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Author: search.filters.author.Hiransakolwong, NualsawatSubject: search.filters.subject.Dragon fruit

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    DIP-CBML: A New Classification of Thai Dragon Fruit Species from Images
    (2023-01-01)
    Yusamran, Naruwan
    ;
    Hiransakolwong, Nualsawat
    The attractiveness of dragon fruit is that it has a strange exterior, beautiful colors, and high nutritional value. In Thailand, there is both import and export of dragon fruit. Each package for export must contain only one species of dragon fruit. From the survey, there are seven species of dragon fruit cultivated in Thailand and only some farmers can identify them on his/her farm. Therefore, this research focuses on the classification of Thai dragon fruit from laboratory images and outdoor images; which is different from the previous works which studied only laboratory images. This method was named DIP-CBML that stands for digital image processing with content-based and machine learning. The method consists of image type identification, pre-processing, red and yellow classification, image background removal, and six classes of red species classification. From the results, DIP-CBML can work with both datasets. It gave 100%, 100% and 95.53% accuracy for the image type identification, red and yellow classification, and the classification of six red species respectively. Hopefully, this research will lead to the innovation for the pre-harvest classification of Thai dragon fruit cultivars, applied to industrial applications, and robot harvesting. In the future, may add value to the yield of Thai dragon fruit cultivation.
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    CBML: CLASSIFICATION – THAI RED DRAGON FRUIT
    (2022-11-01)
    Yusamran, Naruwan
    ;
    Hiransakolwong, Nualsawat
    A dragon fruit is nutritious having low sugar levels suitable for diabetics. Its shape is spherical with petals surrounding the bark. In Thailand, 7 species of dragon fruit are grown. Its appearance can be divided into red bark (6 species) or yellow bark (1 species). People can separate between yellow and red bark. Since the red-skinned dragon fruit may have its fruit pulp in one of three possible colors: white, red or pink, it is difficult to guess from the outside what color of the fruit pulp will be. The big problem is that people cannot classify its species of red bark. Some people may be allergic or dislike dragon fruit in some species. Therefore, this research is trying to find the best way to automatically classify the species of Thai red dragon fruit from its image. The CBML stands for content base and Machine Learning. This method uses content base to extract key features (34 attributes), and uses Machine Learning for giving optimization results with the Support Vector Machine (SVM), setting kernel for polynomial in 8 degree. The results showed that the CBML method was able to identify any species of dragon fruits in the red bark group with an accuracy of 98.47%.
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    DIPDEEP: Classification for Thai dragon fruit
    (2022-02-22)
    Yusamran, Naruwan
    ;
    Hiransakolwong, Nualsawat
    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.