PDF(Pubmed)
Abstract：
UNASSIGNED: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process.
UNASSIGNED: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice.
UNASSIGNED: The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme.
UNASSIGNED: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.
UNASSIGNED: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice.
UNASSIGNED: The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme.
UNASSIGNED: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.
摘要：
■在稻田管理中使用化肥直接影响水稻产量。传统的水稻种植往往依靠农民的经验来制定施肥计划,不能根据水稻的肥料要求进行调整。目前,农用无人机被广泛用于水稻的早期监测，但是由于他们缺乏理性，它们不能直接指导受精。如何在分耕期准确施用氮肥以稳定水稻产量是当前水稻规模化生产过程中亟待解决的问题。
■WOFOST是一种高度机械的作物生长模型，可以有效地模拟施肥对水稻生长发育的影响。然而,由于其缺乏空间异质性，它在田间水平上模拟作物生长的能力较弱。本研究基于无人机遥感获取水稻冠层高光谱数据,利用WOFOST作物生长模型,研究水稻分耕期氮肥施用决策方法。利用连续投影算法提取水稻冠层高光谱特征,构建基于极限学习机的水稻生物量高光谱反演模型.通过使用两种数据同化方法，集成卡尔曼滤波与四维变分,对水稻生物量高光谱反演模型和局部WOFOST作物生长模型的反演生物量进行同化,并对WOFOST模型的仿真结果进行了修正。以平均产量为目标，利用WOFOST模型制定施肥决策，制作施肥处方图，实现水稻分耕阶段精准施肥。
■研究结果表明，水稻生物量高光谱反演模型的训练集R2和RMSE分别为0.953和0.076，而测试集R2和RMSE分别为0.914和0.110。当获得相同的产量时，基于ENKF同化方法的施肥策略，与标准施肥方案相比减少了5.9%。
■这项研究通过数据同化提高了无人机遥感机器的合理性，为水稻施肥决策提供了新的理论依据。
■WOFOST是一种高度机械的作物生长模型，可以有效地模拟施肥对水稻生长发育的影响。然而,由于其缺乏空间异质性，它在田间水平上模拟作物生长的能力较弱。本研究基于无人机遥感获取水稻冠层高光谱数据,利用WOFOST作物生长模型,研究水稻分耕期氮肥施用决策方法。利用连续投影算法提取水稻冠层高光谱特征,构建基于极限学习机的水稻生物量高光谱反演模型.通过使用两种数据同化方法，集成卡尔曼滤波与四维变分,对水稻生物量高光谱反演模型和局部WOFOST作物生长模型的反演生物量进行同化,并对WOFOST模型的仿真结果进行了修正。以平均产量为目标，利用WOFOST模型制定施肥决策，制作施肥处方图，实现水稻分耕阶段精准施肥。
■研究结果表明，水稻生物量高光谱反演模型的训练集R2和RMSE分别为0.953和0.076，而测试集R2和RMSE分别为0.914和0.110。当获得相同的产量时，基于ENKF同化方法的施肥策略，与标准施肥方案相比减少了5.9%。
■这项研究通过数据同化提高了无人机遥感机器的合理性，为水稻施肥决策提供了新的理论依据。
