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Training of convolutional neural networks for semantic segmentation requiresaccurate pixel-wise labeling which requires large amounts of human effort. Thehuman-in-the-loop method reduces labeling effort; however, it requires humanintervention for each image. This paper describes a general iterative trainingmethodology for semantic segmentation, Automating-the-Loop. This aims toreplicate the manual adjustments of the human-in-the-loop method with anautomated process, hence, drastically reducing labeling effort. Using theapplication of detecting partially occluded apple tree segmentation, we comparemanually labeled annotations, self-training, human-in-the-loop, andAutomating-the-Loop methods in both the quality of the trained convolutionalneural networks, and the effort needed to create them. The convolutional neuralnetwork (U-Net) performance is analyzed using traditional metrics and a newmetric, Complete Grid Scan, which promotes connectivity and low noise. It isshown that in our application, the new Automating-the-Loop method greatlyreduces the labeling effort while producing comparable performance to bothhuman-in-the-loop and complete manual labeling methods.

Minimizing Labeling Effort for Tree Skeleton Segmentation using an Automated Iterative Training Methodology