Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/54987
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dc.contributor.authorPrasad, B Rajendra-
dc.contributor.authorRamashri, T-
dc.contributor.authorNaidu, K Rama-
dc.date.accessioned2020-08-24T06:45:28Z-
dc.date.available2020-08-24T06:45:28Z-
dc.date.issued2020-07-
dc.identifier.issn0975-1084 (Online); 0022-4456 (Print)-
dc.identifier.urihttp://nopr.niscair.res.in/handle/123456789/54987-
dc.description619-625en_US
dc.description.abstractOver the past years, the plant leaf analyses through image processing have drawn a remarkable approach in assessing leaf disease severity through accurate and precise conclusions. We proposed, ‘Scale Invariant Feature Transform’ (SIFT) based Distinctive Scale Invariant Mapping Procedure (DSIMP) for training images. Random Separation Propagation (RSP) Procedure and Redundant multiclass Support Vector Machine (RM-SVM) are implemented to detect the rice and groundnut leaf diseases at its early stages. Discriminative Gray Level Co-occurrence Matrix (DGLCM) and K means clustering is used for recognition and quantification to give the best color analysis. Experiments with 1000 samples of rice and groundnut leaf images show promising performance.en_US
dc.language.isoen_USen_US
dc.publisherNISCAIR-CSIR, Indiaen_US
dc.rights CC Attribution-Noncommercial-No Derivative Works 2.5 Indiaen_US
dc.sourceJSIR Vol.79(07) [July 2020]en_US
dc.subjectClusteringen_US
dc.subjectDisease Severityen_US
dc.subjectPlant leaf analysesen_US
dc.subjectQuantificationen_US
dc.subjectRecognitionen_US
dc.titleVectored Machine Learning Rearing Process: Early Detection of Leaf Diseasesen_US
dc.typeArticleen_US
Appears in Collections:JSIR Vol.79(07) [July 2020]

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