Digital Agriculture: Expectations, Reality, and the Future

Feb 01, 2023

Exploring the challenges facing digital agriculture and the future direction for technology on the farm.

By Haley Bilokraly
Farms.com Intern

Precision agriculture, digital farming, digitalization, and smart farming. These buzz words sound exciting for the agriculture industry, but how are they really implemented?

Dr. Alex Melnitchouck, Chief Technology Officer at Olds College, addressed these advancements during the 2022 Western Precision Agriculture Conference and Ag Technology Showcase in November 2022.

Recent technological growth in precision agriculture has been successful with the utilization of navigation, sectional control, field logistics, fleet logistics, and remote sensing tools. However, Dr. Melnitchouck believes the industry still has a desire for further development of technology.

Dr. Melnitchouck shared that agriculture has been the slowest industry to take part in digitalization, the process defined as collecting data and using it to make better business decisions. But this inactivity is not without good reason.

Unlike the media or finance industry, who are leading the digitalization process, it’s not easy to digitize farming. As Dr. Melnitchouck pointed out, “How do you digitize wheat, barley, canola, corn, soybeans, and potatoes? You still have to put rubber boots on and grow physical assets.”

While outlining the difference between dreams in agriculture technology and their reality, Dr. Melnitchouck detailed how technology like satellite imagery, real-time moisture measurements, and field weather stations are more complex than they initially seem.

So, what does the future of digital agriculture look like? How can digitalization in agriculture happen when there are many of variables to consider? Find out by watching the full video below.

Video: Seeing the Whole Season: How Continuous Crop Modeling Is Changing Breeding

Plant breeding has long been shaped by snapshots. A walk through a plot. A single set of notes. A yield check at the end of the season. But crops do not grow in moments. They change every day.

In this conversation, Gary Nijak of AerialPLOT explains how continuous crop modeling is changing the way breeders see, measure, and select plants by capturing growth, stress, and recovery across the entire season, not just at isolated points in time.

Nijak breaks down why point-in-time observations can miss critical performance signals, how repeated, season-long data collection removes the human bottleneck in breeding, and what becomes possible when every plot is treated as a living data set. He also explores how continuous modeling allows breeding programs to move beyond vague descriptors and toward measurable, repeatable insights that connect directly to on-farm outcomes.

This conversation explores:

• What continuous crop modeling is and how it works

• Why traditional field observations fall short over a full growing season

• How scale and repeated measurement change breeding decisions

• What “digital twins” of plots mean for selection and performance

• Why data, not hardware, is driving the next shift in breeding innovation As data-driven breeding moves from research into real-world programs, this discussion offers a clear look at how seeing the whole season is reshaping value for breeders, seed companies, and farmers, and why this may be only the beginning.