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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中文翻译：

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多季节植被指数通过无人机多光谱观测预测水稻产量的潜力

稻米是全世界最重要的粮食作物，为全球一半以上人口提供主食。大规模准确、无损地预测水稻产量对于评估水稻生长、市场规划和粮食安全监测至关重要。尽管如此，影响最终产量的关键因素仍然没有得到充分的了解。本研究评价了长粒、中粒、短粒水稻品种（YX054、YX054、 DF018和LF203）从2019年到2021年。我们研究了这三个品种不同生长阶段的植被指数（VI）组合与水稻产量的相关性。为了建立预测模型，我们部署了来自多年数据集的多季节 VI 和三种回归算法：偏最小二乘回归 (PLSR)、随机森林回归 (RFR) 和支持向量回归 (SVR)。结果表明单季 VI 与水稻产量之间缺乏显着相关性。 PLSR 算法被认为最适合 YX054，而 RFR 被认为最适合 DF018 和 LF203。此外，与所有三个品种的五生长期 VI 模型相比，三生长期和四生长期 VI 模型都表现出优异的鲁棒性，获得了最高的R ²值 0.86 和最低的 RMSE 88.17 kg/ha。本文强调了多季节 VI 在提高水稻产量预测性能方面的重要性。

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