Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes

Abstract

Recent advancements in artificial intelligence and big data analytics introduce new tools that can enhance the packing efficiency of sweetpotatoes (Ipomoea batatas) (SPs). In this study, we focused on the quantification of inventory as early in the packing process as possible to allow for effective storage planning, smarter inventory selection to fulfill orders, and ultimately reduce the need for refrigeration of excess packed SPs. We built and implemented two scanners to quantify phenotype distributions at different stages of the post-harvest pipeline. Testing and validation were conducted through a collaboration with an industry-partner's packing facility in North Carolina, gaining access to their packing methods, warehouse data, and resources. The first scanner imaged all SPs during the conveyance stage, immediately after they are washed but before they are sorted. The second scanner, positioned to view the top bins after harvest, scanned the top layer of bins on harvesting trucks as they entered the storage warehouse for receiving. We compared the output of our first scanner to the output of a commercial optical sorter under a controlled packing simulation, and then compared our two developed scanners against each other in an observational commercial packing operation. We evaluated millions of SPs, assessing length, width, length-to-width ratio (LW ratio), and weight. We computed a pairwise t-test for each phenotype across scanner pairs and evaluated the Cohen's d effect size to interpret our results. We observed no significant differences in the grade distributions across the scanners, except for the “Giant” weight class, which showed variation between the top bin and eliminator table scanners. In summary, both systems demonstrated promising outcomes, suggesting a potential enhancement in packing efficiency through the timely delivery of comprehensive inventory data.