Boosting plant-part segmentation of cucumber plants by enriching incomplete 3D point clouds with spectral data

Plant scientists require high quality phenotypic datasets. Computer-vision based methods can improve the objectiveness and the accuracy of phenotypic measurements. In this paper, we focus on 3D point clouds for measuring plant architecture of cucumber plants, using spectral data and deep learning (DL). More specifically, the focus of this paper is on the segmentation of the point clouds, such that for each point it is known to which plant part (e.g. leaf or stem) it belongs. It was shown that the availability of spectral data can improve the segmentation, with the mean intersection-over-union rising from 0.90 to 0.95. Furthermore, we analysed the effect of uncertainty in the collection of ground truth data. For this purpose, we hand-labelled 264 point clouds of cucumber plants twice and show that the intra-observer variability between those two annotation sets can be as low as 0.49 for difficult classes, while it was 0.99 for the class with the least uncertainty. Adding the second set of hand-labelled data to the training of the network improved the segmentation performance slightly. Finally, we show the improved performance of a 4-class segmenta-tion over an 8-class segmentation, emphasizing the need for a careful design of plant phenotyping experiments. The results presented in this paper contribute to further development of automated phenotyping methods for complex plant traits. (c) 2021 The Authors. Published by Elsevier Ltd on behalf of IAgrE. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This paper focuses on measuring plant architecture of cucumber plants using 3D point clouds, spectral data, and deep learning. Results show that spectral data can improve segmentation accuracy, and the effect of uncertainty in ground truth data collection was analyzed.