PENDETEKSIAN KERAPATAN DAN JENIS GULMA DENGAN METODE BAYES DAN ANALISIS DIMENSI FRAKTAL UNTUK PENGENDALIAN GULMA SECARA SELEKTIF
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.
Penulis: Mohamad Solahudin,
Kudang Boro Seminar, I Wayan Astika, Agus Buono
Kode Jurnal: jppertaniandd100231