The Implementation of K-Means Algorithm as Image Segmenting Method in Identifying the Citrus Leaves Disease

Febrinanto, Falih Gozi; Dewi, Candra; Triwiratno, Anang

doi:10.1088/1755-1315/243/1/012024

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…Their new algorithm gave an accuracy of 96% in predicting the number of food items on a plate. Febrinanto et al (2019) used the K-Means method to segment the disease in an image of a citrus leaf. After segmentation, they used the K-Nearest Neighbor (K-NN) algorithm to distinguish between diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm

Qumsiyeh¹,

Sabha²

2022

Preprint

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The agricultural sector in Palestine has a significant role in its economy. However, the production of this sector is affected by different kinds of plant diseases, specifically leaf diseases. Automatic agricultural leaf disease detection is essential for the early diagnosis and controls the overall health of fields. Image segmentation techniques, clustering, and deep learning are often used to detect diseased leaves. This study proposes a novel hybrid approach based on image classification. The hybrid approach combines the k-means clustering algorithm with Convolutional Neural Network (CNN), where k-means is used to detect the leaf’s infected area, then CNN is used for specifying the disease. We used the PlantVillage dataset for experimental verification as it contains several crops with different kinds of challenging diseases. We also examined the selection of optimal k-value using the Silhouette coefficient, Elbow method, and Kneedle Algorithm. The Silhouette technique was analyzed using three distance metrics; Euclidean, Manhattan, and Cosine. Its scores for the three-distance metrics were low, near-zero, and failed to produce the optimum k value. Besides, the Elbow method was complicated to use in image segmentation in terms of executing and visualizing the k value in its graph plot. Based on verification results, the Kneedle Algorithm produced better results in the consistency of choosing the optimal k value and showed superiority over other approaches. Therefore, the processed images were segmented with the k-means clustering algorithm with a Kneedle algorithm-based k value. Finally, a Convolutional Neural Network (CNN) is trained to classify the type of disease based on analyzing and testing leaf images. The hybrid model achieved high accuracy of 93.79% in disease identification, confirming the proposed model’s robustness.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm

Qumsiyeh¹,

Sabha²

2022

Preprint

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“…Infected shoots and through the insect vector Diaphorina citri [3,4]. Until the 1980s, orange plantations in Selayar Islands District were still declared CVPD-free, but in 1992 it was reported that citrus plants were suspected of being infected with CVPD [5] reported that there were 125 citrus trees in Batang Matasapo Village, Bontomatene District, and in Polebunging Village, Bontomarannu District that were attacked by CVPD, both mild and severe, but the transmitting insect, Diaphorina citri Kuway, had not been found.…”

Section: Introductionmentioning

confidence: 99%

Detection of citrus vein phloem degeneration disease (Candidatus Liberibacter asiaticum) in orange cv. Selayar, Citrus reticulata L.

Patandjengi,

Farham,

Kuswinanti

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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CVPD disease (Huanglongbing) is the most severe citrus disease caused by the bacterium Cand. Liberibacter asiaticus. This pathogen lives in the phloem tissue of citrus plants and is transmitted through the vector Diaphorina citri and by grafting. The study aimed to know the presence of CVPD disease in Orange cv Selayar nurseries and mother tree gardens in the Selayar Islands Regency. The PCR test was carried out in the research using the Laboratory of Agricultural Biotechnology, Hasanuddin University. The DNA of the citrus leaves was extracted using the Genomic DNA Kit method and the DNA CLas was amplified using the polymerase chain reaction (PCR) with the primer pairs OI1-O12c. The results showed that the percentage of mother plants of orange cv. Selayar infected with CVPD-like symptoms were 10-21 %. The percentage of citrus seedlings with CVPD-like symptoms in 2 nurseries were 0.27% and 0.09%. The symptoms of CVPD disease have been confirmed using a PCR technique, which was successful in amplifying the DNA fragment of CLa measuring 1160 bp, although not all symptoms suspected of CVPD are confirmed positive.

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“…Image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems. It has a wide range of applications such as biometrics [ 6 , 7 , 8 ], medical image analysis [ 9 , 10 , 11 ], disease detection and classification in cultures [ 12 , 13 , 14 , 15 , 16 ], traffic control systems [ 1 , 17 , 18 , 19 , 20 ], self driving cars [ 21 , 22 , 23 , 24 ], locating objects in satellite images [ 25 ], and content image retrieval systems [ 26 , 27 , 28 , 29 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Graphical Image Region Extraction with K-Means Clustering and Watershed

Jardim

António²,

Mora

2022

J. Imaging

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With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.

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The Implementation of K-Means Algorithm as Image Segmenting Method in Identifying the Citrus Leaves Disease

Cited by 12 publications

References 3 publications

Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm

Classification of Leaf Disease via Deep Neural Network combined with Clustering Algorithm

Detection of citrus vein phloem degeneration disease (Candidatus Liberibacter asiaticum) in orange cv. Selayar, Citrus reticulata L.

Graphical Image Region Extraction with K-Means Clustering and Watershed

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