Minimal Geometric Representation and Strawberry Stem Detection

Abstract

This paper takes a crucial step toward a visual system for an automated strawberry harvester. We present an algorithm based on the Blum medial axis that outputs for a given berry image a bounding box containing the berry's stem, and determines minimal geometric information to do so. The algorithm first generates three potential boxes, then automatically selects which of the three contains the stem. We compare the performance of our geometric-based stem detection with two other methods. The first, implemented already for a berry harvesting robot, relies on the principal axes of the berry shape to define the bounding box. The second takes as input the three potential boxes generated using the medial axis, then selects the one containing the stem by computing geometric and appearance features within each box for use in an ensemble classifier of 250 trees boosted by RUSboost with five-leaf minimum and a learning rate of 0.1. Note that because our data is imbalanced we used class-proportional sampling. Our geometric approach outperforms the other two methods on a database of 286 strawberry images.