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Potential of multi-seasonal vegetation indices to predict rice yield from UAV multispectral observations

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Abstract

Rice stands as the paramount food crop worldwide, catering to more than half of the global populace as staple sustenance. Accurately and non-destructively predicting rice yield on a large scale assumes paramount importance for assessing rice growth, market planning and food security monitoring. Nonetheless, the pivotal factors that influence the final yield remain inadequately understood. In this study, we evaluated the variation patterns of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Ratio Vegetation Index, Red Edge Ratio Vegetation Index and Normalized Difference Red Edge during crucial growth stages of long, medium and short-grain rice cultivars (YX054, DF018 and LF203) from 2019 to 2021. We investigated the correlation between vegetation index (VI) combinations at different growth stages and rice yield for these three cultivars. To establish predictive models, we deployed multi-seasonal VIs from multi-year dataset and three regression algorithms: partial least squares regression (PLSR), random forest regression (RFR) and support vector regression (SVR). The outcomes evinced a lack of significant correlation between single-season VIs and rice yield. The PLSR algorithm was deemed optimal for YX054, while the RFR was adjudged most suitable for DF018 and LF203. Moreover, the triple-growth and quadruple-growth period VIs models evinced superior robustness compared to the penta-growth period VIs models for all three cultivars, attaining the highest R2 value of 0.86 and the lowest RMSE of 88.17 kg/ha. This paper underscores the criticality of multi-seasonal VIs in bolstering the performance of rice yield prediction.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This work was supported by the Heilongjiang Provincial Postdoctoral Science Foundation (LBH-Z22090); Heilongjiang Provincial key research and development program (2022ZX05B05); Heilongjiang Natural Science Foundation Research Team Project (TD2023E001). The authors would like to thank the technical support and useful discussions from Ruidong Wang, Chao Song, and Fangyu Guo from the planting season to the harvesting season during 2019 to 2022. Also, many thanks to lab technician Rui Guan, Longhui Niu, and Mengchen Cai for their support. We thank Jinwu Wang for the exciting discussions about results and the professional suggestions during the field experiment.

Funding

Funding was provided by Heilongjiang Provincial Postdoctoral Science Foundation (Grant No. LBH-Z22090), Heilongjiang Provincial key research and development program (Grant No. 2022ZX05B05), Heilongjiang Natural Science Foundation Research Team Project (Grant No. TD2023E001).

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  1. College of Engineering, Northeast Agricultural University, Harbin, 150030, China

    Xiaobo Sun & Panli Zhang

  2. College of Agriculture, Northeast Agricultural University, Harbin, 150030, China

    Zhenhua Wang

  3. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China

    Yijia-Wang

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Sun, X., Zhang, P., Wang, Z. et al. Potential of multi-seasonal vegetation indices to predict rice yield from UAV multispectral observations. Precision Agric (2024). https://doi.org/10.1007/s11119-023-10109-6

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