Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture
Biomass estimation, fertilisation, and crop production reflect crop yield potential. The prediction of these variables allows the selection of crop cultivars with high yield potential. Deep neural networks (DNNs) can predict such crop variables. However, DNNs are data greedy algorithms that overfit/...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/108355/ https://www.sciencedirect.com/science/article/abs/pii/S0168169923000340?via%3Dihub |
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| Summary: | Biomass estimation, fertilisation, and crop production reflect crop yield potential. The prediction of these variables allows the selection of crop cultivars with high yield potential. Deep neural networks (DNNs) can predict such crop variables. However, DNNs are data greedy algorithms that overfit/underfit on small-size datasets. Additionally, the collection of big data is expensive and laborious. Therefore, providing synthetic big data is preferable. This study aims to: (i) develop a trigonometric-Euclidean-smoother interpolation (TESI) for continuous time-series and non-time-series data augmentation to prevent DNNs from under/overfitting; (ii) compare the TESI performance to the tabular variational autoencoder (TVAE) and the conditional tabular generative adversarial network (CTGAN); and (iii) compare the DNN performance before and after data augmentation. Two time-series datasets, oil palm production and rice. |
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