TELKOMNIKA Telecommunication, Computing, Electronics and Control
An effective feature extraction method for rice leaf disease classification
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
An effective feature extraction method for rice leaf disease classification
An effective feature extraction method for rice leaf disease classification
Subject
Classification
Extreme gradient boosting
Image processing
Machine learning
Plant disease detection
Rice leaf diseases
XGBoost
Extreme gradient boosting
Image processing
Machine learning
Plant disease detection
Rice leaf diseases
XGBoost
Description
Our society is getting more and more technology dependent day by day.
Nevertheless, agriculture is imperative for our survival. Rice is one of the
primary food grains. It provides sustenance to almost fifty percent of the world
population and promotes huge amount of employments. Hence, proper
mitigation of rice plant diseases is of paramount importance. A model to detect
three rice leaf diseases, namely bacterial leaf blight, brown spot, and leaf smut
is proposed in this paper. Backgrounds of the images are removed by
saturation threshold while disease affected areas are segmented using hue
threshold. Distinctive features from color, shape, and texture domain are
extracted from affected areas. These features can robustly describe local and
global statistics of such images. Trying a couple of classification algorithms,
extreme gradient boosting decision tree ensemble is incorporated in this model
for its superior performance. Our model achieves 86.58% accuracy on rice leaf
diseases dataset from UCI, which is higher than previous works on the same
dataset. Class-wise accuracy of the model is also consistent among the classes.
Nevertheless, agriculture is imperative for our survival. Rice is one of the
primary food grains. It provides sustenance to almost fifty percent of the world
population and promotes huge amount of employments. Hence, proper
mitigation of rice plant diseases is of paramount importance. A model to detect
three rice leaf diseases, namely bacterial leaf blight, brown spot, and leaf smut
is proposed in this paper. Backgrounds of the images are removed by
saturation threshold while disease affected areas are segmented using hue
threshold. Distinctive features from color, shape, and texture domain are
extracted from affected areas. These features can robustly describe local and
global statistics of such images. Trying a couple of classification algorithms,
extreme gradient boosting decision tree ensemble is incorporated in this model
for its superior performance. Our model achieves 86.58% accuracy on rice leaf
diseases dataset from UCI, which is higher than previous works on the same
dataset. Class-wise accuracy of the model is also consistent among the classes.
Creator
Muhammad Anwarul Azim, Mohammad Khairul Islam, Md. Marufur Rahman, Farah Jahan
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Oct 7, 2020
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Muhammad Anwarul Azim, Mohammad Khairul Islam, Md. Marufur Rahman, Farah Jahan, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
An effective feature extraction method for rice leaf disease classification,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3692.
An effective feature extraction method for rice leaf disease classification,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3692.