Invited Presenter

R.D. Horsley ¹, S. Atanda ², D. Murillo Florez², M. Souza², and A.M. Heilman Morales²
1. Department of Plant Sciences, North Dakota State University
2. North Dakota State University Agricultural Data Analytics
Corresponding Author: Richard Horsley, richard.horsley@ndsu.edu

Horsley, Richard

Richard.Horsley@ndsu.edu

Public agricultural research programs excel at collecting data; however, there is a gap from collection to analysis that could be addressed by using tools and technologies that support interoperability for quicker decision-making. Incorporating digital solutions that connect experimental design, data analysis, and predictive modeling would allow researchers without programming skills to manage their experiment design and analysis, as well as develop predictive models for genomic or phenomic selection using high-throughput phenotyping (HTP) data. The tools include FieldHub, MrBean, PredictPro, and AgSkySight. FieldHub facilitates the design of field and greenhouse experimental trials. Its web-based interface makes it easy to create simple or complex designs of experiments (DOE). For example, the spatial design with repeated checks in FieldHub works well for mist-irrigated FHB nurseries. Analyzing this type of data is regularly done with MrBean, an application that combines descriptive analysis, measures of dispersion and central tendency, visualizations, and linear mixed models (LMMs) analysis for single- and multi-environment trials. Additionally, PredictPro helps develop, validate, and deploy predictive models for genomic or phenotypic selection, using statistical-, machine-, and deep-learning algorithms. Lastly, AgSkySight converts image data into vegetative indices for further analysis. When used together, FieldHub, MrBean, PredictPro, and AgSkySight create a fully connected analytics ecosystem that enhances transparency, interoperability, and scalability for genomic selection and HTP prediction. This approach aligns with the goals of the U.S. Wheat and Barley Scab Initiative (USWBSI), demonstrating that open collaboration and shared digital tools can accelerate genetic gains and improve resilience in modern agricultural research.

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