Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum

Asfarian, Auzi; Herdiyeni, Yeni; Rauf, Aunu; Mutaqin, Kikin Hamzah

doi:10.1109/ic3ina.2013.6819152

Cited by 73 publications

(24 citation statements)

References 13 publications

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“…Paddy disease identification based on texture analysis using fractal descriptor and "S" component of HSV color space [4] achieved at least 83 % in classifying the image of infected leaves. Classification technique is also used to recognize fungi-caused disease on sugarcane leaves based on segmented spot of the disease image, with 95.25% precision [5].…”

Section: Introductionmentioning

confidence: 99%

Sugarcane leaf disease detection and severity estimation based on segmented spots image

Ratnasari

Mentari

Dewi

et al. 2014

Proceedings of International Conference on Information, Communication Technology and System (ICTS) 2014

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About 15% of sugarcane leaf is defective because of diseases, it reduces the quantity and quality of sugarcane production significantly. Early detection and estimation of plant disease is a way to control these diseases and minimize the severe infection. This paper proposes a model to identify the severity of certain spot disease which appear on leaves based on segmented spot. The segmented spot is obtained by thresholding a* component of L*a*b* color space. Diseases spots are extracted with maximum standard deviation of segmented spot that use for detection the type of disease using classification techniques. The classifier is a Support Vector Machine (SVM) which uses L*a*b* color space for its color features and Gray Level Co-Occurrence Matrix (GLCM) as its texture features. This proposed model capable to determine the types of spot diseases with accuracy of 80% and 5.73 error severity estimation average.

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Section: Introductionmentioning

confidence: 99%

Sugarcane leaf disease detection and severity estimation based on segmented spots image

Ratnasari

Mentari

Dewi

et al. 2014

Proceedings of International Conference on Information, Communication Technology and System (ICTS) 2014

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“…More than 60,000 entries / year were evaluated in one greenhouse in IRRI. Whereas the procedures to obtain the image data during resistance level scoring using SSST from [6] can be seenin Figure 2 and Table 1. …”

Section: B Standard Seedboxes Screening Test (Ssst)mentioning

confidence: 99%

“…From the research, the best result was obtained from using the A component from CIELab [5]. Another research [6] has been done to measure the infection severity, by using the component A of CIELab color space on paddy hills images, to differentiate the infected plants from the healthy plants, then looking for the interval through diagram box plot. The measurement accuracy obtained in the experiment was 70.83%.…”

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confidence: 99%

Computer Vision for Screening Resistance Level of Rice Varieties to Brown Planthopper

Nurfadhilah¹,

Herdiyeni²,

Rauf³

et al. 2015

ijacsa

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Abstract-Brown planthopper is one of the most important insect pest that threatens the stability of national rice production in Indonesia. One of the efforts to save rice production is by using brown planthopper resistant variety. Currently the determination approach is still conventional based on Standard Seedboxes Screening Test from IRRI with assistance of experienced experts in the scoring process resistance level.In this study, a prototype of application system to predict resistance levels by image color approach was developed. The method consists of collecting images data, preparation process (background and objects segmentation), and determination of area proportion which has been infected (sick and dead) and healthy, based on 'A' value from CIELab color space laboratory. According to proportion value distribution, the rule of rice resistance to brown planthopper assessment based on image was developed. The rule is mostly similar with IRRI standard rules. All of images were assessed based on the rule and then the model was developed with an error rate of 17.02%.

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“…Auzi Asfarian et.al., [5], has endeavored to distinguish the four noteworthy paddy sicknesses in Indonesia to be specific leaf blast, brown spot, bacterial leaf blight and tungro. Fractal descriptors are utilized to dissect the composition of the sores.…”

Section: Related Workmentioning

confidence: 99%

Detection and Recognition of Diseases from Paddy Plant Leaf Images

Mohan¹,

Balasubramanian²,

Palanivel³

2016

IJCA

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Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum

Cited by 73 publications

References 13 publications

Sugarcane leaf disease detection and severity estimation based on segmented spots image

Sugarcane leaf disease detection and severity estimation based on segmented spots image

Computer Vision for Screening Resistance Level of Rice Varieties to Brown Planthopper

Detection and Recognition of Diseases from Paddy Plant Leaf Images

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