Multi_layer graph plant leaf segmentation

Published: 15 March 2024| Version 1 | DOI: 10.17632/46n94cngkx.1
Lyasmine ADADA,


We showcase the results of our graph-based diffusion technique utilizing random walks with restarts on a multi-layered graph using the publicly accessible Pl@ntleaves (H. Go¨eau, P. Bonnet, A. Joly, N. Boujemaa, D. Barth´el´emy, J.-F. Molino, P. Birnbaum, E. Mouysset, M. Picard, The clef 2011 plant images classification task, Vol. 1177, 2011.) dataset. This dataset comprises 233 high-resolution leaf images taken in their natural environments, presenting various segmentation challenges such as shadows, diverse lighting conditions, and leaf overlap. Our method primarily focuses on identifying leaf regions by initially locating the leaves within the images and then propagating intensity scores from foreground templates to image boundaries to generate saliency maps. By applying a threshold to these saliency maps produced through the diffusion process, we derive binary masks that effectively separate the leaves from the backgrounds. Ground truth images are provided for visual evaluation of our algorithm's performance.Folders description: image: RGB images mask: Ground truth masks FG_templates: foreground templates and bounding boxes defined on dataset images Salinecy_map: saliency maps obtained by our approach PR_masks: Predicted masks obtained by tresholding our salinecy maps Plant_Leaf_Segmentation: a compressed folder containing the above folders.



Universite Mouloud Mammeri de Tizi Ouzou


Image Processing, Image Segmentation, Precision Agriculture, Computer Vision Algorithms