Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning

摘要

The overall work-flow of panicle-leaf quantification. The upper shows the training procedure of the FPN-Mask model implemented in this study. The bottom depicts the GvCrop working procedure to calculate the LPR (leaf to panicle ratio). (1) Generating 1896 patches by random manual cutting. (2) Manual labelling of every pixel to panicle, leaf and background. (3) Brightness enhancement of patches, normalization to [0, 1] and resizing to 256 × 256 pixels. (4) Training the FPN-Mask model. (5) Daily validation of FPN-Mask with field images and iterative addition of negative samples. (6) Integration of the saved model to semantic segmentation of field images by GvCrop. (7) Manual modification of the predicted result by super-pixel segmentation method integrated in GvCrop