From robots to AI, there’s a new cutting edge for crops
Advanced robots that pick strawberries. Noninvasive wireless sensors that can detect when a cow is going into labor. Tractors that can be controlled remotely or preprogrammed. Technology now touches almost every aspect of agriculture, fueling greater efficiencies and insights, and helping bolster sustainable practices.
Agtech, proponents say, could even be critical to human survival. With a predicted global population of 9.7 billion by 2050, and with climate change making conventional agriculture more difficult, agtech project leaders are exploring innovative solutions to feed more people more efficiently.
In October, South Australia’s government committed to invest AU$2.4 million to create demonstration farms and startup hubs to help local farmers adopt agtech practices. Such practices could return AU$2.6 billion annually to the local economy, according to the minister for Primary Industries and Regional Development. Land O’Lakes, which has in its network more than 40 percent of the 349 million acres (141 million hectares) under crop production in the United States, launched a multiyear agtech partnership with Microsoft to execute sustainable-practice projects using remote sensing and satellite data. In the same month, Mastercard unveiled a new project to integrate blockchain technology into GrainChain’s global agricultural supply chain tech solution, a decision solidified and accelerated by pandemic-related disruptions, according to GrainChain CEO Luis Macias.
“COVID has proven this is where we need to go,” he told Pymnts. “When you start pulling people out of the equation and putting systems into the equation … at this moment in history, that is something everyone is into.”
PHOTO COURTESY OF KENTUCKY FRESH HARVEST
Workers at Kentucky Fresh Harvest
The pandemic may have put a crimp in agtech project funding, but it didn’t decimate the surging sector, according to global venture firm AgFunder.
*Projected total for first half of 2020
Yet before agtech projects can realize their potential benefits, project teams first have to secure the buy-in of key stakeholders, including agricultural workers who have well-entrenched ways of working.
Kentucky Fresh Harvest has seen firsthand the value of earning stakeholder buy-in. Last year, the organization partnered with agtech software company InData.farm to develop software that monitors, controls and optimizes the light, air and soil conditions inside a US$20 million greenhouse that it opened in 2020. As part of that initiative, the InData.farm team remotely interviewed stakeholders throughout the organization to identify their project requirements, including their data needs. Those insights fueled the team’s decision to create software that could be accessed on touchscreen tablets.
“That would make it as user friendly as possible,” says Mike Braico, CEO, InData.farm, Valencia, California, USA.
To bridge the gap between agriculture and technology, the team launched a two-month testing phase following the software’s beta release in August. During the pilot, the team showed users the software’s benefits by letting agriculture workers use the product and observing how they used it. Then the team made adjustments along the way, such as reducing the number of times a user had to click on the tablet to access certain features.
“We did anything we could to make it easier for them to use it and to improve user acceptance,” Braico says. “And we were able to develop it in just five months because we used agile instead of waterfall.”
—Mike Braico, InData.farm, Valencia, California, USA
Now, Kentucky Fresh Harvest workers no longer have to manually log information about produce. Instead, they scan QR codes to track each item throughout production and distribution. The data gives the organization a complete picture: If tomatoes in one area of the greenhouse aren’t doing as well, for example, lighting can be adjusted or a different tomato variety will be considered. And if there’s ever a food safety issue, the organization can quickly trace that product and know exactly where and when it grew.
In a future initiative for the greenhouse, the InData.farm team will develop and implement machine-learning cameras to recognize and monitor the produce and detect any defects.
“That’s really the next phase in agtech: collecting enough data to make accurate predictions and make good business decisions,” Braico says.