IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v303y2024ics0378377424003949.html
   My bibliography  Save this article

Crop water stress detection based on UAV remote sensing systems

Author

Listed:
  • Dong, Hao
  • Dong, Jiahui
  • Sun, Shikun
  • Bai, Ting
  • Zhao, Dongmei
  • Yin, Yali
  • Shen, Xin
  • Wang, Yakun
  • Zhang, Zhitao
  • Wang, Yubao

Abstract

Agricultural water accounts for more than 70 % of the total global water usage, and the scarcity of global freshwater resources will largely limit global agricultural production. Precision irrigation is the key to improving water efficiency and achieving sustainable agriculture. Accurate and rapid access to crop water information is an essential prerequisite for precise irrigation decisions. Traditional moisture detection methods based on soil moisture and crop physiological parameters are faced with the problems of variable field conditions, low efficiency and lack of spatial information, which can be extremely limited in practical applications. By contrast, unmanned aerial vehicle (UAV) remote sensing has the advantages of low cost, small size, flexible data acquisition time, and easy acquisition of high-resolution image data. Therefore, UAV remote sensing has become an easy and efficient method for crop water information monitoring. This study systematically introduces the principles, methods and applications of crop water stress analysis using the UAV technology. First, the mechanism of crop water stress analysed by UAV is elaborated, focusing on the relationship between canopy temperature, evapotranspiration, sun-induced chlorophyll fluorescence (SIF) and crop water stress. Next, various UAV imaging technologies for crop water stress monitoring are presented, including optical sensing systems, red, green and blue (RGB) images, multi-spectral sensing systems, and hyper-spectral sensing systems. Subsequently, the application of machine learning algorithms in the field of UAV monitoring of crop water information is outlined, demonstrating their potential for data processing and analysis. Finally, new directions and challenges in UAV-based crop water information acquisition and processing are synthesised and discussed, with special emphasis on the prospects of data assimilation algorithms and non-stomatal restriction in monitoring crop water information in the future. This study provides a comprehensive comparison and assessment of the mechanisms, technologies and challenges of UAV-based crop water information monitoring, providing insights and references for researchers in related fields.

Suggested Citation

  • Dong, Hao & Dong, Jiahui & Sun, Shikun & Bai, Ting & Zhao, Dongmei & Yin, Yali & Shen, Xin & Wang, Yakun & Zhang, Zhitao & Wang, Yubao, 2024. "Crop water stress detection based on UAV remote sensing systems," Agricultural Water Management, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:agiwat:v:303:y:2024:i:c:s0378377424003949
    DOI: 10.1016/j.agwat.2024.109059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377424003949
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2024.109059?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. Tianle Yang & Weijun Zhang & Tong Zhou & Wei Wu & Tao Liu & Chengming Sun, 2021. "Plant phenomics & precision agriculture simulation of winter wheat growth by the assimilation of unmanned aerial vehicle imagery into the WOFOST model," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-9, October.
    2. Veysi, Shadman & Naseri, Abd Ali & Hamzeh, Saeid & Bartholomeus, Harm, 2017. "A satellite based crop water stress index for irrigation scheduling in sugarcane fields," Agricultural Water Management, Elsevier, vol. 189(C), pages 70-86.
    3. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    4. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).
    5. Mwinuka, Paul Reuben & Mbilinyi, Boniface P. & Mbungu, Winfred B. & Mourice, Sixbert K. & Mahoo, H.F. & Schmitter, Petra, 2021. "The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L)," Agricultural Water Management, Elsevier, vol. 245(C).
    6. Li, L. & Nielsen, D.C. & Yu, Q. & Ma, L. & Ahuja, L.R., 2010. "Evaluating the Crop Water Stress Index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain," Agricultural Water Management, Elsevier, vol. 97(8), pages 1146-1155, August.
    7. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2022. "Effects of image spatial resolution and statistical scale on water stress estimation performance of MGDEXG: A new crop water stress indicator derived from RGB images," Agricultural Water Management, Elsevier, vol. 264(C).
    8. Luan, Yajun & Xu, Junzeng & Lv, Yuping & Liu, Xiaoyin & Wang, Haiyu & Liu, Shimeng, 2021. "Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging," Agricultural Water Management, Elsevier, vol. 246(C).
    9. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Trout, Thomas J., 2016. "Estimating maize water stress by standard deviation of canopy temperature in thermal imagery," Agricultural Water Management, Elsevier, vol. 177(C), pages 400-409.
    10. Longfei Zhou & Xiaohe Gu & Shu Cheng & Guijun Yang & Meiyan Shu & Qian Sun, 2020. "Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data," Agriculture, MDPI, vol. 10(5), pages 1-14, May.
    11. Carpintero, E. & Mateos, L. & Andreu, A. & González-Dugo, M.P., 2020. "Effect of the differences in spectral response of Mediterranean tree canopies on the estimation of evapotranspiration using vegetation index-based crop coefficients," Agricultural Water Management, Elsevier, vol. 238(C).
    12. Morales-Santos, Angela & Nolz, Reinhard, 2023. "Assessment of canopy temperature-based water stress indices for irrigated and rainfed soybeans under subhumid conditions," Agricultural Water Management, Elsevier, vol. 279(C).
    13. Alessandro Matese & Salvatore Filippo Di Gennaro, 2018. "Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture," Agriculture, MDPI, vol. 8(7), pages 1-13, July.
    14. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).
    15. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    16. Ali, Shahzad & Xu, Yueyue & Jia, Qianmin & Ahmad, Irshad & Wei, Ting & Ren, Xiaolong & Zhang, Peng & Din, Ruixia & Cai, Tie & Jia, Zhikuan, 2018. "Cultivation techniques combined with deficit irrigation improves winter wheat photosynthetic characteristics, dry matter translocation and water use efficiency under simulated rainfall conditions," Agricultural Water Management, Elsevier, vol. 201(C), pages 207-218.
    17. Liu, E.K. & Mei, X.R. & Yan, C.R. & Gong, D.Z. & Zhang, Y.Q., 2016. "Effects of water stress on photosynthetic characteristics, dry matter translocation and WUE in two winter wheat genotypes," Agricultural Water Management, Elsevier, vol. 167(C), pages 75-85.
    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. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).
    2. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    3. 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).
    4. 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).
    5. Li, Yangyang & Liu, Ningning & Fan, Hua & Su, Jixia & Fei, Cong & Wang, Kaiyong & Ma, Fuyu & Kisekka, Isaya, 2019. "Effects of deficit irrigation on photosynthesis, photosynthate allocation, and water use efficiency of sugar beet," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    6. Bounajra, Afaf & Guemmat, Kamal El & Mansouri, Khalifa & Akef, Fatiha, 2024. "Towards efficient irrigation management at field scale using new technologies: A systematic literature review," Agricultural Water Management, Elsevier, vol. 295(C).
    7. Jingwei Wang & Wenquan Niu & Yuan Li, 2020. "Effects of Drip Irrigation with Plastic on Photosynthetic Characteristics and Biomass Distribution of Muskmelon," Agriculture, MDPI, vol. 10(3), pages 1-15, March.
    8. Salgado, Ramiro & Mateos, Luciano, 2021. "Evaluation of different methods of estimating ET for the performance assessment of irrigation schemes," Agricultural Water Management, Elsevier, vol. 243(C).
    9. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    10. 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.
    11. de Almeida, Ailson Maciel & Coelho, Rubens Duarte & da Silva Barros, Timóteo Herculino & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto & Moreno-Pizani, Maria Alejandra & Farias-Ram, 2022. "Water productivity and canopy thermal response of pearl millet subjected to different irrigation levels," Agricultural Water Management, Elsevier, vol. 272(C).
    12. Fang, Heng & Liu, Fulai & Gu, Xiaobo & Chen, Pengpeng & Li, Yupeng & Li, Yuannong, 2022. "The effect of source–sink on yield and water use of winter wheat under ridge-furrow with film mulching and nitrogen fertilization," Agricultural Water Management, Elsevier, vol. 267(C).
    13. Zhuang, Tingxuan & Ata-UI-Karim, Syed Tahir & Zhao, Ben & Liu, Xiaojun & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Cao, Qiang, 2024. "Investigating the impacts of different degrees of deficit irrigation and nitrogen interactions on assimilate translocation, yield, and resource use efficiencies in winter wheat," Agricultural Water Management, Elsevier, vol. 304(C).
    14. Zhao, Junfang & Peng, Huiwen & Yang, Jiaqi & Huang, Ruixi & Huo, Zhiguo & Ma, Yuping, 2024. "Response of winter wheat to different drought levels based on Google Earth Engine in the Huang-Huai-Hai Region, China," Agricultural Water Management, Elsevier, vol. 292(C).
    15. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).
    16. Yan, Shicheng & Wu, You & Fan, Junliang & Zhang, Fucang & Guo, Jinjin & Zheng, Jing & Wu, Lifeng, 2022. "Optimization of drip irrigation and fertilization regimes to enhance winter wheat grain yield by improving post-anthesis dry matter accumulation and translocation in northwest China," Agricultural Water Management, Elsevier, vol. 271(C).
    17. 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.
    18. Pei Wang & Jingjing Ma & Juanjuan Ma & Haitao Sun & Qi Chen, 2021. "A Novel Approach for the Simulation of Reference Evapotranspiration and Its Partitioning," Agriculture, MDPI, vol. 11(5), pages 1-12, April.
    19. Gabriel Italo Novaes da Silva & Alexandre Maniçoba da Rosa Ferraz Jardim & Wagner Martins dos Santos & Alan Cézar Bezerra & Elisiane Alba & Marcos Vinícius da Silva & Jhon Lennon Bezerra da Silva & Lu, 2024. "Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    20. Christian Mera-Parra & Fernando Oñate-Valdivieso & Priscilla Massa-Sánchez & Pablo Ochoa-Cueva, 2021. "Establishment of the Baseline for the IWRM in the Ecuadorian Andean Basins: Land Use Change, Water Recharge, Meteorological Forecast and Hydrological Modeling," Land, MDPI, vol. 10(5), pages 1-18, May.

    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:303:y:2024:i:c:s0378377424003949. 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.