Prediction of Pineapple Sweetness from Images Using Convolutional Neural Network

Sangsongfa, Adisak and Am-Dee, Nopadol and Meesad, Payung (2020) Prediction of Pineapple Sweetness from Images Using Convolutional Neural Network. EAI Endorsed Transactions on Context-aware Systems and Applications, 7 (21): e4. ISSN 2409-0026

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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%.

Item Type: Article
Uncontrolled Keywords: CNN, prediction, sweetness measurement
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 14 Sep 2020 11:10
Last Modified: 14 Sep 2020 11:10
URI: https://eprints.eudl.eu/id/eprint/266

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