Missouri Soybeans: Using Drones to Estimate Yield Loss Following 2,4-D, Dicamba Injury – Video

    One of the most difficult challenges in predicting yield loss from dicamba injury to sensitive soybean is that the amount of dicamba, which contacted the sensitive soybean is unknown. As Ph.D. student Eric Oseland discusses in this 5-minute video, UAV sensors may provide a method to determine yield loss estimates in a field setting.

    Although Eric’s research focuses on known rates of dicamba and 2,4-D injury on soybean, the results allow him to develop a prediction model that is based on soybean symptoms and not the rate of dicamba or 2,4-D that contacted the sensitive soybean. As you will see in the video, some indices were quite useful in predicting yield loss.

    To learn more about this project please leave a question on our social media pages or contact Dr. Bradley (bradleyke@missouri.edu).




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