Detection of Leaf Disease Using Principal Component Analysis and Linear Support Vector Machine

C, Heltin Genitha; Dhinesh, E; Jagan, A

doi:10.1109/icoac48765.2019.246866

Cited by 9 publications

(7 citation statements)

References 5 publications

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“…After obtaining the results of the convolution, then the feature magnitude will also be obtained using Eqns. (7) and (8).…”

Section: Feature Extraction Texture-basedmentioning

confidence: 99%

“…Research by Hlaing and Zaw [7] used feature extraction based on statistical texture and color. Furthermore, Genitha et al [8] research used principal component analysis (PCA) for the feature extraction process, to be further classified by SVM with a linear kernel. The SVM algorithm has a number of kernels that can be used, research by Mokhtar et al [9] conducted tests for a number of kernels, namely Cauchy, Invmult, and Laplacian.…”

Section: Introductionmentioning

confidence: 99%

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Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

Wiharto¹,

Nashrullah²,

Suryani³

et al. 2021

RIA

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The disease in tomato plants, especially on tomato leaves will have an impact on the quality and quantity of tomatoes produced. Handling disease on tomato leaves that must be done is to detect the type of disease as early as possible, then determine the treatment that must be done. Detection of its types of tomato plant diseases requires sufficient knowledge and experience. The problem is that many beginner farmers in growing tomatoes do not have much knowledge, so they have failed in growing tomatoes. Based on these cases, this study proposes a model for the early detection of disease in tomato leaves based on image processing. The research method used is divided into 5 stages, namely preprocessing, segmentation, feature extraction, classification, and performance evaluation. The feature extraction stage used is texture-based with Gabor filters and color-based filters. The final decision is determined by the Support Vector Machine (SVM) classification algorithm with the Radial Basis Function (RBF) kernel. The test results of the tomato leaf disease detection system produced an average performance parameter of 98.83% specificity, 90.37% sensitivity, 90.34% F1-score, 90.37% accuracy, and 94.60% area under the curve (AUC). Referring to the resulting of the AUC performance, the tomato leaf disease detection system is in the very good category.

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“…After obtaining the results of the convolution, then the feature magnitude will also be obtained using Eqns. (7) and (8).…”

Section: Feature Extraction Texture-basedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

Wiharto¹,

Nashrullah²,

Suryani³

et al. 2021

RIA

View full text Add to dashboard Cite

show abstract

“…Additionally, it has been used for picture segmentation, crop recognition, and automated agriculture technology, including the classification of crops and fruits ( Dhinesh et al., 2019 ). Models based on convolutional neural networks (CNNs) have gained popularity due to their improved accuracy in object detection ( Sangeetha et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…CNNs can save time on preprocessing because they automatically extract features from the input images. The ability to identify crop diseases has made great strides in recent years ( Dhinesh et al., 2019 ; Sangeetha et al., 2022 ). There are now two distinct kinds of CNN-based object detectors: those with a single detection stage and those with two.…”

Section: Introductionmentioning

confidence: 99%

Detection and identification of plant leaf diseases using YOLOv4

Aldakheel,

Zakariah,

Alabdalall

2024

Front. Plant Sci.

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Detecting plant leaf diseases accurately and promptly is essential for reducing economic consequences and maximizing crop yield. However, farmers’ dependence on conventional manual techniques presents a difficulty in accurately pinpointing particular diseases. This research investigates the utilization of the YOLOv4 algorithm for detecting and identifying plant leaf diseases. This study uses the comprehensive Plant Village Dataset, which includes over fifty thousand photos of healthy and diseased plant leaves from fourteen different species, to develop advanced disease prediction systems in agriculture. Data augmentation techniques including histogram equalization and horizontal flip were used to improve the dataset and strengthen the model’s resilience. A comprehensive assessment of the YOLOv4 algorithm was conducted, which involved comparing its performance with established target identification methods including Densenet, Alexanet, and neural networks. When YOLOv4 was used on the Plant Village dataset, it achieved an impressive accuracy of 99.99%. The evaluation criteria, including accuracy, precision, recall, and f1-score, consistently showed high performance with a value of 0.99, confirming the effectiveness of the proposed methodology. This study’s results demonstrate substantial advancements in plant disease detection and underscore the capabilities of YOLOv4 as a sophisticated tool for accurate disease prediction. These developments have significant significance for everyone involved in agriculture, researchers, and farmers, providing improved capacities for disease control and crop protection.

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“…The key problem in this scenario for the farmers is the accurate detection and classification of the disease because the visual observation technique of disease detection has proved to be ineffective, unreliable, costly, and time-consuming with low accuracy in disease detection. This causes a significant loss of the agricultural yield [9]. Usually, the disease appears first in the leaves before spreading to other parts of the plant, significantly affecting plant life, quality, and quantity of the yield [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection

AlArfaj,

Altamimi,

Aljrees

et al. 2023

IEEE Access

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Agricultural production is a cornerstone of national economies, and the prevalence of plant diseases poses a significant threat to crop yields. Timely disease detection is essential to mitigate these risks. However, manual plant observation methods are labor-intensive and time-consuming, necessitating a shift toward automated solutions. This study addresses the pressing problem of plant disease identification by leveraging advanced image processing techniques. This research begins with a comprehensive analysis of the pepper bell leaf disease dataset. Through a series of meticulously designed image processing steps, the dataset is normalized, enhancing its quality and consistency. Building upon this preprocessing, the UNET segmentation technique in conjunction with the InceptionV3 transfer learning model is employed. This novel approach yields exceptional results, with 99.48% accuracy, 99.97% precision, 99.99% recall, and 99.98% F1 scores. To objectively assess the significance of the proposed model, the performance is benchmarked against existing state-of-the-art models. The findings demonstrate the superiority of the proposed approach in the domain of plant disease identification. By automating the detection process, this research not only enhances efficiency but also enables early disease detection, thereby potentially contributing to the agricultural sector to identify crop disease and manage it efficiently. INDEX TERMS Pepper bell leaf disease; image processing; transfer learning; InceptionV3The global economy heavily relies on agriculture for it

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Detection of Leaf Disease Using Principal Component Analysis and Linear Support Vector Machine

Cited by 9 publications

References 5 publications

Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

Detection and identification of plant leaf diseases using YOLOv4

Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection

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