Early Detection and Continuous Quantization of Plant Disease Using Template Matching and Support Vector Machine Algorithms

Rong, Zhou; Kaneko, Shun’ichi; Tanaka, Fumio; Kayamori, Miyuki; Shimizu, Mitsugu

doi:10.1109/candar.2013.52

Cited by 23 publications

(8 citation statements)

References 7 publications

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“…A two-dimensional (2D) xy-color histogram feature was introduced in this study for training SVM classification model to segment the disease pixels from healthy and their background pixels. Support vector machine is superior than other classification techniques like k-Nearest neighbors, naive bayes etc..., [21].…”

Section: Fig 2 Rust Sporesmentioning

confidence: 99%

“…The two sets are one is the training data at interval of 2:00, 6:00, 10:00, 14:00, 18:00, 22:00, the other is test data at time of 0:00, 4:00, 8:00, 12:00, 16:00, 20:00. A desired classification of 96.52% and 99.47% is obtained for samples (training and testing) with their relative parameters setting are given as c= 4, = 2 [21]. 2) Suggestion of Pesticides for the Affected Disease: The pesticides for the affected disease in leaf are identified.…”

Section: Diagnosis and Treatment 1) Detection And Classification Omentioning

confidence: 99%

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Analysis of Infection in Plant Leaf and Implication of Suitable Pesticides based on Image Processing

Kavipriya¹

2020

IJRASET

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Agriculture plays a vital role in livelihood. Agriculture is the only way to feed ever growing populations. Several researchers are going on energetically in plant disease detection. Plant infection can affect the leaf which leads to huge loss in cultivation of crop and economical value of market. Mainly, in between the stages of sowing and harvesting. Plant infection are caused by various pests like virus, fungus and bacteria which causes infection to plants and results in loss of quantity and amount of production. Also, plant diseases increase due to pollution and other environmental changes. The plants should be supervised from its germinal stage of their life-cycle to evade such diseases. To monitor such type of infection manually is extremely difficult and also time consuming. It is necessary to devise a method to speed up this identification process. This technique is imperative, so it is decided to automate the disease detection system. Hence, image processing is used for the recognition of plant infection. Here, matching and segmentation algorithms are used for identifying the diseases. Classification algorithm is used for classifies the disease. Machine learning techniques are used for improving the diagnosis of plant infection. Identification of pesticides for those affected diseases is also suggested.

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Section: Fig 2 Rust Sporesmentioning

confidence: 99%

Section: Diagnosis and Treatment 1) Detection And Classification Omentioning

confidence: 99%

Analysis of Infection in Plant Leaf and Implication of Suitable Pesticides based on Image Processing

Kavipriya¹

2020

IJRASET

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“…Rong Zhou et al [10], discussed a technique for robust and early recognition of leaf spot in sugar beet. The algorithm uses hybrid techniques of template matching and support vector machine (SVM).…”

Section: Literature Surveymentioning

confidence: 99%

An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective

Dandawate

Kokare

2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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The major cause for decrease in the quality and amount of agricultural productivity is plant diseases. Farmers encounter great difficulties in detecting and controlling plant diseases. Thus, it is of great importance to diagnose the plant diseases at early stages so that appropriate and timely action can be taken by the farmers to avoid further losses.The project focuses on the approach based on image processing for detection of diseases of soybean plants. The soybean images are captured using mobile camera having resolution greater than 2 mega pixels. The purpose of the proposed project is to provide inputs for the Decision Support System (DSS), which is developed for providing advice to the farmers as and when require over mobile internet. Our proposed work classifies the images of soybean leaves as healthy and diseased using Support Vector Machine (SVM). The algorithm comprises of four major steps: image acquisition, extracting the leaf from complex background, statistical analysis and classification. The pre-processing step includes conversion from RGB to HSV (Hue Saturation Value) color space. For extracting the region of interest (ROI) from the original image, multithresholding is used. The color based and cluster based methods are used for segmentation. The algorithm uses Scale Invariant Feature Transform (SIFT) technique which automatically recognizes the plant species based on the leaf shape. The SVM classifier proves its ability in automatic and accurate classification of images. Finally, it can be concluded from the experimental results that this approach can classify the leaves with an average accuracy of 93.79%. The proposed system will enable the farmers to get advice from the agricultural experts with minimal efforts.

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“…Considering damage, those approaches employ an array of lesion segmentation and classification techniques such as thresholding [27,28], edge detection [29,30], watershed [31], fuzzy c-means [32], superpixel clustering [33], color transformation [17], pixel classification [22], improved histogram segmentation method [34], and genetic algorithms [14]. Popularly used classification techniques for plant lesion identification are K-means [35], K-nearest neighbor [36], Artificial Neural Networks [37,38], Support Vector Machine [39][40][41], and Deep Learning [42][43][44][45][46][47] as a new standard in digital image analysis. Due to the complexity and variation of lesion symptoms, and as the color of normal region and lesion region is also uneven and unclear [48], segmentation of lesions in an image is challenging.…”

Section: Introductionmentioning

confidence: 99%

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

et al. 2020

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Background: Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results: We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions: Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.

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Early Detection and Continuous Quantization of Plant Disease Using Template Matching and Support Vector Machine Algorithms

Cited by 23 publications

References 7 publications

Analysis of Infection in Plant Leaf and Implication of Suitable Pesticides based on Image Processing

Analysis of Infection in Plant Leaf and Implication of Suitable Pesticides based on Image Processing

An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective

High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

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