Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level
- Published
- Accepted
- Subject Areas
- Agricultural Science, Computational Biology, Food Science and Technology, Computational Science, Data Mining and Machine Learning
- Keywords
- Cape gooseberry, support vector machines, artificial neural networks, decision trees, K-nearest neighbors
- Copyright
- © 2018 Cotrina et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Using machine learning techniques and different color spaces for the classification of Cape gooseberry (Physalis peruviana L.) fruits according to ripeness level. PeerJ Preprints 6:e26691v1 https://doi.org/10.7287/peerj.preprints.26691v1
Abstract
The classification of fresh fruits according to their ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques such as those based on computer vision and machine learning. In this paper, we propose the use of non-intrusive techniques for the classification of Cape gooseberry fruits. The proposal is based on the use of machine learning techniques combined with different color spaces. Given the success of techniques such as artificial neural networks,support vector machines, decision trees, and K-nearest neighbors in addressing classification problems, we decided to use these approaches in this research work. A sample of 926 Cape gooseberry fruits was obtained, and fruits were classified manually according to their level of ripeness into seven different classes. Images of each fruit were acquired in the RGB format through a system developed for this purpose. These images were preprocessed, filtered and segmented until the fruits were identified. For each piece of fruit, the median color parameter values in the RGB space were obtained, and these results were subsequently transformed into the HSV and L*a*b* color spaces. The values of each piece of fruit in the three color spaces and their corresponding degrees of ripeness were arranged for use in the creation, testing, and comparison of the developed classification models. The classification of gooseberry fruits by ripening level was found to be sensitive to both the color space used and the classification technique, e.g., the models based on decision trees are the most accurate, and the models based on the L*a*b* color space obtain the best mean accuracy. However, the model that best classifies the cape gooseberry fruits based on ripeness level is that resulting from the combination of the SVM technique and the RGB color space.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Ripening levels of Cape gooseberry fruits
A dataset with information about color spaces (A) RGB, (B) SVM, and (C) L * a * b *, corresponding to seven ripening levels of Cape gooseberry fruits.