By Sara Downey, Technology Thought Leadership, Dell Technologies
Keith Bradley grew up on a potato farm in Canada where his family harvested potatoes once a year. He’s still in the farming business, but now he’s the VP of IT at Nature Fresh Farms, one of Canada’s largest independent greenhouse produce growers, where tomatoes, peppers, and cucumbers harvest all year round, thanks to data.
Nature Fresh Farms is using sensors, scanners, video technology, and artificial intelligence (AI) robots to capture and process data at the Edge (computing that’s done at or near the source of the data). As a smart, instrumented grower, it takes every opportunity it can to control the environment and increase its yield from a single vine. Bradley can see how much light a plant is getting, the irrigation levels, nutrients in and out, and when the plant is awake or asleep. Based on this information, his team can adjust the settings remotely.
It’s a far cry from the days of yore, when “traditional farming” was laborious and time-consuming, vulnerable to changes in weather, the ravages of pests, and food waste was normal fare. Of course, there will always be extreme events, like wildfires, droughts, and floods, but with cutting-edge facilities and practices—enabled by data—growers like Nature Fresh Farms have a fighting chance to minimize their environmental impact, and costs, while continuing to feed the nation.
As such, today’s Nature Fresh Farms’ growers are primarily data scientists applying data to fresh produce, rather than traditional growers trying to grapple with data. Based on the insights they glean from AI robots, they don’t use pesticides. Instead, they gather intelligence on what bugs might be attacking their crop and which good bugs could be introduced to kill the bad bugs. They also capture and tabulate pollination data to ascertain when to release its fleet of bees.
By automating its greenhouse and putting data at the center of its operations, Nature Fresh Farms’ yield is now 12-16 times more per square meter compared to when it only harvested fruit and vegetables once a year.
Similar to farmers, a growing cohort of winemakers are harnessing data to create premium wines, starting with the premise that the perfect wine requires the perfect grapes.
Increasing numbers of winemakers around the world, from Canada and the U.S. to Germany, South Africa, and Lebanon, are using sensor technology to identify the best grapes, determine which grapes might be untainted (for instance, not affected by wildfire smoke or downy mildew disease), exactly when they should be harvested (getting the timing wrong, even by a few days, can make the difference between a fresh, fruity wine and a flabby, overripe wine) and their phenolic ripeness (the condition of the tannin in the seeds and skins of the grape).
Creating the best microclimate is also important. More winemakers are now using data captured at the Edge with sensors in the ground to measure soil water content, on the vine to measure stem water potential in real-time, and in the sky to capture thermal imagery. Plus, they’re using data to aid the fermentation process, from cameras able to scan hundreds of times more grapes than the human eye, to sensors in the tank to measure liquid density, sugar content, and alcohol. They can then tier and store the data to look back and identify new opportunities to tweak the process further.
Over the last decade, Internet of Things (IoT) sensors in winemaking have given rise to the “Internet of Vines.” However, the latter day focus on managing data and applying machine learning, AI, and neural networks to improve decision-making, vine by vine, has become the real game-changer.
By relying on data rather than visual observations (which can be misleading), winemakers are producing wine we want to raise a glass with, while incurring far less risk and waste, including water waste.
Sustainable Agriculture, Powered by Emerging Technology
Given that today agriculture consumes 70 percent of our fresh water, finding ways to reduce water waste is a modern day imperative. Feeding an ever-growing global population while conserving valuable natural resources will be one of society’s greatest challenges in the coming years and decades. Smart farming and smart winemaking—indeed smart anything—are our best routes to conserving natural resources.
For instance, one vertical farming customer is using diverse data sets generated by connected sensors via an IoT solution. It’s also employing advanced data analytics technologies that leverage AI to grow pesticide-free food that’s bursting with flavor. Effectively, by embracing precision farming it uses 95 percent less water and claims to be 390 times more productive than conventional field farms.
At the epicenter of these innovations is data, which used to be hemmed in by single applications or ecosystems, stored in silos, and marooned in outlier locations. Now data is part of a distributed computing architecture. This shift is bringing compute closer to the source of data and accelerating the switch from data to insights. With data at the Edge, large data sets are being analyzed in fractions of a second and AI is finding patterns and solutions quicker than ever before.
Putting the “Smart” in Everything
Data science isn’t just the preserve of agriculture. In the data era, it’s a vital tool for all business. According to the Digital Transformation Index, 91 percent of senior business decision-makers across 12 sectors agree that extracting valuable insights from data will be more important for their business than ever before. Meanwhile, 85 percent believe that in next three-to-five years, businesses will use AI and data models to predict potential disruptions, so they can mitigate disruptions.
Of course, some companies will always be laggards, clinging to old-school methods and plain intuition. Increasingly, however, enterprises of all shapes and sizes—including farmers, winemakers, healthcare companies, stock exchanges, among others—are coming to the fore to transform production in far more efficient (and in some cases delicious) new ways.