Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural-networks-based Classification

Bashish, Dheeb Al; Braik, Malik; Bani‐Ahmad, Sulieman

doi:10.3923/itj.2011.267.275

Cited by 236 publications

(77 citation statements)

References 8 publications

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“…Our program has been tested on five diseases which affect on the plants; they are: Early scorch, Cottony mold, Ashen mold, late scorch, and tiny whiteness. The proposed framework could successfully detect and classify the tested diseases with precision of more than 94% in average with more than 20% speedup over the presented approach in [1]. The minimum precision value was 84% compared to 80% precision in the previous approach.…”

Section: Introductionmentioning

confidence: 74%

“…The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm"s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1]. …”

mentioning

confidence: 98%

“…Comparatively, visually identifying plant diseases is expensive, inefficient, and difficult. Also, it requires the expertise of trained botanist [1].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase.…”

mentioning

confidence: 99%

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Fast and Accurate Detection and Classification of Plant Diseases

Hiary¹,

Ahmad²,

Reyalat³

et al. 2011

IJCA

413

184

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We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm"s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].

show abstract

Section: Introductionmentioning

confidence: 74%

mentioning

confidence: 98%

“…Comparatively, visually identifying plant diseases is expensive, inefficient, and difficult. Also, it requires the expertise of trained botanist [1].…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Fast and Accurate Detection and Classification of Plant Diseases

Hiary¹,

Ahmad²,

Reyalat³

et al. 2011

IJCA

413

184

View full text Add to dashboard Cite

show abstract

“…A speedy and precise method was designed which was based on kmeans based segmentation. This design also usedsneural network based classification technique for detecting and classifying the leaf disease [15]. However, an automatic classification system of leaf diseases was also designed for stereo and high resolution multispectral images of sugar beet leaves [16].…”

Section: Related Workmentioning

confidence: 99%

Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop

Ullagaddi¹,

Raju²

2017

IJMECS

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Abstract-Machine vision and soft computing techniques have been promising in the field of agriculture and horticulture to remove the barriers of conventional methods for detecting the plant diseases using different plant parts. Image segmentation technique is first and primary step in all the related researches such as fruit grading, leaf lesion region detection etc. In this paper, a robust technique for Mango crop using different plant parts such as Fruit, Flower and Leaf has been proposed in order to detect the disease more accurately. The captured real time images are pre-processed for illumination normalization and color space conversion before segmentation. The standard K-Means clustering scheme has been made adaptive and edge detection transforms have been applied to improve the segmentation results. Here, the objective function of K-Means clustering technique has been modified and cluster centers also have been updated to segment the diseased parts from images. The results obtained are better in the terms of both general human observation and in computational time.

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Leaf Disease Detection

2022

Data Science Handbook

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Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural-networks-based Classification

Cited by 236 publications

References 8 publications

Fast and Accurate Detection and Classification of Plant Diseases

Fast and Accurate Detection and Classification of Plant Diseases

Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop

Leaf Disease Detection

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