A Classification of Crown Flower using Feature Extraction and Machine Learning

Main Article Content

Sasin Tiendee
Kewalin Khamnipat
On-Uma Pramote

Abstract

This research presents an algorithm for image analysis dahlia classification that uses machine learning techniques to learn the distinctive features of dahlia images. The dahlia flowers used in this study were divided into four classes: (1) well-formed flowers, (2) fungal-infected flowers, (3) unequal petals, and (4) basal flowers. The dahlia flowers used in this research were divided into four classes: (1) flowers with good appearance, (2) flowers with fungal attachment, (3) flowers with unequal petal length, and (4) flowers at the base that are adjacent to each other. There are 200 flowers per class, a total of 800 flowers. The main features used in the research were three attributes: (1) percentage of dark pixels, (2) petal area (3) area between petals. Three techniques were used in this study: (1) Decision Trees, (2) Support Vector Machines, and (3) Deep Learning. In this study, 70% of the total data was used to create the classifier, and the remaining 30% was used for the classifier test. The classifier benchmarks with ten-fold cross-validation were precision 95.92, recall 95.90, accuracy 95.89, and f-measure 95.91. Test datasets were used to measure performance and found that decision trees were the most efficient learning machines. The results were precision 99.59 %, recall 99.58%, accuracy 99.58%, and f-measure rate 99.58%.

Article Details

How to Cite
[1]
S. Tiendee, K. Khamnipat, and O.-U. Pramote, “A Classification of Crown Flower using Feature Extraction and Machine Learning”, JIST, vol. 12, no. 2, pp. 1–10, Jul. 2022.
Section
Research Article: Soft Computing (Detail in Scope of Journal)

References

Nattavadee Hongboonmee and Praphasiri Trepanichkul, “Comparison of Data Classification Efficiency to Analyze Risk Factors that Affect the Occurrence of Hyperthyroidusing Data Mining Techniques”, Journal of Information Science and Technology, Vol. 9, No. 1, pp. 41-51, JAN-JUN 2019.

Arika Thammano1, Muthita Wangkid and Arit Thammano, “Breast Cancer Prediction Using K-mean Classification Algorithm with Self-adaptive Weight”, Journal of Information Science and Technology, Vol. 10, No. 2, pp. 1-9, JUL-DEC 2020.

Songgrod Phimphisan and Nattavut Sriwiboon, “Image Processing for Fundus Image Classification using Deep Learning”, Journal of Information Science and Technology, Vol. 10, No. 2, pp. 19-25, JUL-DEC 2020.

Nattavadee Hongboonmee and Nutthapong Jantawong, “Apply of Deep Learning Techniques to Measure the Sweetness Level of Watermelon via Smartphone”, Journal of Information Science and Technology, Vol. 10, No. 2, pp. 59-69, JUL-DEC 2020.

Sarawoot Boonkidram and Nattavut Sriwiboon, “Physical Quality Investigation of Germinated Brown Rice by using Image Processing”, Journal of Information Science and Technology, Vol. 10, No. 2, pp. 101-109, , JUL-DEC 2020.

Jittrapong Jaroenjit, Apirak Panpanasakul, Pollawat Chaisri, Peerapong Promduang and Sutida Prompongusawa, “Classification pearls using image processing,” presented at the 9 th Hatyai National and International Conference, Hatyai University, Songkhla, Thailand, Jul. 20, 1679-1691, 2018.

Nattavadee Hongboonmee and Kanin Pratoomthong, “The Analysis System of Counterfeit Banknote by Photo on Smartphone using Deep Learning Technique”, Journal of Information Science and Technology, Vol. 10, No. 2, pp. 90-100, JUL- DEC 2020.

Somying Thainimit, “Introduction to Image Processing,” in Digital Image Processing with MATLAB, Thailand: Kasetsart University, 2010. Accessed: Oct. 1, 2021. [Online]. Available: https://ebook.lib.ku.ac.th/ebook27/ebook/2014RG0087/index.html#p=1

Charturong Tantibundhit, “Introduction to Pattern Recognition,” in Pattern Recognition, Thailand: Thammasat Printing house, 2012.

Parinya Sanguansat, “Decision Tree,” in Artificial Intelligence with Machine Learning, Thailand: INFOPRESS, 2019.

Buncha Pasilatesung, “Support Vector Machines,” in Python Machine Learning, Thailand: SE-EDUCATION PUBLIC COMPANY LIMITED, 2021.

Tatpong Katanyukul, “Neural Network,” in Introduction to Machine Learning, Thailand: Faculty of Engineering Khon Kaen University, 2017.

Jakkarin Sanuksan and Olarik Surinta, “Deep Convolutional Neural Networks for Plant Recognition in the Natural Environment”, Journal of Science and Technology Mahasarakham University, Vol. 38, No. 2, pp. 113-124, MAR-APR 2019.

Rotsarin Tritanasombat and Tawin Tanawong, “Orchid Species Analyze System with Artificial Convolution Neural Network,” presented at the 9 th Asia Undergraduate Conference on Computing, Rajamangala University of Technology Rattanakosin, Prachuap Khiri Khan, Thailand, Feb. 25, 1337-1343, 2021.

Thanawat Poonyarit and Chutiphon Srisawat, “Peace Lily Images Classification with Visual Contents,” presented at the UTCC Academic Day Conference, University of the Thai Chamber of Commerce, Bangkok, Thailand, Jun. 7, 704-716, 2017.

Put Panuwanitchakorn and JanjiraPayakpate, “Using Image Segmentation Technique on the Image of Orchids Paphiopedilum Native Species of Thailand,” presented at the 10 th Mahasarakham University Research Conference, Mahasarakham University, Mahasarakham, Thailand, Sep. 11-12, 1679-1691, 2014.

Nopparut Pattansarn and Nattavut Sriwiboon, “Image Processing for Classifying the Quality of the Chok-Anan Mango by Simulating the Human Vision using Deep Learning”, Journal of Information Science and Technology, Vol. 10, No. 1, pp. 24-29, JAN-JUN 2020.

Hossin, M.1 and Sulaiman, M.N, “A Review on Evaluation Metrics for Data Classification Evaluations”, International Journal of Data Mining & Knowledge Management Process, Vol. 5, No. 2, pp. 1-11, , March 2015.