Analisis Morfometrik Daun Cabai Bergejala Kuning Keriting Menggunakan Pendekatan Pengolahan Citra Digital dan Algoritma Data Mining
Abstract
Morphometric Analysis of Chili Leaves with Yellow Curly Symptom Using Digital Image Processing Approach and Data Mining Algorithm
Yellow curling symptoms on chili leaves are generally caused by Begomovirus infection. The leaves of infected plants not only change color as an indicator of chlorophyll damage but also experience changes in morphological shape. This research aims to quantify the symptoms of Begomovirus infection based on morphological changes in leaf shape using digital image processing and data mining algorithms that will facilitate monitoring and analysis of plant disease development. A total of 33 images of cayenne pepper leaves with yellow curly symptoms and without symptoms became the dataset of this study. Using the Fiji-ImageJ application, the chili leaf images were processed and extracted in the shape characteristics, i.e., circularity, aspect ratio, roundness, and solidity. Furthermore, a t-test and image clustering using the Simple K-Means algorithm was conducted, followed by evaluation of the accuracy of the clustering results based on the ARI and NMI indexes. The results showed that, in general, there was a significant difference in shape between symptomatic and non-symptomatic leaves. The ratio and solidity value of leaves with yellow curly symptom was smaller than those of non-symptomatic chili leaves. In contrast, circularity and roundness value of symptomatic leaves was larger than those of non-symptomatic chili leaves. Evaluation of the accuracy of samples grouping for cayenne pepper leaves with and without symptoms based on the ARI and NMI indicated that grouping them into two groups gave the best value.
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