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Semantic Segmentation of Coffee Fruits with 2D Information

Lizeth Herrera, Flavio Prieto, Paula Ramos


The current conditions of agriculture in Colombia show the need to automate small and large processes that are carried out every day. One of these processes is the collection of coffee beans, which has high impact in the world. In this work, the automatic segmentation is proposed for different vegetative structures such as Green, Yellow, Orange and Red fruits, Stem, Leaf, Flower and Bottom, under uncontrolled light conditions. The learning was done with Conditional Random Field (CFR), using SLIC to find superpixels and acquire features with 2D information. As a result, the vegetative structures of the images were segmented with a recall higher than 0.79.


SLIC, CRF, Segmentation, coffee, 2D.

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