Improving soybean (Glycine max L. (Merr.)) yield is crucial for strengthening national food security. Predicting soybean yield is essential to maximize the potential of crop varieties. Non-destructive methods are needed to estimate yield before crop maturity. Various approaches, including the pod-count method, have been used to predict soybean yield, but they often face issues with the crop background color. To address this challenge, we explored the application of a depth camera to real-time filtering of RGB images, aiming to enhance the performance of the pod-counting classification model. Additionally, this study aimed to compare object detection models (YOLOV7 and YOLOv7-E6E) and select the most suitable deep learning (DL) model for counting soybean pods. After identifying the best architecture, we conducted a comparative analysis of the model’s performance by training the DL model with and without background removal from images. Results demonstrated that removing the background using a depth camera improved YOLOv7’s pod detection performance by 10.2% precision, 16.4% recall, 13.8% mAP@50, and 17.7% mAP@0.5:0.95 score compared to when the background was present. Using a depth camera and the YOLOv7 algorithm for pod detection and counting yielded a mAP@0.5 of 93.4% and mAP@0.5:0.95 of 83.9%. These results indicated a significant improvement in the DL model’s performance when the background was segmented, and a reasonably larger dataset was used to train YOLOv7.

The Northern Uniform Soybean Tests (NUST) are a regional field trial network coordinated by the United States Department of Agriculture to evaluate experimental soybean ( Glycine max L.) strains developed by public institutions. Historical data from the NUST compiled, curated, and reported herein comprise a valuable multi‐environment trial dataset including relevant elite soybean germplasm from maturity groups 00 to IV evaluated over 28 years in 199 locations, totaling 1652 environments. Our aim was to characterize the genetic structure of the NUST experimental strains, perform genome‐wide association studies using historical phenotypic data, and assess the usefulness of these historical data for genomic prediction model training. Molecular marker information was collected on 2544 unique NUST experimental strains using the BARCSoySNP6K assay. High fixation index values between early and later maturity groups were observed in a region on chromosome 10 near the known soybean maturity gene E2 . We failed to find strong genetic divergence between strains from different breeding programs, reflecting the germplasm sharing common among public programs. Genome‐wide association analyses on important agronomic traits identified marker‐trait associations, many of which overlap with quantitative trait loci previously reported in the literature. Genomic prediction models trained using the historical NUST data produced moderate to high predictive abilities in most cases, suggesting these data could make useful contributions to training sets. We have made these data publicly available as a data resource for others to study genotype–phenotype relationships within elite public soybean germplasm and develop predictive models for advancement and implementation of genomics‐assisted breeding.

The soybean cultivar ‘ND21008GT20’ [ Glycine max (L.) Merr.] (PVP no. 202200004; Reg. No. CV‐570, PI 699333), originally named as breeding line ND14‐6120(GT), was released in January 2021 by the North Dakota Agricultural Experiment Station, North Dakota State University (NDSU). ND21008GT20 is a Maturity Group 00 (relative maturity 00.8) glyphosate‐tolerant (GT) cultivar suitable for northern North Dakota. It has purple flower color, tawny pubescence, brown pod color, gray hilum, and dull seed‐coat luster. ND21008GT20 was evaluated by the NDSU soybean breeding program in advanced yield trials and in United States Department of Agriculture (USDA) Uniform tests for a total of 20 site‐years between 2016 and 2018. The cultivar ND21008GT20 presents early maturity, glyphosate tolerance, resistance to Phytophthora sojae Kaufmann & Gerdemann race 4, and high tolerance to iron deficiency chlorosis (IDC).