Generalised Linear Cepstral Models for the Spectrum of a Time Series

Proietti, Tommaso; Luati, Alessandra

doi:10.5705/ss.202017.0322

Cited by 3 publications

(1 citation statement)

References 65 publications

(22 reference statements)

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“…Revill [21] used machine learning with Gaussian process regression to calibrate LAI UAV models using ground data. Barzin [22] used a machine learning approach to estimate the nitrogen (N) content of corn leaves based on UAV MS data and identified Gradient Lifter and Random Forest as the most suitable models for estimating leaf N. Proietti [23] introduced a generalized linear model for a time series that can integrate alternative spectral estimation methods within the same probability-based framework. Li [24] investigated an SVM method for field weed identification involving multiple features using spectral data from soil images.…”

Section: Introductionmentioning

confidence: 99%

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

Li,

Cheng,

Chen

et al. 2024

Remote Sensing

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Winter wheat is an important grain that plays a crucial role in agricultural production and ensuring food security. Its yield directly impacts the stability and security of the global food supply. The accurate monitoring of grain yield is imperative for precise agricultural management. This study aimed to enhance winter wheat yield predictions with UAV remote sensing and investigate its predictive capability across diverse environments. In this study, RGB and multispectral (MS) data were collected on 6 May 2020 and 10 May 2022 during the grain filling stage of winter wheat. Using the Pearson correlation coefficient method, we identified 34 MS features strongly correlated with yield. Additionally, we identified 24 texture features constructed from three bands of RGB images and a plant height feature, making a total of 59 features. We used seven machine learning algorithms (Cubist, Gaussian process (GP), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), K-Nearest Neighbors algorithm (KNN), Support Vector Machine (SVM), Random Forest (RF)) and applied recursive feature elimination (RFE) to nine feature types. These included single-sensor features, fused sensor features, single-year data, and fused year data. This process yielded diverse feature combinations, leading to the creation of seven distinct yield prediction models. These individual machine learning models were then amalgamated to formulate a Bayesian Model Averaging (BMA) model. The findings revealed that the Cubist model, based on the 2020 and 2022 dataset, achieved the highest R2 at 0.715. Notably, models incorporating both RGB and MS features outperformed those relying solely on either RGB or MS features. The BMA model surpassed individual machine learning models, exhibiting the highest accuracy (R2 = 0.725, RMSE = 0.814 t·ha−1, MSE = 0.663 t·ha−1). Additionally, models were developed using one year’s data for training and another year’s data for validation. Cubist and GLM stood out among the seven individual models, delivering strong predictive performance. The BMA model, combining these models, achieved the highest R2 of 0.673. This highlights the BMA model’s ability to generalize for multi-year data prediction.

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Section: Introductionmentioning

confidence: 99%