Segmentation of Cotton Leaves Based on Improved Watershed Algorithm - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2016

Segmentation of Cotton Leaves Based on Improved Watershed Algorithm


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
Origin : Files produced by the author(s)

Dates and versions

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





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⟩
80 View
215 Download



Gmail Facebook X LinkedIn More