Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes

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.Comment: 21 pages, 11 figure

A Win–Win Situation: Performance and Adaptability of Petite Sweetpotato Production in a Temperate Region

New-found interest in sweetpotato production in the Mid-Atlantic and Northeastern U.S. has been steadily increasing in the last several years. Sweetpotatoes are usually grown for fresh market use and novel marketing strategies and new consumer niches are providing farmers options of growing new sweetpotato varieties with exciting colors and flavor profiles that are adapted to the Mid-Atlantic and Northeastern U.S. Petite sweetpotatoes have gained market attention because they are easier to handle and faster to cook compared to U.S. No. 1 storage roots. The goal of this research was to determine the performance and adaptableness of eight commercial sweetpotato varieties and two unreleased accessions for U.S. No.1 and Petite sweetpotato production under black plastic mulch tailored for the mild temperate growing conditions of the Mid-Atlantic and Northeastern U.S. Two in-row spacings (15 cm and 30 cm) and two harvest dates (90 and 120 days after planting, DAP) were evaluated during the 2018 and 2019 growing seasons. Our results showed that the ideal harvest time is at least 120 DAP compared to an early harvest at 90 DAP as there was a 2-fold difference in marketable yield at both 15 and 30 cm in-row spacing with marketable yield between 20 and 54 t ha−1. ‘Averre’ and ‘Beauregard’ produced the highest U.S. No. 1 and Petite yields under both in-row spacing treatments harvested at 120 DAP for both years evaluated, though the general effect of in-row spacing and DAP interaction (separate years) on yield performance was cultivar specific. We also found that growing degree days is a better predictor for harvest than days after planting, with an accumulation of at least ~700 GDD (base temperature 15.5 °C) or ~1300 GDD (base temperature 10 °C) for both U.S. No. 1 and Petite roots. Additional studies are required to identify the stability of cultivars tested and treatments imposed with environmental interactions in this region. In addition, there is an urgency for updated sweetpotato management practices exclusively designed for sweetpotato varieties for the Mid-Atlantic and Northeastern U.S