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DIPDEEP: Classification for Thai dragon fruit |
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| รหัสดีโอไอ | |
| Creator | 1. Naruwan Yusamran 2. Nualsawat Hiransakolwong |
| Title | DIPDEEP: Classification for Thai dragon fruit |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2565 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 49 |
| Journal No. | 4 |
| Page no. | 521-530 |
| Keyword | Dragon fruit, Classification, Digital image processing, Deep learning |
| URL Website | https://ph01.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | Thai dragon fruit is an interesting fruit with beautiful colors and high nutritional value, which can be used for food and pharmaceutical. In Thailand, there are 7 species of dragon fruits. Its skin can be divided into red or yellow groups, but inside can be divided into white, red or pink groups. If farmers know the species of dragon fruits, they can export fruits at a good price. Most people are unable to distinguish species of the dragon fruits. This paper is focus on classifying species of dragon fruits from images using digital image processing and deep learning called DIPDEEP. The DIPDEEP method has three steps; color space transformation, calculation ratio of the yellow and red color, classification using the deep learning method. First, the Thai dragon fruit images were classified into yellow (1 species) out from red (6 species). The Thai red dragon fruit was resized into 100x100 pixel resolutions in the pre-processing step only 6 species. Then, the red class was sent to classify again using the deep learning method. The experiments were processed in a dataset with 9,754 dragon fruit images on a black background (laboratory), and 10,072 images of dragon fruits at outdoor environment (outdoor). The results showed that accuracy of classification between the red and yellow dragon fruit for laboratory and outdoor datasets was 100% and 95.26%, respectively. The red dragon fruit is classified its species with accuracy 98.80%. The DIPDEEP has the smallest file size, and can save workload time because of separating yellow skin out at first step. |
