  Leaf segmentation is the most direct and effective way for high-throughput
plant phenotype data analysis and quantitative researches of complex traits.
Currently, the primary goal of plant phenotyping is to raise the accuracy of
the autonomous phenotypic measurement. In this work, we present the LeafMask
neural network, a new end-to-end model to delineate each leaf region and count
the number of leaves, with two main components: 1) the mask assembly module
merging position-sensitive bases of each predicted box after non-maximum
suppression (NMS) and corresponding coefficients to generate original masks; 2)
the mask refining module elaborating leaf boundaries from the mask assembly
module by the point selection strategy and predictor. In addition, we also
design a novel and flexible multi-scale attention module for the dual
attention-guided mask (DAG-Mask) branch to effectively enhance information
expression and produce more accurate bases. Our main contribution is to
generate the final improved masks by combining the mask assembly module with
the mask refining module under the anchor-free instance segmentation paradigm.
We validate our LeafMask through extensive experiments on Leaf Segmentation
Challenge (LSC) dataset. Our proposed model achieves the 90.09% BestDice score
outperforming other state-of-the-art approaches.
