August 5, 2019

And who will win a prominent spot on the playing field?

By Barney Bernstein, Senior Associate 

It’s strange to think, but 10 years ago I questioned if we had the capability to manage big data sets and use predictive analytics to drive decision making on the farm. 

Today, I can’t imagine NOT pursuing decision making on the farm without leveraging big data sets and predictive analytics.

Big data means data sets and calculations too large for today’s processors to handle in a single process. Data manipulations need to be handled in manageable chunks and the results weaved back together. The Oxford dictionary defines big data as “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.”

Handling big data sets using “artificial intelligence” is not simply a “nice thing to have”—it is a requirement in agriculture going forward, perhaps even a necessity for survival.

But why? For starters, we need to tap every resource possible to meet the demands of a growing population, and artificial intelligence is one key to our success. Furthermore, many of the most promising technologies to emerge in recent years—all of course designed to make farming more efficient to meet the demands of that growing population—cannot reach their full potential without leveraging artificial intelligence and predictive analytics to manage very complex relationships.

Helping Feed the World

As an industry, we have a shared mission to fulfill the nutritional needs of a world population predicted to surpass 9 billion by 2050. We all must make contributions to producing more food while making less of an impact on the earth. Farmers are the greatest stewards of the land; and as ag professionals, we also must be good stewards and bring the technologies to farmers that can help them continually improve their environmental stewardship and productivity.

That means being more precise with inputs, following practices to diminish erosion, improving growth rates of plants, and reducing fuel used to grow crops, among others. With artificial intelligence, there’s hope we can accelerate technological advancements needed to excel in these areas.

Which leads us to the second reason why ag needs artificial intelligence…

Conquering Our Data Challenge (and actually putting that data to work)

We are data-challenged in agriculture. Technologies today are able to collect trillions of data points in seconds; but, without adequate tools to decipher it, all you’ve got is a mountain of useless data. This mountain is more than any human brain can analyze, so we have no choice but to rely on computers to do the job. The sophisticated computations and algorithms generated through artificial intelligence are the only way to conquer it.

Farmers go through trial and error season after season—an inefficient and costly guessing game. The promise of predictive and prescriptive analytics, enabled through artificial intelligence, is that it can unearth recommendations that will make the operation more profitable. This same data science can be applied to companies looking to improve their go-to-market strategies. Ag businesses can use predictive and prescriptive analytics to convert transactional data into better forecasting and targeting, or use it to improve logistics planning—like pinpointing the best time to deliver fertilizer based on weather conditions in the region. These analytics won’t make the decision for you, but they will help get you in the ballpark.

What’s Stopping Us?

Let’s talk about all that data. Can you even fathom that despite an infinite supply of data, it’s still not enough? The fact is we need more data to improve the accuracy of the results from artificial intelligence algorithms.

The way artificial intelligence works is that machine learning algorithms process the data to identify meaningful trends and possible outcomes. They find correlations that are closer to statistical fact than human logic and reasoning—correlations that would not likely be discovered by a human mind.

Machine learning is powered by data. The more you feed it, the more it learns and the more accurate it becomes. Data teaches the application how to think so it can predict trends and future outcomes, which, in theory, should lead to more effective decision making. This sounds wonderful, but is it working? As Sam Eathington, Chief Science Officer for Climate Corporation, recently said in an article, “Put their benchmarks in context, run split-planting trials, and get to know their farming data.” Data science has not yet cracked the complexity of what hybrid works best on a particular field. There is complexity that artificial intelligence needs to filter through, even with extremely large data sets. You have to ask: Is the data quality good enough? Is the data science robust or mature enough to predict correctly for extremely complex data sets?   

Results thus far have been underwhelming, showing little benefit beyond being a great tool for looking at data. And without results we can’t overcome the next hurdle: Getting farmers to adopt it.

As with any new technology, growers look at artificial intelligence with cautious optimism—guarded, but willing to try if they trust you. Entira has talked to many growers and has found very few who are thrilled with the predictive tools of today’s platforms. Growers use these platforms to keep their data and generate yield maps, which they may review with their trusted advisors to determine ways to help manage costs. But, beyond creating interesting visuals, today’s tools have not been a rousing success. None are great at actually predicting specific actions a grower should take to reach their goals.

There are countless players coming onto the scene with solutions and technologies that are piquing the curiosity of growers, industry and investors alike. What will it take for them to win?

Building the Market

The thing about markets for new technology is that it takes time for the playing field to be set. It has certainly caught the attention of Silicon Valley, where agriculture is now one of the top investment categories for major technology firms, and billions of dollars are being allocated toward agtech startups.

Artificial intelligence is still relatively new, especially to ag, and even the most enthusiastic participants have very limited experience. There are those who lead the way and set standards with innovations, and those who imitate but don’t do it as well. And, there are those who continually improve and advance. As all these contenders hit the sieve, only a few will remain well-positioned to play. Some will join forces. All will have to evolve to stay in the game.

One thing is for certain: To remain viable, companies must invest in data analytics—more specifically, the human capital capable of guiding the science. That’s because the real power comes from the combination of artificial intelligence and human rationalization, and that’s why having a quality staff in place is essential to your success. You must build a team that can formulate your data into something meaningful.

Look inside and outside your organization to build up your team. You cannot rely solely on internal resources—it’s important to reach out to qualified entities that can be collaborative partners. This expertise is not easy to find—not everyone can be a world-class data scientist; having experienced and highly knowledgeable data scientists is critical to success.

Companies have data sets in the billions of lines, but that data is useless without a way to synthesize it into useful information: to find the trends and provide predictive and prescriptive decision tools. Data is a precious commodity and you cannot skimp on the resources to mine it. To win at this game, companies have to make collection of quality data and handling big data a primary focus.

At Entira, we believe artificial intelligence holds GREAT promise for agriculture and we are deepening our involvement in advancing the collaboration of sciences. It’s nothing short of life-sustaining to the future of agriculture and the food value chain. There are huge opportunities for everybody. The best thing we can do is be clear and straightforward in helping farmers and agribusinesses understand the tools available and help put them to work improving their operations.

If you want to learn more about artificial intelligence in the ag food industry and how it might impact your business, contact Barney Bernstein at bbernstein@entira.net or 919.830.6527.