Paper
14 August 2019 Identification of co-substrate composted with sewage sludge using convolutional neural networks
S. Kujawa, J. Mazurkiewicz, W. Mueller, Ł. Gierz, K. Przybył, D. Wojcieszak, M. Zaborowicz, K. Koszela, P. Boniecki
Author Affiliations +
Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117939 (2019) https://doi.org/10.1117/12.2539800
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
In this paper an attempt was made to build classification models, based on convolutional neural networks, for identification of co-substrate composted with sewage sludge. Due to the pilot character of the studies, they were limited to two co-substrates, i.e. maize straw and rapeseed straw. In total, 12 composting experiments were carried out, each half of them with the content of each of the adopted types of straw. As a result of experiments, 2304 images of composted material samples were obtained, and they bacame the input information for the neural networks. Classification models were developed using the Tensorflow environment, TFLearn library and Python programming language. In their structure, one convolutional layer with different number of convolutional filters and one pooling layer were used to extract image features, and also two fully-connected layers were adopted for classification purposes. The training of the network was carried out with the use of the Adam optimization algorithm. Finally, 4 convolutional neural networks were developed, and their classification error estimated for the test set ranged from 4.1 to 11.0%.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Kujawa, J. Mazurkiewicz, W. Mueller, Ł. Gierz, K. Przybył, D. Wojcieszak, M. Zaborowicz, K. Koszela, and P. Boniecki "Identification of co-substrate composted with sewage sludge using convolutional neural networks", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117939 (14 August 2019); https://doi.org/10.1117/12.2539800
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Convolutional neural networks

Image analysis

Image resolution

Life sciences

Neurons

Photography

RGB color model

Back to Top