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Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the ...
Tomato sucker pruning is an important activity, but there is no public research about how to remove tomato suckers automatically.Figure 1shows an example of a tomato sucker which is green in the right image ofFigure 1. A sucker is always between a stem and a branch. It should be cut off...