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A Novel Method for Leaf Identification using Machine Learning

Authors: C Omkar; S Hemanth Gowda; N Lakshminarayan;

A Novel Method for Leaf Identification using Machine Learning

Abstract

Plants play a very important role for sustenance of life on earth. The leaf of a plant is one of the significant factors to identify various plant characteristics like their species, health and even the climate. The growing interest in biodiversity and the increasing availability of digital images help us to review and analyze plant leaf. We use features like leaf outlines, shape, vein structures and textures and use wide range of analytical methods to describe the mentioned characteristics. We use image processing techniques for a set of training images to generate the feature vectors. Once the machine is trained, we apply test images for feature detection and classification of the plant species. Application of this method can be used in prototypes of hand-held digital field guides and various robotic systems in agriculture. We conclude with a discussion of ongoing work and challenging problems in this area

Keywords

leaf analysis, robotic systems, Image processing, identifying characteristics, detection of species, digital field guide

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Italian National Biodiversity Future Center