High Throughput Soybean Pod-Counting with In-Field Robotic Data Collection and Machine-Vision Based Data Analysis
This work addresses the need for high-throughput, automated pod counting in soybean breeding programs, which is incremental as it applies existing robotics and vision methods to a specific agricultural challenge.
The researchers tackled the labor-intensive problem of automating soybean pod counting by using a mobile robot with vision sensors and machine-vision algorithms, achieving a correlation of 0.67 between automated pod counts and soybean yield across 1463 plots and 0.88 with manual counts in a smaller subset.
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.