, Ginni & Bajaj, Kushank & Amo-Aidoo, Araba & Anderson, Weston & Balgah, Roland A. & Benton, Tim G. & Chari, Martin M. & El, 2022. "Research priorities for global food security under extreme events," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 5(7), pages 756-766.
    19. Christoph Görg & Ulrich Brand & Helmut Haberl & Diana Hummel & Thomas Jahn & Stefan Liehr, 2017. "Challenges for Social-Ecological Transformations: Contributions from Social and Political Ecology," Sustainability, MDPI, vol. 9(7), pages 1-21, June.
    20. Encarna Esteban & Elena Calvo & Jose Albiac, 2021. "Ecosystem Shifts: Implications for Groundwater Management," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 79(3), pages 483-510, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:310:y:2025:i:c:s0378377425000794. 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.