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

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Academic year: 2020

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Figure

Fig. 1 Challenges in the field based rice panicle segmentation. A Water reflection. B The illumination unbalance in the same plot
Fig. 2 The flow diagram of the Panicle-SEG algorithm. ated in off-line training is utilized in testing patches classification, and the testing patches were categorized into candidate panicle and confirmed background
Fig. 3 The means and standard deviations of six evaluation indicators for the testing set
Fig. 4 Comparison to state-of-the-art segmentation approaches. Four representative field rice images are selected to illustrate the segmentation effect
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