Spectral characteristics curves of rice plants infected by blast

Elkhatib, H. A.; Hawela, F.; Hamdi, H.; El-Mowelhi, N.M.

doi:10.1109/igarss.1993.322281

Cited by 2 publications

(5 citation statements)

References 2 publications

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“…So the rate of dimensionality reduction is higher for the proposed method compare to the existing methods, shown in Fig. (9). The method does not reduce dimension of data by losing its decision making capability, rather it provides compatible classification accuracy obtained by various classifiers when run using "weka" tool [49] where 10-fold cross-validations are carried out, as listed in Table 10.…”

Section: Resultsmentioning

confidence: 99%

“…degree values. Let the rank functions Rfin and Rfout are defined on the domain sets CLUSin-degree and CLUSoutdegree respectively to map each element of the cluster set to the index in which it belongs, as given below in equation ( 8) and (9).…”

Section: Functional Dependency Of Attributesmentioning

confidence: 99%

“…Panicles of sheaths affected before emergence rot, turn brown or reddish brown and fail to produce any grain. With the advancement of information technology, remote sensing techniques have been used in the field of crop management, described in [8][9][10]. A relation among the ground disease index and remote sensing data is established in [11] to classify the diseases.…”

Section: Introductionmentioning

confidence: 99%

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Feature Selection by Attribute Clustering of Infected Rice Plant Images

Phadikar¹,

Sil²,

Das³

2011

Int J Mach Intell

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Automatic diagnosis of rice plant diseases at an early stage and taking corrective measures in time saves damages of rice crop across the world. The paper aims at developing an appropriate methodology to classify diseases with the help of feature sets obtained by analyzing images of infected rice plants acquired from the field. Since all features are not important in classifying diseases; selection of optimum features is a challenging task to address the problem. The work is performed in three steps. Firstly thirty six features of different category are extracted from the diseased plant images using image processing techniques. Secondly information gain (IG) of each attribute with respect to other attributes is calculated following the concept of information entropy theory. Thirdly using IG, functional dependency of the attributes are evaluated based on which fourteen significant attributes out of thirty six are selected, sufficient to classify the diseases. The proposed method has been applied on four hundred fifty infected rice plant images having three different classes. With the reduced feature set, classification accuracy is calculated using different classifiers demonstrating effectiveness of the proposed model.

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Section: Resultsmentioning

confidence: 99%

Section: Functional Dependency Of Attributesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature Selection by Attribute Clustering of Infected Rice Plant Images

Phadikar¹,

Sil²,

Das³

2011

Int J Mach Intell

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“…where p(x/ω i ) is called the likelihood function of class ω i . Since 1/p(x) is a common factor so dropped from equation (4) and the expression of average loss then reduce to (5).…”

Section: B Classification Between Diseased Leavesmentioning

confidence: 99%

“…New trends of research in agriculture aim towards the development of the disease resistant variant of seed using gene technology, which increase productivity and food quality at reduced expenditure [4]. With the advancement of information technology, remote sensing techniques have been used in the field of crop management described in [5] - [7]. A relation among the ground disease index and remote sensing data is established in [8] to classify the diseases.…”

Section: Introductionmentioning

confidence: 99%

Classification of Rice Leaf Diseases Based onMorphological Changes

Phadikar¹

2012

IJIEE

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Rice is widely cultivated economical crop in the world. During cultivation the earliest and accurate diagnosis of the rice plant diseases able to reduce the damage, resulting environment protection and better return. In the work, an automated system has been developed to classify the leaf brown spot and the leaf blast diseases of rice plant based on the morphological changes of the plants caused by the diseases. Radial distribution of the hue from the center to the boundary of the spot images has been used as features to classify the diseases by Bayes' and SVM Classifier. The system has been validated using 1000 test spot images of infected rice leaves collected from the field, gives 79.5% and 68.1% accuracies for Bayes' and SVM Classifier based system respectively.

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Spectral characteristics curves of rice plants infected by blast

Cited by 2 publications

References 2 publications

Feature Selection by Attribute Clustering of Infected Rice Plant Images

Feature Selection by Attribute Clustering of Infected Rice Plant Images

Classification of Rice Leaf Diseases Based onMorphological Changes

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