Data from: Deployment and analysis of instance segmentation algorithm for in-field yield estimation of sweet potatoes

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 diameter, requires
significant time investments either directly in-field or afterward using
automated graders. We present the results of a model that can perform
grading and provide yield estimates directly in the field faster 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 SP roots and evaluated against an optical sorter.
 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 SP roots.  Our results showed that
the model (Average Precision = 74.1) could distinguish individual roots in
environmental conditions, including variations in lighting and soil
characteristics.  Root mean square error (RMSE) for length,
diameter, 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 to enable rapid yield estimates in the field
without the need for sophisticated and costly sorters and may be more
readily deployed in environments with limited access to these resources or
facilities.