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https://doi.org/10.32914/i.51.1-2.1

CROP DISEASE DETECTION AND CLASSIFICATION BASED ON HYBRID INFORMATION APPROACH

S. Vijayalakshmi ; Odjel za računarstvo i inženjerstvo, Sveučilište Manonmaniam Sundaranar, Tirunelveli, Indija
D. Murugan ; Odjel za računarstvo i inženjerstvo, Sveučilište Manonmaniam Sundaranar, Tirunelveli, Indija

Puni tekst: engleski, pdf (528 KB) str. 1-12 preuzimanja: 343* citiraj
APA 6th Edition
Vijayalakshmi, S. i Murugan, D. (2018). CROP DISEASE DETECTION AND CLASSIFICATION BASED ON HYBRID INFORMATION APPROACH. Informatologia, 51 (1-2), 1-12. https://doi.org/10.32914/i.51.1-2.1
MLA 8th Edition
Vijayalakshmi, S. i D. Murugan. "CROP DISEASE DETECTION AND CLASSIFICATION BASED ON HYBRID INFORMATION APPROACH." Informatologia, vol. 51, br. 1-2, 2018, str. 1-12. https://doi.org/10.32914/i.51.1-2.1. Citirano 21.10.2019.
Chicago 17th Edition
Vijayalakshmi, S. i D. Murugan. "CROP DISEASE DETECTION AND CLASSIFICATION BASED ON HYBRID INFORMATION APPROACH." Informatologia 51, br. 1-2 (2018): 1-12. https://doi.org/10.32914/i.51.1-2.1
Harvard
Vijayalakshmi, S., i Murugan, D. (2018). 'CROP DISEASE DETECTION AND CLASSIFICATION BASED ON HYBRID INFORMATION APPROACH', Informatologia, 51(1-2), str. 1-12. https://doi.org/10.32914/i.51.1-2.1
Vancouver
Vijayalakshmi S, Murugan D. CROP DISEASE DETECTION AND CLASSIFICATION BASED ON HYBRID INFORMATION APPROACH. Informatologia [Internet]. 2018 [pristupljeno 21.10.2019.];51(1-2):1-12. https://doi.org/10.32914/i.51.1-2.1
IEEE
S. Vijayalakshmi i D. Murugan, "CROP DISEASE DETECTION AND CLASSIFICATION BASED ON HYBRID INFORMATION APPROACH", Informatologia, vol.51, br. 1-2, str. 1-12, 2018. [Online]. https://doi.org/10.32914/i.51.1-2.1

Sažetak
The objective of this paper to identify the diseases in the leaves of the all plants. Plant disease diagnosis helps to improve both the quality and quantity of crop productivity. In existing, to detect the diseases they used the spectroscopic techniques. These techniques are very expensive and can only be utilized by trained persons only. This work proposes an approach for the detection of leaf diseases based on the characterization of texture, shape and color properties. The detection of diseases which are detected using ISRC(improved sparse Representation Classifier) technique. First the GENABC clustering approach is applied to the input image to segment the affected area. Then extract the features from the affected area by using feature extraction techniques. In this paper Improved Transform Encoded Local Pattern used to extract the texture feature, Enhanced Gradient Feature (EGF) to extract the shape and Improved Color Histogram Techniques(ICH) are used to extract the color. And then these features are given to the ISRC classifier to get the exact type of disease on affected leaves. To analyze the performance of the proposed method we use four metrics. They are classification accuracy, error rate, precision value and recall value. From the analysis of experimental results, the ISRC method provides the best result than the existing approach.

Ključne riječi
plant diseases; GENABC clustering; ITELP; EGF; ISRC classifier

Hrčak ID: 203300

URI
https://hrcak.srce.hr/203300

[hrvatski]

Posjeta: 486 *