MATEC Web Conf.
Volume 255, 2019Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|Number of page(s)||5|
|Section||Smart Manufacturing and Industrial 4.0|
|Published online||16 January 2019|
Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling
1 Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia
2 CARIFF, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia
3 Faculty of Chemical & Natural Resources Engineering, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia
* Corresponding author: firstname.lastname@example.org
Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.