  Potato yield is an important metric for farmers to further optimize their
cultivation practices. Potato yield can be estimated on a harvester using an
RGB-D camera that can estimate the three-dimensional (3D) volume of individual
potato tubers. A challenge, however, is that the 3D shape derived from RGB-D
images is only partially completed, underestimating the actual volume. To
address this issue, we developed a 3D shape completion network, called CoRe++,
which can complete the 3D shape from RGB-D images. CoRe++ is a deep learning
network that consists of a convolutional encoder and a decoder. The encoder
compresses RGB-D images into latent vectors that are used by the decoder to
complete the 3D shape using the deep signed distance field network (DeepSDF).
To evaluate our CoRe++ network, we collected partial and complete 3D point
clouds of 339 potato tubers on an operational harvester in Japan. On the 1425
RGB-D images in the test set (representing 51 unique potato tubers), our
network achieved a completion accuracy of 2.8 mm on average. For volumetric
estimation, the root mean squared error (RMSE) was 22.6 ml, and this was better
than the RMSE of the linear regression (31.1 ml) and the base model (36.9 ml).
We found that the RMSE can be further reduced to 18.2 ml when performing the 3D
shape completion in the center of the RGB-D image. With an average 3D shape
completion time of 10 milliseconds per tuber, we can conclude that CoRe++ is
both fast and accurate enough to be implemented on an operational harvester for
high-throughput potato yield estimation. Our code, network weights and dataset
are publicly available at
https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git.
