Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine
Joint Authors
Kiratiratanapruk, Kantip
Temniranrat, Pitchayagan
Sinthupinyo, Wasin
Prempree, Panintorn
Chaitavon, Kosom
Porntheeraphat, Supanit
Prasertsak, Anchalee
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-01
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
To increase productivity in agricultural production, speed, and accuracy is the key requirement for long-term economic growth, competitiveness, and sustainability.
Traditional manual paddy rice seed classification operations are costly and unreliable because human decisions in identifying objects and issues are inconsistent, subjective, and slow.
Machine vision technology provides an alternative for automated processes, which are nondestructive, cost-effective, fast, and accurate techniques.
In this work, we presented a study that utilized machine vision technology to classify 14 Oryza sativa rice varieties.
Each cultivar used over 3,500 seed samples, a total of close to 50,000 seeds.
There were three main processes, including preprocessing, feature extraction, and rice variety classification.
We started the first process using a seed orientation method that aligned the seed bodies in the same direction.
Next, a quality screening method was applied to detect unusual physical seed samples.
Their physical information including shape, color, and texture properties was extracted to be data representations for the classification.
Four methods (LR, LDA, k-NN, and SVM) of statistical machine learning techniques and five pretrained models (VGG16, VGG19, Xception, InceptionV3, and InceptionResNetV2) on deep learning techniques were applied for the classification performance comparison.
In our study, the rice dataset were classified in both subgroups and collective groups for studying ambiguous relationships among them.
The best accuracy was obtained from the SVM method at 90.61%, 82.71%, and 83.9% in subgroups 1 and 2 and the collective group, respectively, while the best accuracy on the deep learning techniques was at 95.15% from InceptionResNetV2 models.
In addition, we showed an improvement in the overall performance of the system in terms of data qualities involving seed orientation and quality screening.
Our study demonstrated a practical design of rice classification using machine vision technology.
American Psychological Association (APA)
Kiratiratanapruk, Kantip& Temniranrat, Pitchayagan& Sinthupinyo, Wasin& Prempree, Panintorn& Chaitavon, Kosom& Porntheeraphat, Supanit…[et al.]. 2020. Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine. Journal of Sensors،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1190513
Modern Language Association (MLA)
Kiratiratanapruk, Kantip…[et al.]. Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine. Journal of Sensors No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1190513
American Medical Association (AMA)
Kiratiratanapruk, Kantip& Temniranrat, Pitchayagan& Sinthupinyo, Wasin& Prempree, Panintorn& Chaitavon, Kosom& Porntheeraphat, Supanit…[et al.]. Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine. Journal of Sensors. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1190513
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1190513