Ana Heilman-Morales, Ph.D., director of the Agricultural Data Analytics team at North Dakota State University, is leading several projects as part of the Food, Energy, and Water Security (FEWS) initiative.
Agricultural researchers and professionals now have more tools than ever to collect vast amounts of data using advanced technology. However, managing this flood of information presents a new challenge—storing, organizing and integrating it into centralized systems. The goal is to make this data findable, accessible, interoperable and reusable by plant breeders, agronomist and end users like growers, helping them making informed data-driven decisions.
Ana Heilman-Morales, Ph.D., director of the Agricultural Data Analytics team at North Dakota State University, is leading several projects as part of the Food, Energy, and Water Security (FEWS) initiative. These projects aim to develop cutting-edge data technologies that address agricultural challenges in North Dakota and globally. The Ag Data Analytics team is currently working on developing automated data workflows to support AES, Extension specialist, as well as end users. Key projects focus on building efficient data processing systems that help users explore agronomic, phenomic and genomic data while synthesizing and providing insights for crop variety development.
Notable tools being utilized include the ExLibris, PredictPro and AGSkySight apps which support data exploration and data management. The Pest Management App is designed to provide unbiased scientific knowledge on pesticide use.
“What motivates me and our team every day to put these things together is I have the public breeding programs close to my heart,” said Heilman-Morales. “They produce varieties that get to directly impact the farmer. Our teams serve the farmers and by serving the farmers, we are supporting the greater good.”
ExLibris is a querying system that simplifies access to agronomic data by automating data storage, transformation and access. For example, it allows breeders to quickly review field trial data across years, locations and traits, which allows them to gain quicker access to their data. This will help them make faster decision on variety development.
PredictPro is a tool that analyzes omics data (genomic and phenomic) collected from field trials. It equips users with strong performance metrics and rich visualizations that promote thorough model evaluation and interpretations. This tool plays a vital role in streamlining and improving the efficiency of plant breeding programs and other areas that rely on omics data for predictive modeling.
AgSkySight is a software platform designed to streamline the process of drone image stitching and vegetation index calculations for agricultural research. The use of drone technology has dramatically increased in agriculture, and AgSkySight enables researchers to obtain timely insights from drone data, enhancing their ability to monitor and optimize crop performance. The platform solves issues with scalability, vegetation index accuracy and integration with high-performance computing (HPC).
“The development of these types of data pipelines mark the beginning of the digitization of agricultural research, with long-term benefits across agriculture, food systems and natural resources,” said Frank Casey, Ph.D., NDSU Agricultural Experiment Station Associate Director. “These tools allow NDSU’s plant breeders to more efficiently develop the varieties that are uniquely adapted to our climate, disease-resistant and better yielding, helping our farmers have more resiliency and success. Beyond breeding, digitizing, organizing, and applying advanced models to this data drives innovation and improve decision-making across the entire ag industry.”
Currently the team is working alongside other researchers within NDSU, the US and internationally. One of those projects, includes working alongside SciNet scientists to adapt the AgSkySight tool to work with high-performance computing centers. SciNET is the USDA branch aimed “to provide agricultural scientists access to high-performance computing, networking and training,” according to its website. This project has the potential to extend well beyond plant breeding, offering applications to other disciplines that range from grassland management and ecology to assessing land management practices and beyond.
NDSU’s FEWS measurement and monitoring project, led by Aaron Reinholz and Heilman-Morales, is exploring the growing role of sensors in agriculture. The team is focusing on identifying practical applications for IoT devices, which are expected to outnumber humans by 2030. These sensors can provide real-time data on soil, plant health and weather, offering valuable insights for researchers and farmers. NDSU has invested in 4G/5G and LoRaWAN towers at two different research experimental centers in Casselton and Hettinger, which will support the use of low-energy sensors for improved agricultural decision-making across the state.
As technology evolves, the integration of advanced data systems in agriculture promises to transform how we access and use data. NDSU's ongoing data-driven research has the potential to modernize how we address agricultural challenges, improving productivity, sustainability and resilience for researchers, farmers and growers alike. With each step, NDSU is paving the way for a smarter, more efficient agricultural future while preparing the next generation of plant breeders, agronomists, horticulturists, engineers, natural scientists, technologists and entrepreneurs the world needs.
The NDSU Food, Energy and Water Security (FEWS) initiative projects receive funding from the United States Department of Agriculture’s Agricultural Research Service.
