Detection of oil palm leaf disease based on color histogram and supervised classifier

Hamdani, Hamdani; Septiarini, Anindita; Sunyoto, Andi; Suyanto, Suyanto; Utaminingrum, Fitri

doi:10.1016/j.ijleo.2021.167753

Cited by 49 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, previous studies have used various color spaces, such as HSV, YIQ, and YCbCr. While utilizing these color spaces, a conversion process based on the values in the RGB color space was required, which is defined in Equations ( 1) -( 7) [15]- [17]. The sample image was resized and converted to RGB to HSV, YIQ, and YCbCr color space is shown in Fig.…”

Section: Pre-processingmentioning

confidence: 99%

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Winarno

Hadikurniawati

Septiarini

et al. 2022

Int. J. Adv. Intell. Informatics

Self Cite

View full text Add to dashboard Cite

Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.

show abstract

Section: Pre-processingmentioning

confidence: 99%

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Winarno

Hadikurniawati

Septiarini

et al. 2022

Int. J. Adv. Intell. Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The average classification accuracy of 87.75 percent was reported when the number of hidden nodes was assigned as 6. The performances of six different classifiers, namely KNN, NB, C4.5, decision tree, ANN, and SVM, in identifying healthy and infected oil palm leaves were studied by Hamdani et al (2021). They concluded that applying the principal component analysis to extract the input features for ANN by splitting the histogram of RGB, L*a*b, HIS, and HSV color spaces into eight bins provided the highest accuracy.…”

Section: Related Work Plant Disease Detection Using Conventional Mach...mentioning

confidence: 99%

Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network

Ong

Woon

2022

JICT

View full text Add to dashboard Cite

Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the manual extraction of different discriminative features before the classification models can be developed. In this study, deep learning models, specifically, the AlexNet convolutional neural network (CNN) and the combination of AlexNet and support vector machine (AlexNet-SVM), which overcome the limitation of handcrafting of feature representation were implemented for oil palm leaf disease identification. The images of healthy and infected leaf samples were collected, resized, and renamed before the model training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in models, without the need for segmentation and feature extraction as in the conventional machine learning methods. The optimal architecture of AlexNet CNN and AlexNet-SVM models were then determined and subsequently applied for the oil palm leaf disease identification.Comparative studies showed that the overall performance of the AlexNet CNN model outperformed AlexNet-SVM-based classifier.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The classification method to recognize fruit or other objects commonly consists of three main processes: segmentation, feature extraction, and classification [5], [6]. Segmentation is the process of differentiating the object area and the background [5]. Previous studies on fruit segmentation have implemented various fruit objects, including apples [7], grapes [8], oranges [9], olives [10], oil palms [11], and tomatoes [12].…”

Section: Introductionmentioning

confidence: 99%

Chili Classification Using Shape and Color Features Based on Image Processing

Sihombing

Septiarini

Kridalaksana

et al. 2022

SJI

Self Cite

View full text Add to dashboard Cite

Abstract. Purpose: Chili is an agricultural product that has several varieties and is in great demand. It can be consumed directly or processed first. This study aims to classify the types of chili using color and shape features. The types of chili are divided into five classes: cayenne pepper, green chili, big green chili, big red chili, and curly chili. The chili classification method was evaluated using three parameters: precision, recall, and accuracy.Methods: This study applied the K-Nearest Neighbors (KNN) method with the Euclidean and Manhattan distance calculation algorithm and used two feature types: color and shape. The color features were extracted based on RGB color space by obtaining the mean and standard deviation values. Meanwhile, the shape features used aspect ratio, area, and boundary.Result: The evaluation results of the classification method were able to achieve the precision, recall, and accuracy values of 1.0, which means that all test data were classified correctly. The evaluation was applied with 210 training images and 90 test images based on the test results.Novelty: This study extracted two types of features: color and shape. Those features fed the KNN method by applying the Euclidean and Manhattan distance calculation algorithm; hence, the optimal results were achieved.

show abstract

Detection of oil palm leaf disease based on color histogram and supervised classifier

Cited by 49 publications

References 31 publications

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network

Chili Classification Using Shape and Color Features Based on Image Processing

Contact Info

Product

Resources

About