  Shape estimation of sweetpotato (SP) storage roots is inherently challenging
due to their varied size and shape characteristics. Even measuring "simple"
metrics, such as length and width, requires significant time investments either
directly in-field or afterward using automated graders. In this paper, we
present the results of a model that can perform grading and provide yield
estimates directly in the field quicker than manual measurements. Detectron2, a
library consisting of deep-learning object detection algorithms, was used to
implement Mask R-CNN, an instance segmentation model. This model was deployed
for in-field grade estimation of SPs and evaluated against an optical sorter.
Storage roots from various clones imaged with a cellphone during trials between
2019 and 2020, were used in the model's training and validation to fine-tune a
model to detect SPs. Our results showed that the model could distinguish
individual SPs in various environmental conditions including variations in
lighting and soil characteristics. RMSE for length, width, and weight, from the
model compared to a commercial optical sorter, were 0.66 cm, 1.22 cm, and 74.73
g, respectively, while the RMSE of root counts per plot was 5.27 roots, with
r^2 = 0.8. This phenotyping strategy has the potential enable rapid yield
estimates in the field without the need for sophisticated and costly optical
sorters and may be more readily deployed in environments with limited access to
these kinds of resources or facilities.
