Research Article
Prediction of Pineapple Sweetness from Images Using Convolutional Neural Network
@ARTICLE{10.4108/eai.13-7-2018.165518, author={Adisak Sangsongfa and Nopadol Am-Dee and Payung Meesad}, title={Prediction of Pineapple Sweetness from Images Using Convolutional Neural Network}, journal={EAI Endorsed Transactions on Context-aware Systems and Applications}, volume={7}, number={21}, publisher={EAI}, journal_a={CASA}, year={2020}, month={7}, keywords={CNN, prediction, sweetness measurement}, doi={10.4108/eai.13-7-2018.165518} }- Adisak Sangsongfa
Nopadol Am-Dee
Payung Meesad
Year: 2020
Prediction of Pineapple Sweetness from Images Using Convolutional Neural Network
CASA
EAI
DOI: 10.4108/eai.13-7-2018.165518
Abstract
The objective of this research is to propose a deep learning based-prediction model for pineapple sweetness. In this research, we use a Convolutional Neural Network (CNN) to predict sweetness of pineapples from images. The dataset contains 4,860 pineapple images for training. Based on the CNN designed it is found that the best image size is 300 × 300 pixels resized to 30 × 30 pixels. The classification accuracy of training and testing are 72.38% and 78.50%, respectively. In addition, the root mean square error values for training and testing are 0.1362 and 0.1156, respectively. When developed as a mobile application, the accuracy of the application is 80.15%, the root mean square error value is 0.0156 and the reliability is 95.00%.
Copyright © 2020 Adisak Sangsongfa et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
