Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture

•A new method is proposed for data augmentation for deep neural network use.•The method uses a trigonometric-Euclidian space to generate the new data points.•The new method is compared to the deep learning-based augmentation methods.•The new method retained the data's original distribution, gai...

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Vydáno v:	Computers and electronics in agriculture Ročník 206; s. 107646
Hlavní autoři:	Derraz, Radhwane, Muharam, Farrah Melissa, Jaafar, Noraini Ahmad, Yap, Ng Keng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.03.2023
Témata:	aboveground biomass agriculture Autoencoder Continuous data augmentation crop yield cultivars data collection Deep neural network Elaeis guineensis electronics Generative adversarial network labor Machine learning algorithm phenology prediction probability distribution rice Smart farming time series analysis variance Smart farming Deep neural network Autoencoder Continuous data augmentation Machine learning algorithm Generative adversarial network
ISSN:	0168-1699, 1872-7107
On-line přístup:	Získat plný text
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