Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images

Li, Jinmin; Shi, Liangsheng; Mo, Xiaohu; Hu, Xiaolong; Su, Chenye; Han, Jingye; Deng, Xianzhi; Du, Shuai; Li, Shenji

doi:10.1016/j.compag.2024.109497

Computers and Electronics in Agriculture

2024

DOI: 10.1016/j.compag.2024.109497

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Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images

Jinmin Li,

Liangsheng Shi,

Xiaohu Mo

et al.

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Improving Simulations of Rice Growth and Nitrogen Dynamics by Assimilating Multivariable Observations into ORYZA2000 Model

Li,

Shi,

Han

et al. 2024

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The prediction of crop growth and nitrogen status is essential for agricultural development and food security under climate change scenarios. Crop models are powerful tools for simulating crop growth and their responses to environmental variables, but accurately capturing the dynamic changes in crop nitrogen remains a considerable challenge. Data assimilation can reduce uncertainties in crop models by integrating observations with model simulations. However, current data assimilation research is primarily focused on a limited number of observational variables, and insufficiently utilizes nitrogen observations. To address these challenges, this study developed a new multivariable data assimilation system, ORYZA-EnKF, that is capable of simultaneously integrating multivariable observations (including development stage, DVS; leaf area index, LAI; total aboveground dry matter, WAGT; and leaf nitrogen concentration, LNC). Then, the system was tested through three consecutive years of field experiments from 2021 to 2023. The results revealed that the ORYZA-EnKF model significantly improved the simulations of crop growth compared to the ORYZA2000 model. The relative root mean squared error (RRMSE) for LAI simulations decreased from 23–101% to 16–47% in the three-year experiment. Moreover, the incorporation of LNC observations enabled more accurate predictions of rice nitrogen dynamics, with RRMSE for LNC simulations reduced from 16–31% to 14–26%. And, the RRMSE decreased from 32–50% to 30–41% in the simulations of LNC under low-nitrogen conditions. The multivariable data assimilation system demonstrated its effectiveness in improving crop growth simulations and nitrogen status predictions, providing valuable insights for precision agriculture.

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Improving Simulations of Rice Growth and Nitrogen Dynamics by Assimilating Multivariable Observations into ORYZA2000 Model

Li,

Shi,

Han

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

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Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images

Cited by 1 publication

References 34 publications

Improving Simulations of Rice Growth and Nitrogen Dynamics by Assimilating Multivariable Observations into ORYZA2000 Model

Improving Simulations of Rice Growth and Nitrogen Dynamics by Assimilating Multivariable Observations into ORYZA2000 Model

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