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Weed Instance Segmentation
  • Jesus Franco Robles ,
  • Jorge Eduardo Aviles Mejia ,
  • Ouiddad Labbani-Igbida
Jesus Franco Robles
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Jorge Eduardo Aviles Mejia
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Ouiddad Labbani-Igbida
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Abstract

Precision agriculture brings together most innovative topics from research in the fields of vision, artificial intelligence and robotics aimed at developing sustainable and resilient solutions. In this context, crop identification is a crucial issue. This paper exposes a new algorithm to enhance real-time crop/weed instance segmentation, merging a learning-based method for instance segmentation and a feature model-based image processing strategy that relies on the vegetation characteristics of the crops. The proposed algorithm compensates for the shortcomings of each method performing independently. That is, the image processing strategy fulfils crop segmentation by generating finely refined masks; however, some weeds are segmented incorrectly. On the other hand, instance segmentation methods perform well on weeds and crops but, when inaccurate bounding boxes are present, imperfect masks are generated. The experiments are conducted in different evaluation campaigns within the ACRE international competition framework.