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Other research product . 2012

Quantization of moisture content in yerba mate leaves through image processing

Leiva, Lucas; Acosta, Nelson;
Open Access
Published: 01 Oct 2012
Country: Argentina
The Yerba Mate quality is defined by estimating the product moisture content. This value allows adjusting the production system, by controlling the stake of the dryer to ensure the product quality. Currently this process is done manually. However, this paper presents a first approach method to estimate the moisture contents of Yerba Mate leaves through image processing techniques. The output of the proposed system is established by a neural network MLPBP, which quantifies the level of moisture for a given sample. Also present the results of applying the proposed method to a set of 55 samples collected in a Yerba Mate production establishment.
Eje: Workshop Procesamiento de señales y sistemas de tiempo real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI)

Ciencias Informáticas, Neural nets, Signal processing, Real time, Yerba Mate moisture quantization, image processing, artificial neural networks