Journal
PLANT METHODS
Volume 13, Issue -, Pages -Publisher
BIOMED CENTRAL LTD
DOI: 10.1186/s13007-017-0254-7
Keywords
Rice (O. sativa) panicles; Image segmentation; Deep learning; Convolutional neural network; Superpixel
Categories
Funding
- National Program on High Technology Development [2013AA102403]
- National key research and development program [2016YFD0100101-18]
- National Natural Science Foundation of China [31701317, 31770397]
- Hubei Provincial Natural Science Foundation of China [2017CFB208]
- Scientific Conditions and Resources Research Program of Hubei Province of China [2015BCE044]
- Fundamental Research Funds for the Central Universities [2662015QC006, 2662017PY058]
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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 randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online. Conclusions: In conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.
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