DocumentCode
3612127
Title
Identifying Central Features of Cotton Leaves in Digital Images with Difficult Backgrounds
Author
Garcia Arnal Barbedo, Jayme
Author_Institution
Centro Nac. de Pesquisa Tecnol. em Inf. paraAgricultura, Empresa Brasileira de Pesquisa Agropecuaria, São Paulo, Brazil
Volume
13
Issue
9
fYear
2015
Firstpage
3072
Lastpage
3079
Abstract
Many digital image processing techniques applied to agricultural problems have as main target the leaves of certain species of plants. The most basic task in such a context is to segment the leaf of interest from the rest of the scene, which is relatively straightforward when the leaf is isolated and the image is captured under controlled conditions. However, real field conditions will often imply in little control over lighting and, more importantly, the background may include several elements that make the task considerably more challenging. This is especially true if there are other leaves with similar shape, texture and color in the scene, which is often the case. This paper presents a method to identify the main node of cotton leaves (where the petiole meets the veins) and the main primary vein, giving valuable information about the position and orientation of those leaves. The only constraint to which the method is subject is that the leaf of interest be located in a central position in the image.
Keywords
cotton; feature extraction; image colour analysis; image segmentation; image texture; agricultural problems; central features; controlled conditions; cotton leaves; difficult backgrounds; digital image processing; digital images; leaf segments; primary vein; real field conditions; scene color; scene shape; scene texture; Cotton; Deformable models; Digital images; Image resolution; Image segmentation; Irrigation; Veins; cotton leaves; digital images; segmentation;
fLanguage
English
Journal_Title
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher
ieee
ISSN
1548-0992
Type
jour
DOI
10.1109/TLA.2015.7350061
Filename
7350061
Link To Document