# High Throughput Soybean Pod-Counting with In-Field Robotic Data Collection and Machine-Vision Based Data Analysis

arXiv: https://arxiv.org/abs/2105.10568 · HTML: https://mlsanity.com/paper/2105.10568/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 1 of 1)

- [Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications](https://mlsanity.com/paper/2011.07118/) (2020)

## Built on by (most cited first, 2 of 2)

- [Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod](https://mlsanity.com/paper/2204.10902/) (2022)
- [Robust soybean seed yield estimation using high-throughput ground robot videos](https://mlsanity.com/paper/2412.02642/) (2024)

## Abstract (arXiv)

We report promising results for high-throughput on-field soybean pod count with small mobile robots and machine-vision algorithms. Our results show that the machine-vision based soybean pod counts are strongly correlated with soybean yield. While pod counts has a strong correlation with soybean yield, pod counting is extremely labor intensive, and has been difficult to automate. Our results establish that an autonomous robot equipped with vision sensors can autonomously collect soybean data at maturity. Machine-vision algorithms can be used to estimate pod-counts across a large diversity panel planted across experimental units (EUs, or plots) in a high-throughput, automated manner. We report a correlation of 0.67 between our automated pod counts and soybean yield. The data was collected in an experiment consisting of 1463 single-row plots maintained by the University of Illinois soybean breeding program during the 2020 growing season. We also report a correlation of 0.88 between automated pod counts and manual pod counts over a smaller data set of 16 plots.