Segmentation of Cotton Leaves Based on Improved Watershed Algorithm - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Segmentation of Cotton Leaves Based on Improved Watershed Algorithm

Résumé

Crop leaf segmentation was one important research content in agricultural machine vision applications. In order to study and solve the segmentation problem of occlusive leaves, an improved watershed algorithm was proposed in this paper. Firstly, the color threshold component (G−R)/(G+R) was used to extract the green component of the cotton leaf image and remove the shadow and invalid background. Then the lifting wavelet algorithm and Canny operator were applied to extract the edge of the pre-processed image to extract cotton leaf region and enhance the leaf edge. Finally, the image of the leaf was labeled with morphological methods to improve the traditional watershed algorithm. By comparing the cotton leaf area segmented using the proposed algorithm with the manually extracted cotton leaf area, successful rates for all the images were higher than 97 %. The results not only demonstrated the effectiveness of the algorithm, but also laid the foundation for the construction of cotton growth monitoring system.
Fichier principal
Vignette du fichier
434296_1_En_41_Chapter.pdf (578.9 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01557800 , version 1 (06-07-2017)

Licence

Paternité

Identifiants

Citer

Chong Niu, Han Li, Yuguang Niu, Zengchan Zhou, Yunlong Bu, et al.. Segmentation of Cotton Leaves Based on Improved Watershed Algorithm. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. pp.425-436, ⟨10.1007/978-3-319-48357-3_41⟩. ⟨hal-01557800⟩
86 Consultations
220 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More