Based on historical weather data to predict summer field-scale maize yield: Assimilation of remote sensing data to WOFOST model by ensemble Kalman filter algorithm

This study delves into the Extended Kalman Filter's (EKF) use in ocean science through a detailed bibliometric and text mining examination. Tracing its roots back to the original Kalman Filter from the 1960s, the EKF has become crucial for managing nonlinear dynamics, especially in oceanography. Our analysis, drawing from Scopus data covering 1980-2023, delivers an extensive overview of the EKF's growth, applications, and cross-disciplinary influence in this area. We employed sophisticated bibliometric methods, including Biblioshiny, and text mining approaches via VOSviewer to dissect trends, and thematic groupings in EKF-related ocean science research. The results demonstrate a steady increase in EKF applications, particularly in autonomous underwater vehicle navigation, forecasting ocean currents, and modeling marine ecosystems. The bibliometric findings show its broad interdisciplinary appeal, while the text analysis underscores the EKF's integration with cutting-edge computational techniques and its significance in burgeoning oceanographic technologies. The paper highlights the EKF's indispensable role in ocean science, reflecting its historical importance and versatility in addressing contemporary challenges in marine technology. The study not only sheds light on the EKF's historical and current uses but also suggests potential future directions for research and innovation. It aims to offer crucial insights to researchers, academicians, and policy makers, underlining the EKF's significance in the dynamic, ever-changing realm of ocean science.

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Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China

Hu,

Ren,

Zhou

et al. 2024

Agriculture

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Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R2) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R2 (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management.

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Based on historical weather data to predict summer field-scale maize yield: Assimilation of remote sensing data to WOFOST model by ensemble Kalman filter algorithm

Cited by 4 publications

References 36 publications

In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling

In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling

Analysis of the extended Kalman filter's role in oceanic science

Oilseed Rape Yield Prediction from UAVs Using Vegetation Index and Machine Learning: A Case Study in East China

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