Maize Leaf Disease Image Classification Using Bag of Features

Syarief, Mohammad; Prastiti, Novi; Setiawan, Wahyudi

doi:10.20895/infotel.v11i2.428

Cited by 10 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each type is divided into 4 image classes, namely healthy leaf images, cercospora, rust disease and leaf blight where each class consists of 50 images. The results of this study indicate an accuracy of 82% for RGB images, 77% for grayscale images and 85% for segmented images [6]. DOI:10.7753/IJCATR1102.1002 www.ijcat.com Other researchers investigated the various uses of features in the classification of maize plant diseases.…”

Section: Introductionmentioning

confidence: 85%

Implementation of CBIR Method for Identification of Corn Disease Using Extreme Learning Machine

Purnomo¹,

Mudjirahardjo²,

Pramono³

2022

IJCATR

View full text Add to dashboard Cite

Various artificial neural network methods and digital image processing techniques have been applied in agriculture. Previous researchers have proposed methods for identification or detection of plant diseases, such as using K-NN, support vector machine (SVM), backpropagation and so on. The disease attack can reduce the amount of agricultural productivity and result in other material losses for farmers. Plant diseases can be observed from the physical and color changes of the affected parts such as roots, stems and leaves. However, with the development of computer vision technology, observations can be assisted by image processing methods and artificial neural networks, for automation in agriculture. This study proposes a method of applying digital image processing with artificial neural networks to identify corn plant diseases. The purpose of this research is as a comparative study of the proposed method with other conventional methods. The methods used are content based image retrieval (CBIR) and extreme learning machine (ELM). Content based image retrieval (CBIR) is the process of searching for an image in a database by comparing the features in the query image with the features that have been stored in the image database. Meanwhile, extreme learning machine (ELM) is an artificial neural network with one hidden layer or single hidden layer feedforward neural network (SLFNs). The feature extraction used consists of 3 features, namely color, shape, and texture feature extraction. The dataset consists of 3 types of images, namely images of healthy leaves, leaf spot disease, and rust disease. The ELM artificial neural network serves as a classifier that classifies the types of diseases based on the given feature extraction. The proposed system is able to identify the type of disease based on the pattern that appears on the leaf surface with the highest precision of 0.9922.

show abstract

Section: Introductionmentioning

confidence: 85%

Implementation of CBIR Method for Identification of Corn Disease Using Extreme Learning Machine

Purnomo¹,

Mudjirahardjo²,

Pramono³

2022

IJCATR

View full text Add to dashboard Cite

show abstract

“…Type of features include color [4], [5], [23], shape [4], [5], [27] and texture such as Gray Level Co-Occurrence Matrix [4], [5], [23], [27] are commonly used. In addition, there are also more specific features such as Histogram of Gradients (HOG) [28], [30], [22], RELIEF-F [31], Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Features from Accelerated Segment Test (FAST) [30], [6].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Classification is a step for grouping features based on similarity or proximity. Various classical machine learning methods are used for classification such as Naïve Bayes [4], [30], [32], Decision Tree [4], [27], [30], [32], k-Nearest Neighbor [4], [33], Support Vector Machine with all its variants [4]- [6], [23], [25], [26], [29]- [35], Random Forest [4], [30], [32], Deep Forest [4], [36]. Neural Network [22], [24], [25], [32], and Bag of Features [6].…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. The classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. This article compare machine learning and deep learning for maize leaf disease classification. There are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. The number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles.

show abstract

“…The maize crop in Madura is widespread in all four districts namely Bangkalan, Sampang, Pamekasan and Sumenep with the proportion of maize use being dominated by food consumption, with maize generally grown on dry land but rainfed rice fields. Especially in Sumenep Regency, maize can be said to be a staple food for some people, especially for remote or rural communities [10] [13].…”

Section: A Maize Nutrient Deficiencymentioning

confidence: 99%

Performance of the K-Nearest Neighbors method on identification of maize plant nutrients

Khotimah

2022

J.INFOTEL

View full text Add to dashboard Cite

Maize is one kind of commodity consumption in domestic as well as export that has high economic value. However, the low productivity is caused by the main factor, namely the decreased level of soil fertility, so that it has the same effect on crop yields. These problems require the application of technology with the K-Nearest Neighbor (KNN) method. The method of study is based on 17 signs of nutrient deficiencies with Minkowski distance calculation process, calculation of deficiency of soil nutrients based on the value of K determined. The test results of the research use K = 75 to get an accuracy of 92.40. Comparative analysis of the K-nearest neighbor (K-NN) and NB methods by looking for the closeness between the criteria for new cases and old case criteria based on the criteria for the closest cases. The results showed that the K-Nearest Neighbor (K-NN) Algorithm had a better accuracy value than NB.

show abstract

Maize Leaf Disease Image Classification Using Bag of Features

Cited by 10 publications

References 15 publications

Implementation of CBIR Method for Identification of Corn Disease Using Extreme Learning Machine

Implementation of CBIR Method for Identification of Corn Disease Using Extreme Learning Machine

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Performance of the K-Nearest Neighbors method on identification of maize plant nutrients

Contact Info

Product

Resources

About