iPhone – High-throughput Phenotyping with Smart Phones. #Phenoapp 

PI - Jesse Poland

Kansas State University

CoPI - Mitchell Neilsen

Kansas State University

CoPI - Bruce Gooch

Texas A&M University

CoPI - Michael Gore

Cornell University

 jpoland4  Neilsen  Gooch  Gore



Plant breeding programs must evaluate thousands of candidate varieties to identify and deliver the best high-yielding new varieties to farmers. Novel tools are needed to accelerate this process to meet the growing demands for food, feed, and fiber. One promising approach is to utilize the rapidly advancing technology of unmanned aerial vehicles (UAVs). Through this project, we will implement UAVs outfitted with cutting-edge imaging tools to rapidly assess field trials in wheat breeding programs and extract precise measurements from the aerial images of important plant traits relating to plant health and yield. We will evaluate thousands of field plots of candidate varieties in the Kansas State University and International Wheat Research Center (CIMMYT) breeding programs and use the 'big-data' generated to develop yield prediction models to assist breeders with identifying and selecting the best candidate varieties. We will also use Deep Learning to automatically measure important traits from UAV captured images in ways that are consistent with what an expert breeder would do in the field. This approach will provide an 'eye-in-the-skies' to give breeders additional information for quickly identifying the best new varieties out of thousands of field plots. These approaches using UAVs for rapid measurement of large field trials in wheat breeding developed through this project will be implemented in powerful and breeder-friendly software. These tools will enable breeders to more effectively and quickly identify superior new varieties and deliver them to farmers. The rapid development and delivery of high-yielding varieties is a critical part of maintaining stable food supplies and obtaining global food security.