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<p>Vectored Machine Learning Rearing Process: Early Detection of Leaf Diseases</p>

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Title Statement <p>Vectored Machine Learning Rearing Process: Early Detection of Leaf Diseases</p>
 
Added Entry - Uncontrolled Name Prasad, B Rajendra; Department of ECE, JNTUA, Anantapuramu, Andhra Pradesh, India
Ramashri, T ; Department of ECE, S V University, Tirupati, Andhra Pradesh, India
Naidu, K Rama; Department of ECE, JNTUA, Anantapuramu, Andhra Pradesh, India
 
Uncontrolled Index Term Clustering; Disease Severity; Plant leaf analyses; Quantification; Recognition
 
Summary, etc. <p class="Abstract"><span lang="EN-GB">Over 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.</span></p><br />
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2020-11-09 16:10:07
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/40475
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 7
 
Language Note en