Work in robotic phenotyping requires computer vision methods that estimate the number of fruit or grains in an image. To decide what to use, we compared three methods for counting fruit and grains, each method representative of a class of approaches from the literature. These are two methods based on density estimation and regression (single and multiple column), and one method based on object detection. We found that when the density of objects in an image is low, the approaches are comparable, but as the density increases, counting by regression becomes steadily more accurate than counting by detection. With more than a hundred objects per image, the error in the count predicted by detection-based methods is up to 5 times higher than when using regressionbased ones.

In this paper, a grounding framework is proposed that combines unsupervised and supervised grounding by extending an unsupervised grounding model with a mechanism to learn from explicit human teaching. To investigate whether explicit teaching improves the sample efficiency of the original model, both models are evaluated through an interaction experiment between a human tutor and a robot in which synonymous shape, color, and action words are grounded through geometric object characteristics, color histograms, and kinematic joint features. The results show that explicit teaching improves the sample efficiency of the unsupervised baseline model.

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Using AI for agriculture requires the fast transmission and processing of large volumes of data. Cost-effective high speed processing may not be possible on-board agricultural vehicles, and suitably fast transmission may not be possible with older generation wireless communications. In response, the work presented here investigates the use of 5G wireless technology to support the deployment of AI in this context.

This paper presents the results of preliminary experiments in humanrobot collaboration for an agricultural task. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Computer systems organization → Robotic control; • Computing methodologies → Vision for robotics.