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Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice

Author

Listed:
  • Wu, Yinshan
  • Jiang, Jie
  • Zhang, Xiufeng
  • Zhang, Jiayi
  • Cao, Qiang
  • Tian, Yongchao
  • Zhu, Yan
  • Cao, Weixing
  • Liu, Xiaojun

Abstract

Real-timely monitoring of the crop water status can improve irrigation scheduling to increase water saving and enhance agricultural sustainability, whereas the canopy temperature measured by thermal imaging is an essential indicator for determining the rice water status. The primary goal of this study was to propose a new temperature index based on the temporal variance of the daily temperature and to develop the rice water status estimation model. Two field experiments involving two rice varieties and multi-irrigation regimes were conducted from 2019 to 2020. A thermal imaging camera was used to measure the canopy temperature from 8:00–16:00 at 2-hour intervals across all growth stages. Three plant water parameters, namely plant water content (PWC), canopy water content (CWC), and canopy equivalent water thickness (CEWT), were collected simultaneously. The results showed that canopy temperature and plant water parameters differed obviously among different irrigation treatments. The relative canopy temperature velocity (RCTV) was developed based on the temporal variance of the daily temperature, and the RCTV8–12 performed well in distinguishing different irrigation treatments and quantifying the rice water status. The coefficient of determination (R2) values of the exponential relationships between the optimal RCTV and plant water parameters reached 0.47 (PWC), 0.39 (CWC) and 0.18 (CEWT). The random forest model, which integrates the multi-temperature indices, achieved a good estimation for PWC (R2 = 0.78), CWC (R2 = 0.77), and CEWT (R2 = 0.64) across all growth stages. In summary, combining the multi-temperature indices derived from the thermal infrared imagery and machine learning algorithm can facilitate the non-destructive estimation of the rice water status and improve the precision irrigation schedule.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:289:y:2023:i:c:s0378377423003864
    DOI: 10.1016/j.agwat.2023.108521
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    1. Wang, Jun & Huang, Guanhua & Li, Jiusheng & Zheng, Jianhua & Huang, Quanzhong & Liu, Haijun, 2017. "Effect of soil moisture-based furrow irrigation scheduling on melon (Cucumis melo L.) yield and quality in an arid region of Northwest China," Agricultural Water Management, Elsevier, vol. 179(C), pages 167-176.
    2. 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.
    3. Li, Doudou & Li, Ximeng & Xi, Benye & Hernandez-Santana, Virginia, 2022. "Evaluation of method to model stomatal conductance and its use to assess biomass increase in poplar trees," Agricultural Water Management, Elsevier, vol. 259(C).
    4. Gago, J. & Douthe, C. & Coopman, R.E. & Gallego, P.P. & Ribas-Carbo, M. & Flexas, J. & Escalona, J. & Medrano, H., 2015. "UAVs challenge to assess water stress for sustainable agriculture," Agricultural Water Management, Elsevier, vol. 153(C), pages 9-19.
    5. Padilla-Díaz, C.M. & Rodriguez-Dominguez, C.M. & Hernandez-Santana, V. & Perez-Martin, A. & Fernández, J.E., 2016. "Scheduling regulated deficit irrigation in a hedgerow olive orchard from leaf turgor pressure related measurements," Agricultural Water Management, Elsevier, vol. 164(P1), pages 28-37.
    6. 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).
    7. 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.
    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. Luo, Wanqi & Chen, Mengting & Kang, Yinhong & Li, Wenping & Li, Dan & Cui, Yuanlai & Khan, Shahbaz & Luo, Yufeng, 2022. "Analysis of crop water requirements and irrigation demands for rice: Implications for increasing effective rainfall," Agricultural Water Management, Elsevier, vol. 260(C).
    10. Elsayed, Salah & Elhoweity, Mohamed & Ibrahim, Hazem H. & Dewir, Yaser Hassan & Migdadi, Hussein M. & Schmidhalter, Urs, 2017. "Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 189(C), pages 98-110.
    11. Zhang, Huimeng & Xiong, Yunwu & Huang, Guanhua & Xu, Xu & Huang, Quanzhong, 2017. "Effects of water stress on processing tomatoes yield, quality and water use efficiency with plastic mulched drip irrigation in sandy soil of the Hetao Irrigation District," Agricultural Water Management, Elsevier, vol. 179(C), pages 205-214.
    12. Xu, Junzeng & Peng, Shizhang & Yang, Shihong & Wang, Weiguang, 2012. "Ammonia volatilization losses from a rice paddy with different irrigation and nitrogen managements," Agricultural Water Management, Elsevier, vol. 104(C), pages 184-192.
    13. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
    14. Krishna, Gopal & Sahoo, Rabi N. & Singh, Prafull & Bajpai, Vaishangi & Patra, Himesh & Kumar, Sudhir & Dandapani, Raju & Gupta, Vinod K. & Viswanathan, C. & Ahmad, Tauqueer & Sahoo, Prachi M., 2019. "Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing," Agricultural Water Management, Elsevier, vol. 213(C), pages 231-244.
    15. Nalley, Lawton Lanier & Massey, Joseph & Durand-Morat, Alvaro & Shew, Aaron & Parajuli, Ranjan & Tsiboe, Francis, 2022. "Comparative economic and environmental assessments of furrow- and flood-irrigated rice production systems," Agricultural Water Management, Elsevier, vol. 274(C).
    16. 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.
    17. Kullberg, Emily G. & DeJonge, Kendall C. & Chávez, José L., 2017. "Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients," Agricultural Water Management, Elsevier, vol. 179(C), pages 64-73.
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