Precision nitrogen management regimes to obtain high yield and good eating quality of medium indica hybrid rice in mechanical transplanting with bowl-type nursery tray (MTB) based on the critical nitrogen concentration

He, Haiyang; Yang, Kun; Xu, Haochong; Yao, Bo; Li, Ganghua; Zhang, Xuenan; Yang, Runjun; You, Chunping; Ke, Jian; Wu, Liquan

doi:10.1016/j.eja.2022.126711

Cited by 5 publications

(1 citation statement)

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“…The different coupled parameters at different growth stages may related to the differences in rice growth characteristics. Previous studies have demonstrated that the canopy structure is unstable and that leaf area and biomass are in a state of constant growth during the booting and flowering stages in rice plants [ 52 ]. Irrigation regimes directly regulate canopy growth and development [ 53 ].…”

Section: Discussionmentioning

confidence: 99%

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

Zhang,

Xu,

She

et al. 2024

Plant Methods

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Background Leaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants. Results The results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND(1287,1673) and crop water stress index (CWSI) (R2 = 0.86, RMSE = 0.01). Conclusions The machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.

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Section: Discussionmentioning

confidence: 99%