Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization.

ABSTRACT:

Background

Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field's complex background, rice panicle segmentation in the field is a very large challenge.

Results

In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were ran

SUBMITTER: Xiong X 

PROVIDER: S-EPMC5704426 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature