Color moment and gray level co-occurrence matrix in classification of soil organic matter for patchouli plantation

Abstract

<span>Patchouli is one of the essential plants that have the most potential and widely cultivated in Indonesia. Patchouli is greedily absorbing soil nutrients and organic matter. Therefore, the selection of soil with high organic matter will maximize the patchouli’s productivity. This paper aims to facilitate soil’s organic matter identification by classifying soil image based on the combination of color and texture features. The color feature extraction was done using the Color Moments method and the texture feature was done using Gray Level Co-occurrence Matrix (GLCM) method. The selection of features was performed to obtain the best combination of color and texture features. The selected features then was used as input of classification by using Modified K-Nearest Neighbor (MKNN). The samples of soil that used as data were taken from several districts in Blitar, East Java province. The testing result of this research showed the highest accuracy of 93,33% by using 180 training data, and also particular color and texture feature combination.</span>

Sub-Surface Soil Characterization Using Image Analysis: Material Recognition Using the Grey Level Co-Occurrence Matrix Applied to a Video-CPT-Cone

“…Already long-established image processing techniques, such as the grey level cooccurrence matrix (GLCM), are used to estimate textures, patterns and structure anomalies in a wide range of fields, from the medical field (identification cancer tissues [13,14], bone fractures [15]; etc.) to geography [16,17] and mining [18][19][20].…”