High-throughput classification and quantification of skinning phenotype in sweet potatoes

Sweetpotatoes (SPs) (Ipomoea batatas) are crops valued for their color, flavor, and nutrition.  Harvesting is labor-intensive, requiring hand-picking to maintain skin quality.  Mechanical harvesting often causes skin damage, known as “skinning,” where skin is cut, scraped, or torn, leading to lower quality during packing. To manage this, packers may conduct a costly “field-switch” to reduce skinning in the production line. Currently, skinning levels are visually assessed by the packers and stakeholders.  Field-switches involve transitioning between multiple fields during harvest to meet specific customer orders (e.g., supermarkets, processing plants, or end users) that require higher-quality SPs. This process aims to minimize skinning and ensure the SPs meet the desired quality standards for those orders.  This study introduces a computer vision (CV) pipeline to automate skinning assessment using a ResNet50-based DeepLabV3+ semantic segmentation model. The CV system ..., Data Collection
The data collection process involved reviewing millions of SPs over a two-year period, from late winter 2022 to late winter 2024. Based on discussions with our industry collaborator, we identified specific days when skinning issues were most apparent. SPs for the training dataset were chosen for their fresh skinning marks, which were likely caused by harvesting, rough handling, improper curing, washing, or damage from machinery. These skinning issues typically occurred within one to two weeks of the harvest, guiding the selection of images.
The images collected were stored in .jpg format with a ground sample distance (GSD) of 0.521 mm/pixel. Most images had a size of 866x1599 pixels, though a few were smaller and resized to 866x1599 pixels using MATLAB. The data collection process was ongoing throughout the study, with adjustments made to the dataset after each performance evaluation to ensure that only the most relevant and high-quality images were retained.
Data Curati..., , # Data from: High-throughput classification and quantification of skinning phenotype in sweet potatoes

[https://doi.org/10.5061/dryad.v15dv4272](https://doi.org/10.5061/dryad.v15dv4272)

## Description of the data and file structure

Data are provided for the machine learning model and pipeline, including the model's coefficients in Matlab, and regions of interest (ROIs) used for training and validation.

### Files and variables

#### File: Skinning_ML_Pipeline.zip

**Description:** The Skinning_ML_Pipeline.zip archive contains three directories and one MATLAB script:

1. Skinning_SP_Training.m: Script for loading training images, running model transfer learning, and converting the raw imagery data used for validation and testing. Certain cells can also be used for inference of new imagery, or inference can be run on the sample data provided.
2. Folder Train_and_validate_labels/ contains the labels for the training and validation images used for creating the model. The sub-folder \"vali...,