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Quantization of moisture content in yerba mate leaves through image processing

Leiva, Lucas et al · SEDICI UNLP · 2012

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Author / responsible party
Leiva, Lucas et al
Publisher
SEDICI UNLP
Year
2012
Language
English

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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)

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APA 7

Leiva, L. E. A. (2012). Quantization of moisture content in yerba mate leaves through image processing. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/23812

MLA

Leiva, Lucas et al. Quantization of moisture content in yerba mate leaves through image processing. SEDICI UNLP, 2012. http://sedici.unlp.edu.ar/handle/10915/23812.

Chicago

Leiva, Lucas et al. 2012. Quantization of moisture content in yerba mate leaves through image processing. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/23812.

Harvard

Leiva, L. E. A. 2012, Quantization of moisture content in yerba mate leaves through image processing, SEDICI UNLP, available at: http://sedici.unlp.edu.ar/handle/10915/23812 [Accessed 7 Aug. 2026].

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Title
Quantization of moisture content in yerba mate leaves through image processing
Author / contributors
Leiva, Lucas et al
Publisher
SEDICI UNLP
Publication year
2012
Language
English

Subjects

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