Plant Diseases Detection Rate Improvement with Enhanced SVD Technique

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Plant Diseases Detection Rate Improvement with Enhanced SVD Technique

Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 9

Abstract

In modern era, agriculture plays one of the most essential role for growing habitat. Flora provides major source of energy and also helps to reduce global warming. Agricultural plants face various diseases in different stages of its life. Remote sensing techniques are used to explore multi and hyper spectral pictures. It employs digital image processing to monitor health and diseases related with plants. For general production practice, observation through naked eye gives severity and subjective results. Higher disease detection accuracy can be achieved efficiently through advance digital image processing techniques. In the existing work, back propagation (BP) and principal component analysis (PCA) algorithms were usually used for plant diseases detection. These are neural network based learning and supervision algorithms. Much higher accuracy can be achieved using Single Value Decomposition (SVD) algorithms. This paper represents implementation of SVD along with BP and PCA for disease detection. It also shows that better accuracy is achieved with enhanced SVD in contrast to these three algorithms individually.

Authors and Affiliations

Amit Pandey, Amrit Singh

Keywords

Back Propagation; Principle Component Analysis; SVD; Plant diseases; Pattern recognition

EP ID EP392447
DOI 10.9790/9622-0709081520.
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