Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning

Javidan, Seyed Mohamad; Banakar, Ahmad; Vakilian, Keyvan Asefpour; Ampatzidis, Yiannis

doi:10.1016/j.atech.2022.100081

Cited by 109 publications

(38 citation statements)

References 52 publications

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“…The threshold value was considered in the frequency range of 180-130 pixels. RGB Binary space was detected to detect the damaged part of the cucumber leaf surface, which is shown in Figure (6).…”

Section: Results Of Segmentation and Feature Extractionmentioning

confidence: 99%

“…In [13], a method based on examining the histogram of the image layer and applying morphological filters, as well as examining the histogram of the images in the HSV color model to prevent the spread of internal measles in cucumber greenhouses with 90% accuracy was used. In [6] a novel algorithm for separate the disease area from healthy part in Kmeans clustering automatically proposed. In that study, the texture features of grape leaf diseases, i.e., black measles, black rot, and leaf blight were extracted and SVM classification method was used.…”

Section: Introductionmentioning

confidence: 99%

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Identify fungal diseases of cucumber (Powdery Mildew and Anthracnose) using image processing and artificial neural network approach

Hosseini

Mohammadzamani

Javidan

et al. 2023

Preprint

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Plant disease can cause reduce quality and quantity of agriculture crops. In some countries farmers spend considerable time to consult with plant protection, while the time is an important factor to controlling of disease. Due to the fact that Powdery Mildew and Anthracnose fungal diseases cause the most damage in cucumber greenhouses, in this study, by presenting a non-destructive method based on image processing technique and artificial neural network, these two types of fungal diseases have been diagnosed. The steps related to the implementation of the proposed method are divided into three parts: segmentation, separation of damaged parts from the leaf and classification of the disease type class. After color and texture features were extracted from cucumber leaf samples, a multilayer perceptron neural network with error post-diffusion learning algorithm was used to separate different classes of images. Network input is the average of the main color components (R, G, B) of the images and the output is zero as a healthy leaf, number one as Powdery Mildew and number two as Anthracnose. The structure of this network was 24-3-4-3, which uses the tansig transfer function for the hidden and output layer, and among the educational functions. So back propagation (BP) algorithm in neural network by using lovenerg marquart (LM) function training has been successfully to diagnosis and classifies plant diseases in 6 second with 99.95% accuracy.

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Section: Results Of Segmentation and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identify fungal diseases of cucumber (Powdery Mildew and Anthracnose) using image processing and artificial neural network approach

Hosseini

Mohammadzamani

Javidan

et al. 2023

Preprint

Self Cite

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“…The objects are classified by minimizing the sum of squares of the distance between the object and the corresponding cluster. The K ‐means clustering algorithm can be described by four steps (Javidan et al., 2023): (1) picking the center of the K th cluster either randomly or based on some heuristics; (2) assigning each pixel to a cluster that minimizes the distance between the pixel and the cluster center; (3) computing the cluster centers by averaging all the pixels in the cluster; and finally, (4) repeating steps 2 and 3 until convergence is obtained.…”

Section: Methodsmentioning

confidence: 99%

“…The objects are classified by minimizing the sum of squares of the distance between the object and the corresponding cluster. The K-means clustering algorithm can be described by four steps (Javidan et al, 2023):…”

Section: Symptom Area Segmentation Using K-means Clusteringmentioning

confidence: 99%

“…This should be done very carefully and preferably automatically. Infecting a plant with several diseases (or disorders) is another challenge in diagnosing and classifying the disease, especially if the symptoms of the diseases look similar (Abdulridha et al., 2022; Cruz et al., 2019; Javidan et al., 2023). For example, many symptoms of diseases in tomato plants are very similar in terms of color (brown and yellow spots in the disease and the degree of greenness as the healthy area of the leaf), texture (the degree of severity of the disease color, smoothness, roughness, regularity, or no regularity of the changes in the disease area, the fineness and coarseness of the image), and the disease shape (ellipse, elongated, or circular shape and the number of disease spots on the leaf surface and the extent of the disease spread on the leaf surface).…”

Section: Introductionmentioning

confidence: 99%

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Tomato leaf diseases classification using image processing and weighted ensemble learning

et al. 2023

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In ensemble methods, multiple base classifiers with different performances are used to increase classification accuracy. This study proposes a novel weighted majority voting ensemble approach to classify red green blue images to identify the bacterial spot, late blight, leaf mold, sectorial leaf spot, target spot, early blight, and healthy tomato leaves. Color, textural, and shape features of the images were extracted to be used as inputs for base classifiers, followed by selecting the effective features using the relief method to increase the prediction performance. Six machine learning methods, that is, support vector machine, decision tree, random forest, k‐nearest neighbors, naïve Bayes, and discriminative analysis, were used as base classifiers, which resulted in 89.81%, 79.81%, 91.53%, 85.91%, 44.42%, and 82.84% accuracies, respectively. Then, simple majority and weighted majority voting ensemble methods were utilized to improve disease classification, the accuracies of which were 93.49% and 95.58%, respectively. The performance of the proposed method was compared with that of two well‐known deep learning methods, namely GoogLeNet and AlexNet, which exerted poor results. The findings of this study showed that the proposed framework based on weighted majority voting outperformed the base machine learning models in classifying tomato diseases.

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Smart Farming and Precision Agriculture and Its Need in Today’s World

John,

Arul Leena Rose

2024

Signals and Communication Technology

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Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning

Cited by 109 publications

References 52 publications

Identify fungal diseases of cucumber (Powdery Mildew and Anthracnose) using image processing and artificial neural network approach

Identify fungal diseases of cucumber (Powdery Mildew and Anthracnose) using image processing and artificial neural network approach

Tomato leaf diseases classification using image processing and weighted ensemble learning

Smart Farming and Precision Agriculture and Its Need in Today’s World

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