TELKOMNIKA Telecommunication, Computing, Electronics and Control
Machine learning-based approaches for tomato pest classification
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Machine learning-based approaches for tomato pest classification
Machine learning-based approaches for tomato pest classification
Subject
DT
GLCM
HOG
K-NN
LBP
SURF
SVM
GLCM
HOG
K-NN
LBP
SURF
SVM
Description
Insect pests are posing a significant threat to agricultural production. They live in different places like fruits, vegetables, flowers, and grains. It impacts
plant growth and causes damage to crop yields. We presented an automatic detection and classification of tomato pests using image processing with
machine learning-based approaches. In our work, we considered texture features of pest images extracted by feature extraction algorithms like gray
level co-occurrence matrix (GLCM), local binary pattern (LBP), histogram of oriented gradient (HOG), and speeded up robust features (SURF).
The three standard classification methods, including support vector machine (SVM), k-nearest neighbour (k-NN), and decision tree (DT) are used for classification operation. The three classifiers have undergone a comprehensive analysis to present which classifier with which feature yields
the best accuracy. The experiment results showed that the SVM classifier's precision using the feature extracted by local binary patterns (LBP) algorithm achieves the highest value of 81.02%. MATLAB software used for feature extraction and waikato environment for knowledge analysis (WEKA) graphical user interface for classification.
plant growth and causes damage to crop yields. We presented an automatic detection and classification of tomato pests using image processing with
machine learning-based approaches. In our work, we considered texture features of pest images extracted by feature extraction algorithms like gray
level co-occurrence matrix (GLCM), local binary pattern (LBP), histogram of oriented gradient (HOG), and speeded up robust features (SURF).
The three standard classification methods, including support vector machine (SVM), k-nearest neighbour (k-NN), and decision tree (DT) are used for classification operation. The three classifiers have undergone a comprehensive analysis to present which classifier with which feature yields
the best accuracy. The experiment results showed that the SVM classifier's precision using the feature extracted by local binary patterns (LBP) algorithm achieves the highest value of 81.02%. MATLAB software used for feature extraction and waikato environment for knowledge analysis (WEKA) graphical user interface for classification.
Creator
Gayatri Pattnaik, Kodimala Parvathi
Publisher
Universitas Ahmad Dahlan
Date
April 2022
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930,
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Gayatri Pattnaik, Kodimala Parvathi, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Machine learning-based approaches for tomato pest classification,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4917.
Machine learning-based approaches for tomato pest classification,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4917.