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Neural image analysis in determining the content of dry matter in corn cob
Author(s): D. Wojcieszak; J. Przybył; M. Zaborowicz; K. Koszela; P. Boniecki; S. Kujawa; W. Mueller; Ł. Gierz; K. Przybył
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Paper Abstract

The aim of this research was investigate the possibility of using methods of computer image analysis and neural modeling for assess the amount of dry matter in the tested corn cobs. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in this material. Generated neural models may be the beginning of research into the use of neural image analysis assess the content of dry matter in individual corn fractions. The presented models: RBF 31:31-20-1:1 characterized by RMS test error 0.244136 and RBF 18:22-1-1:1 characterized by RMS test error 0.230206 may be more efficient for more learning data. PiAO software and STATISTICA software were used in this work.

Paper Details

Date Published: 14 August 2019
PDF: 6 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117941 (14 August 2019); doi: 10.1117/12.2539783
Show Author Affiliations
D. Wojcieszak, Poznan Univ. of Life Sciences (Poland)
J. Przybył, Poznan Univ. of Life Sciences (Poland)
M. Zaborowicz, Poznan Univ. of Life Sciences (Poland)
K. Koszela, Poznan Univ. of Life Sciences (Poland)
P. Boniecki, Poznan Univ. of Life Sciences (Poland)
S. Kujawa, Poznan Univ. of Life Sciences (Poland)
W. Mueller, Poznan Univ. of Life Sciences (Poland)
Ł. Gierz, Poznan Univ. of Life Sciences (Poland)
K. Przybył, Poznan Univ. of Life Sciences (Poland)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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