Direct Measurement of Sweetpotato Surface Area and Volume Using a Low-cost 3D Scanner for Identification of Shape Features Related to Processing Product Recovery

Abstract

<jats:p>The growing demand for sweetpotato French fry and other processed products has increased the need for producing storage roots of desired shape profile (i.e., blocky and less tapered). Length-width ratio (LW) is the current de facto standard for characterizing sweetpotato shape. Although LW is sensitive and descriptive of some types of shape variability, this index may be inadequate to measure taper and other subtle shape variations. Prior work has shown that surface area (SA) and volume (VOL) are important shape descriptors but current direct measurement methods are tedious, inconsistent, and often destructive. A low-cost three-dimensional (3D) scanner was used to acquire digital 3D models of 210 U.S. No. 1 grade sweetpotato storage roots. The 3D models were imported into Meshmixer, a free software for cleaning and processing 3D files. Processing steps included gap filling and rendering the models water-tight to facilitate VOL measurements. The software includes a tool that enables automatic measurements of length (L), width (W), SA, and VOL. LW and SA-VOL ratio (SAVOL) were subsequently calculated. Separately, a digital caliper was used for manual measurements of L and W. The shrink-wrap method was used to measure SA, and water displacement was used to measure VOL. 3D scanner-based and manual L measurements showed high correlation, whereas VOL was lowest. Principal component analysis (PCA) of 3D scanner-based measurements showed that the first two principal components (PCs) accounted for 96.2% of the total shape variation in the data set, named Ib3D. The first PC accounted for 62.15% of the total variance, and captured variation in storage root shape through changes in VOL, SA, SAVOL, and W. The second PC accounted for 34.4% of the variance, and the main factors were LW and L. Most storage root samples that were classified as processing types were located in the fourth quadrant. The methods described in this work to nondestructively acquire 3D models of sweetpotato also can be adopted for analyzing shape in other horticultural produce like fruits, vegetables, tubers, and other storage roots that meet the specifications for 3D scanning. The data support the hypothesis that knowledge of variables that determine storage root L and W can lead to the development of methods and approaches for enhanced processing product recovery and size assortment for fresh market.</jats:p>

Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery

“…Most research investigating size and shape traits of horticultural crops used lab-scale imaging equipment that are slower and cannot be adapted to production scale environments [17,32]. Further, we considered both ideal and deformed sweetpotatoes making our approach capable of quantifying loss due to shape deformation, while many prior studies excluded sub-standard produce [4,18].…”

“…Previously published methods have focused mostly on 2D morphological features (i.e., height, width, and aspect ratio) and are unsuitable for quantifying produce with highly irregular shapes (e.g., sweetpotatoes, bell peppers, cucumbers, and carrots). In addition, previous studies did not incorporate existing industrial imaging infrastructure, but instead designed or used independent systems for image acquisition, making the methods unsuitable to couple with existing industrial machinery [2,4,17,18].…”

Variation in Phosphorus Availability, Root Architecture Attributes, and Onset of Storage Root Formation among Sweetpotato Cultivars

“…Linking root architecture attributes to storage root formation in sweetpotato paved the way for the systematic investigation of biotic and abiotic variables known to directly or indirectly influence sweetpotato storage root yield potential, including water availability (Villordon et al, 2012a), nitrogen variability (Villordon et al, 2013), virus presence (Villordon and Clark, 2014), and nematode infection (Villordon and Clark, 2018). Regarding sweetpotato in particular, there is increasing interest in understanding the biological and environmental variables that influence primary or main root length because this is directly related to the determination of storage root length and shape (Villordon et al, 2020). Minemba et al (2019) reported evidence of variations in organic exudates among three sweetpotato cultivars in response to Pi levels.…”

Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery

“…Previous studies identified the LW ratio captured from 2D images as the standard feature for quantifying shape variations in agricultural produce [2,33]. However, LW ratio alone is inadequate for capturing sweetpotato shape variation [18]. Our results…”

“…13/18 volume as important shape features for horticultural produce [3,18,34,35]. Interestingly, we did not find volume and tail length among the top influencing variables.…”