Investigation of Optimal Network Architecture for Asparagus Spear Detection in Robotic Harvesting

Abstract

The University of Waikato, in collaboration with Robotics Plus Limited have developed a robotic asparagus harvester that utilises a convolutional neural network for spear detection. This paper serves as a starting point for selecting an optimal network architecture for this purpose. Specifically, this paper compared the performance of Faster RCNN (FRCNN) and Single Shot Multibox Detector (SSD) on a dataset collected by the harvesters camera systems during field trials in California. Additionally, the effect of labelling the dataset using both a single-class and multi-class paradigm were evaluated. It was found that FRCNN, trained using a single-class paradigm, had the best performance of the tested networks. This was characterized by a F1 score of 0.73, approximately 38% higher other networks tested. Multi-class labelling paradigms were found to result in approximately 27% reduction in F1 score than Single-class labelling paradigms for both FRCNN and SSD. Based on these results we conclude that FRCNN based detectors are better suited for asparagus detection than SSD based detectors.