International Journal of Food Properties | |
A machine vision approach for classification the rice varieties using statistical features | |
Syed Furqan Qadri1  Salman Qadri2  Abdul- Razzaq2  Najia Saher3  Tanveer Aslam3  Nazir Ahmad3  Faisal Shahzad3  Muzammil Ur Rehman3  Syed Ali Nawaz3  | |
[1] Computer Vision Institute, College of Computer Science & Software Engineering, Shenzhen University Chin;Department of Computer Science, Muhammad Nawaz Shareef University of Agriculture Multan (Mns-uam), Multan Punjab, Pakista;Department of Information Technology, Islamia University of Bahawalpur, Bahawalpur Punjab, Pakista; | |
关键词: Rice grains; Image & classification process; Feature optimizing; Machine vision process; | |
DOI : 10.1080/10942912.2021.1986523 | |
来源: Taylor & Francis | |
【 摘 要 】
The main objective of this study was to assess the machine vision (MV) techniques to classify six Asian rice varieties commonly named as Kachi-Kainat, Kachi-Toota, Kainat-Pakki, Super-Basmati-Kachi, Super-Basmati-Pakki, and Super-Maryam-Kainat (A1, A2, A3, A4, A5, and A6), mainly cultivated in Pakistan, China, India, Bangladesh, and neighboring countries. The sample of each selected rice variety contained 1800 grains, giving a total of 10800 (1800 × 6) grain samples. A cell phone camera captured the actual field digital images dataset in an open climate. All the captured images were enhanced and converted into the standard 8-bit gray-scale format. Six radius-based non-overlapping regions of interest (ROI’s) were taken on each captured image inducing a total of 3600 (6 × 600) ROI’s image dataset. We have extracted Binary (B), Histogram (H), and Texture (T) features from each image. We converted these forty-three features for each image into 154800 (43 × 3600) feature vector (FV) space to discriminate rice varieties. After optimizing the FV, five MV classifiers, namely; LMT Tree (LMT-T), Meta Classifier via Regression (MCR), Meta Bagging (MB), Tree J48 (T-J48), and Meta Attribute Select Classifier (MAS-C), were deployed attaining the classification accuracies as 97.4%, 97.0%, 96.3%, 95.74%, and 95.2%, respectively. The maximum overall accuracy (MOA) observed was 97.4% by LMT-Tree.
【 授权许可】
CC BY
【 预 览 】
Files | Size | Format | View |
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RO202111263652049ZK.pdf | 6614KB | download |