Evaluating an A.I. Tool on Wisconsin Soybean and Corn Plots

Jan 17, 2022

By Shawn P. Conley

IN A BEAN POD:

Soybean A.I.-based cropping systems were, in general, successful in increasing yield and profit compared to typical systems
Across all locations, following soybean A.I. recommended systems would have increased mean yield by ~7 bu/ac and mean profit by ~$40/ac compared to typically used cropping systems
The potential of corn A.I.-based cropping systems to increase yield and profit was not clear
The corn A.I. tool recommended systems resulted in either increased or similar profit with typical systems by applying 19-223% lower nitrogen fertilizer rate

Algorithm-based decision making will likely play an important role in the coming years. Algorithms can capture and quantify complex relationships that can result in more informative decisions with greater probability of success (effectively increase profit) compared to current approaches. Evaluation of such tools in field conditions which involve unexpected and unmanageable yield adversities is important.

In this work, soybean A.I.-based cropping systems were in general successful to increase yield and profit compared to typical systems. The potential of corn A.I.-based cropping systems to increase yield and profit though was not clear. Additionally, Tar Spot was found and not treated at all three locations. This may have impacted the overall results of the experiment and suggest that the A.I. tool alone cannot account for in-season IPM decisions and should be paired with scouting or other management tools such as TarSpotter.

Source : wisc.edu

Video: Designing a Robotic Berry Picker

Since blackberries must be harvested by hand, the process is time-consuming and labor-intensive. To support a growing blackberry industry in Arkansas, food science associate professor Renee Threlfall is collaborating with mechanical engineering assistant professor Anthony Gunderman to develop a mechanical harvesting system. Most recently, the team designed a device to measure the force needed to pick a blackberry without damaging it. The data from this device will help inform the next stage of development and move the team closer to the goal of a fully autonomous robotic berry picker. The device was developed by Gunderman, with Yue Chen, a former U of A professor now at Georgia Tech, and Jeremy Collins, then a U of A undergraduate engineering student. To determine the force needed to pick blackberries without damage, the engineers worked with Threlfall and Andrea Myers, then a graduate student.