Pendeteksian Kerapatan dan Jenis Gulma dengan Metode Bayes dan Analisis Dimensi Fraktal untuk Pengendalian Gulma Secara Selektif - Mohamad Solahudin; Kudang Boro Seminar; I Wayan Astika; Agus Buono (Jurnal Keteknikan Pertanian, 2010)
Abstract Destructive impacts of herbicide usage on environment and water contamination have led to many researches oriented toward finding solutions for their accurate use. If density and weeds species could be correctly detected, patch spraying or spot spraying can effectively reduce herbicide usage. A precision automated machine vision for weed control could also reduce the usage of chemicals. Machine vision is a useful method for segmentation of different objects in agricultural applications, especially pattern recognition methods. Many indices have been investigated by researchers to perform weed segmentation based on color information of the images.  But there is no research that aims to identify weed diversity and its influence on the consumption of herbicides. The purpose of this research is to build a system that can recognize weeds and plants. In this study the relation between three main components (red, green and blue) of the images and color feature extraction (Hue, Saturation, Intensity) used to define weeds and plants density. Fractal dimension used as the methode to define  shape features to distinguish weeds and plants. Weeds and plants were segmented from background by obtaining H value and its shape was obtained by fractal dimension value. The results show fractal dimension value for weeds and plants has specific values. Corn plants have fractal dimension values in the range 1.148 to 1.268, peanut plants have fractal dimension values in the range 1.511 to 1.629, while the weeds have Fractal dimension values in the range 1.325 to 1.497. Keywords: image processing, machine vision, weed control, fractal dimension Diterima: 26 Juli 2010; Disetujui: 4 Oktober 2010