Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture
Open Access
- 8 August 2021
- journal article
- Published by Universitas Nusantara PGRI Kediri in INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
- Vol. 5 (2), 161-175
- https://doi.org/10.29407/intensif.v5i2.15287
Abstract
Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.Keywords
This publication has 8 references indexed in Scilit:
- Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal SignalKINETIK, 2020
- Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selectionComputers and Electronics in Agriculture, 2018
- SISTEM PAKAR DIAGNOSA PENYAKIT BUAH NAGA MENGGUNAKAN BACKWARD DAN FORWARD CHAININGCCIT Journal, 2017
- Klasifikasi penyakit noda pada citra daun tebu berdasarkan ciri tekstur dan warna menggunakan segmentation-based gray level co-occurrence matrix dan lab color momentsRegister: Jurnal Ilmiah Teknologi Sistem Informasi, 2017
- The distance function effect on k-nearest neighbor classification for medical datasetsSpringerPlus, 2016
- Grey Level Co-Occurrence Matrices: Generalisation and Some New FeaturesInternational Journal of Computer Science, Engineering and Information Technology, 2012
- Support Vector Machines for Pattern ClassificationPublished by Springer Science and Business Media LLC ,2010
- A study of efficiency and accuracy in the transformation from RGB to CIELAB color spaceIEEE Transactions on Image Processing, 1997