Texture features extraction technology using grey level co-occurrence matrix for the k-nearest neighbor classification of citrus disease: an agro-economic analysis

Economic Annals-XXI: Volume 197, Issue (5-6), Pages: 37-44

Citation information:
Kaswidjanti, W., Himawan, H., & Putri, G. W. (2022). Texture features extraction technology using grey level co-occurrence matrix for the k-nearest neighbor classification of citrus disease: an agro-economic analysis. Economic Annals-XXI, 197(5-6), 37-44. doi: https://doi.org/10.21003/ea.V197-06

Wilis Kaswidjanti
MA (Economics),
Informatics Department,
UPN Veteran Yogyakarta
Special Region of Yogyakarta, 55283, Indonesia (Corresponding author)
ORCID ID: https://orcid.org/0000-0002-5906-5525

Hidayatulah Himawan
MA (Economics),
Informatics Department,
UPN Veteran Yogyakarta
Special Region of Yogyakarta, 55283, Indonesia
ORCID ID: https://orcid.org/0000-0002-2506-4364

Galih Wangi Putri
MA (Economics),
Informatics Department,
UPN Veteran Yogyakarta
Special Region of Yogyakarta, 55283, Indonesia
ORCID ID: https://orcid.org/0000-0001-7255-5687

Texture features extraction technology using grey level co-occurrence matrix for the k-nearest neighbor classification of citrus disease: an agro-economic analysis

Abstract. The citrus disease is a problem affecting the decrease of agricultural commodity yields. One way to determine disease in citrus is through the leaves. Leaves, as a place for photosynthesis, with the disease will cause stunted plant growth. This study revolves around an Agro-economic Analysis to classify citrus diseases based on leaf images by applying the Gray Level Co-occurrence Matrix (GLCM) extraction technology using K-Nearest Neighbor (KNN). To meet that aim, Otsu Thresholding segmentation is carried out to separate the disease’s image from the healthy leaves. This experiment was carried out in Yogyakarta, Indonesia over the year 2020, and 345 leaves were collected and divided into three classes: canker, greening, and healthy. Citrus disease classification has four main stages, namely pre-processing, segmentation, feature extraction, and classification. Comparisons are made based on the normalization of the dataset and the KNN distance used. Given the results, dataset without normalization gets the best results with Hassanat distance KNN (k = 29) with an accuracy of 91.86%. A dataset with normalization receives the best results at Euclidean distance (k = 7) with an accuracy of 98.84%. These results affirm the efficiency of this the method in distinguishing diseases. As a result, this study can contribute to improving the quality of crops and reducing unnecessary expenses of pesticides, and finally could play a role in the economics of development.

Keywords: Gray Level Co-Occurrence Matrix; K-Nearest Neighbour; Classification; Extraction Technology; Agro-economic Analysis

JEL Classіfіcatіon: Q01; Q16; Q13; R11

Acknowledgements and Funding: The researchers are grateful to the various parties who have provided support until this research can be completed and UPN Veteran Yogyakarta.

Contribution: The authors contributed equally to this work.

Data Availability Statement: all data will be available upon request.

DOI: https://doi.org/10.21003/ea.V197-06


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Received 26.01.2022
Received in revised form 22.02.2022
Accepted 26.02.2022
Available online 22.06.2022