Identification of Comulative Fruit Responses during Storage Using Neural Networks

https://doi.org/10.22146/agritech.13674

Wahyu Purwanto(1*)

(1) Jurusan Teknologi Industri Pertanian, Fakultas Teknologi Pertanian, Universtas Gadjah Mada, Yogyakarta
(*) Corresponding Author

Abstract


Neural networks are useful to identify complex nonlinear relationships between input and output of a system. Cumulative fruit responses such as water losses and ripening during storage are characterized non-linearly. For identification, several patterns of these cumulative responses, as affectef by environmental factors, are often conducted by repeating the experiment several times under different enviromental conditions. It is not well-known how many response patterns (training data sets) are necessary for an acceptable identifiaction. This research explores an affective way to identify the cumulative responses of tomato during storage using neural networks. Firstly, data for identification were obtained from a mathematical model. Secondly, the relationship between the number of response pattern and the estimation error were investigated. The estimated error becomes smaller when the number of response pattern is three or more. This suggests that three types of response patterns allow cumulative responses to be succesfully identified. Besides, an addition of linear data (1,2,..,N) as input variable significantly improves the identification accuracy of the cumulative response. Finally, the identification of actual was implemented based on these results and satisfactory results will be obtained.

Keywords


Storage process; dynamic system; cumulative fruit responses; identification; neural networks

Full Text:

PDF



DOI: https://doi.org/10.22146/agritech.13674

Article Metrics

Abstract views : 1175 | views : 594

Refbacks

  • There are currently no refbacks.




Copyright (c) 2016 Wahyu Purwanto

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

agriTECH has been Indexed by:


agriTECH (print ISSN 0216-0455; online ISSN 2527-3825) is published by Faculty of Agricultural Technology, Universitas Gadjah Mada in colaboration with Indonesian Association of Food Technologies.


website statisticsView My Stats