Bulk Raisin Classification using Gray Level Co-occurrence Matrix

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KHOJASTEHNAZHAND MOSTAFA; RAMEZANI HAMED

Educational Videos and Scientific Workshops

Scientific Information Database

ISSN: 2588-4824

Journal Paper

Paper Information

Journal: Iranian Journal of Biosystems Engineering Year:2020 | Volume:50 | Issue:4 Page(s): 951-961

Download Full-Text

Persian Verion

View:

482

Download:

Cites:

Information Journal Paper

Title

Bulk Raisin Classification using Gray Level Co-occurrence Matrix

Author(s)

KHOJASTEHNAZHAND MOSTAFA | RAMEZANI HAMED | Issue Writer Certificate

Keywords

RaisinQ1

Support vector machineQ3

ClassificationQ1

Linear discrimination analysisQ2

Machine visionQ1

Abstract

Raisin is one of the most important agricultural products. In this study, by using the Machine vision approach, the quality of bulk Raisin was evaluated in two different conditions. In the first case, six classes of good and bad Raisins mixture, and in the latter case, 15 classes of good, bad and woody Raisins have been studied. Classification results with Linear Discriminate Analysis (LDA) and Support vector machine (SVM) showed that the best Classification accuracy of 6 classes was obtained by linear SVM method with an accuracy of 85. 55%. The results for classifying 15 classes including good, bad and wood showed that the best result was obtained by linear SVM method but with a lower accuracy of 63. 55%. The results showed that the GLCM method was able to detect the class of Raisin bulk product and could replace the expert in Raisin processing plants.

Cites

No record.

References

No record.

Cite

APA: Copy

KHOJASTEHNAZHAND, MOSTAFA, & RAMEZANI, HAMED. (2020). Bulk Raisin Classification using Gray Level Co-occurrence Matrix. IRANIAN JOURNAL OF BIOSYSTEMS ENGINEERING (IRANIAN JOURNAL OF AGRICULTURAL SCIENCES), 50(4 ), 951-961. SID. https://sid.ir/paper/144274/en

Vancouver: Copy

KHOJASTEHNAZHAND MOSTAFA, RAMEZANI HAMED. Bulk Raisin Classification using Gray Level Co-occurrence Matrix. IRANIAN JOURNAL OF BIOSYSTEMS ENGINEERING (IRANIAN JOURNAL OF AGRICULTURAL SCIENCES)[Internet]. 2020;50(4 ):951-961. Available from: https://sid.ir/paper/144274/en

IEEE: Copy

MOSTAFA KHOJASTEHNAZHAND, and HAMED RAMEZANI, “Bulk Raisin Classification using Gray Level Co-occurrence Matrix,” IRANIAN JOURNAL OF BIOSYSTEMS ENGINEERING (IRANIAN JOURNAL OF AGRICULTURAL SCIENCES), vol. 50, no. 4 , pp. 951–961, 2020, [Online]. Available: https://sid.ir/paper/144274/en

Related Journal Papers

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops

Download

Move to top

وبگردی