Open AccessMaize Plant Desease Identification (Zea Mays L. Saccharata) Using Image Processing and K-Nearest Neighbor (K-Nn)
Author(s) -
Prihastuti Harsan,
Arie Qur’ania,
Karina Damayanti
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.20.20581
Subject(s) - biology , blight , agronomy , ostrinia furnacalis , pest analysis , plant disease , zea mays , image processing , grasshopper , botany , lepidoptera genitalia , artificial intelligence , microbiology and biotechnology , computer science , image (mathematics) , ecology
Plant pests of maize are known to attack in all phases of corn plant growth (Zea mays L. saccharata), both vegetative and generative. Common pests found in maize are seed flies (Atherigona sp.), Stem borers (Ostrinia furnacalis), Boricoverpa armigera, leaf-eaters (Spodoptera litura). The process of identification of maize plant disease is done through laboratory analysis and direct observation. The time required to obtain the identification result is 4 (four) months. Plant pests will attack some parts of the plant, including leaves, stems and fruit. Early detection is usually done through leaves. Plant pests will attack the plant leaf area with certain characteristics. Digital image processing is the use of computer algorithms to perform image processing on digital images. Identification of maize plant disease can apply image processing techniques through the characteristics or symptoms of disease raised on the leaves. Characteristic of attacks by pests in maize plants can be detected through the colors and patterns that appear on the leaves. This research performs implementation of digital image processing method to identify disease in maize plant caused by pest. The disease is Hawar Leaf, Bulai (Downy Midew), Hama Grasshopper, Leaf Spot (Sourthern Leaf Blight). Through color and edge detection, the accuracy obtained is 91.7%.