informed decisions made more quickly and accurately despite rapid changes in the environment. It would mean more stable commodity markets as traders act on knowl- edge instead of speculation. It means a mitigation of the environmental impacts of farming as inputs are precisely delivered. Most importantly, it would mean driving the innovation that’s needed to deliver food on the scale needed to feed 9.8 billion by the year 2050. Those are some impressive benefits, but to take advantage of them, our industry must think differently about the role of AI in the fields. The information revolution has begun to transform agriculture, but are we on the verge of something bigger, the artificial intelligence (AI) revolution? All the elements needed to bring AI to agriculture exist, but they must be put together correctly. Data collection systems track information as crops make their way from bags of seed to the fields, and ulti- mately to the supermarket. Each link in the value chain can be closely examined throughout the process. The sheer number of measurements, test results and real-time parameters gathered by the minute far exceed any per- son’s ability to comprehend it all. That’s where data analytics plays a role, absorbing all of this information and making sense of it with an output better matched to human understanding. This might take the form of a digital map that uses red highlights to mark problem areas. Or, the output could be a graph tracking market trends over time. AI takes information processing to the next level. Wouldn’t it be valuable to assess real-time data from the fields and compare and contrast various scenarios to better understand what’s happening in the global market- place? Such capabilities take advantage of machine learn- ing to interpret rapidly changing circumstances. Currently, a commodity trader interested in what’s going on in the United States has to wait for the agri- culture department to release the latest crop figures and related regional information. While these reports are reliable, the data comes from surveys that, in some cases, are filled out on paper and mailed in. Instead of looking at what happened in the past, the AI alternative would give commodity traders a firmer foundation upon which to look ahead. For instance, he might see at a glance that a massive storm threatens to bring severe losses to the corn and soybean crops in Iowa. An AI-powered analysis would convey the percent- age chance of the storm becoming a serious problem along with the impact on commodity prices for each likely scenario. As we all know, forecasting the weather is a dubious undertaking, especially long-range forecasting. AI would work through every conceivable scenario, covering mil- lions of possibilities — more than any single person could imagine. AI would account for potential damage to fields, the impact on each farmer’s pocketbook, the effects on the region’s transportation network and every other relevant factor. It would run through the scenarios at each level of severity and calculate the impact on international trade and the stockpiles of the affected crops on a national scale, as well as the bottom line for farmers on an indi- vidual scale. Traders could decide whether to buy or sell commodi- ties with a firm, fact-based grasp of the rapidly evolving situation. A soup company that depends on the supply of corn from Iowa might line up an alternative source in the event there is a disruption. Growers have the same interest in timely and accurate information on commod- ity price trends. The result is fewer surprises and fewer disruptions in the supply chain. Such an agricultural AI system remains a thought experiment for the moment. The information revolution has made a real-time agricultural data system possible in theory, but it doesn’t yet exist in reality — our informa- tion systems remain fragmented. Stakeholders must see the value of access to coordinated, timely and accurate information before the situation improves. Data analytics have improved agricultural processes in a big way. Plant breeders use analytics to enhance plant genetics using fewer trials than old-fashioned trial-and- error methods. The field of logistics wouldn’t be what it is today without data analytics. AI takes these advances to the next level, but the hot- test research is limited to computer science, medicine, neurology, linguistics and game playing. The latest AI pro- jects aim to replicate human learning processes, interact- ing with the human brain in a way that augments, rather than replaces, human capabilities. There’s a case to be made for pushing AI research into agriculture. Surely ensuring the two billion extra people have enough food to eat in the decades ahead is as important as a cause as harnessing the power of AI for medical diagnosis and disease research. Yet the develop- ment of AI in agriculture is nowhere near as advanced as it is in other sectors. It’s time to develop the pieces needed for the AI of the future to become a reality. That means building the research foundation and developing the data collection systems and infrastructure. NOVEMBER 2017 GERMINATION.CA 43