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A Novel Transfer Learning Strategy Using Soil Spectral Library with Cnn-Transformer Model

https://doi.org/10.2139/ssrn.5374765
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Does the limited use of orthogonal signal correction pre-treatment approach to improve the prediction accuracy of soil organic carbon need attention?
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Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data
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Non-linear memory-based learning for predicting soil properties using a regional vis-NIR spectral library
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Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates
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Hyperspectral Image Band Selection Based on CNN Embedded GA (CNNeGA)
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A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
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Ensemble Methods
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Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy
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Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution
inferredcites →https://doi.org/10.2139/ssrn.5004479openalex-snapshot
Hyperspectral Inversion of Soil Organic Matter Based on Improved Ensemble Learning Method
inferredcites →https://doi.org/10.3390/s18093169openalex-snapshot
Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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