Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Page 8 Page 9 Page 10 Page 11 Page 12 Page 13 Page 14 Page 15 Page 16 Page 17 Page 18 Page 19 Page 20 Page 21 Page 22 Page 23 Page 24 Page 25 Page 26 Page 27 Page 28 Page 29 Page 30 Page 31 Page 32 Page 33 Page 34 Page 35 Page 36 Page 37 Page 38 Page 39 Page 40 Page 41 Page 42 Page 43 Page 44 Page 45 Page 46 Page 47 Page 48 Page 49 Page 50 Page 51 Page 52 Page 53 Page 54 Page 55 Page 56 Page 57 Page 58 Page 59 Page 60 Page 61 Page 62 Page 63 Page 64 Page 65 Page 66 Page 67 Page 6822 Vasant Honovar, a professor at Pennsylvania State University’s College of Information Sciences and Technology, says artificial intelligence has been in use since the adoption of precision ag methods nearly 20 years ago. What’s different now, he explains, is there are far more ways of compiling the data needed for machine learning applica- tions, and the cost of gathering that information is more affordable. “This relatively recent phenomenon of advances in sensors and computing technology makes it increasingly possi- ble to assemble and analyze these mas- sive data sets. That’s a game-changer,” says Honovar, a member of Penn State’s artificial intelligence research lab. “Once you have these large data sets, you can use machine learning to try and make accurate predictions.” One of the most popular uses of AI in agriculture is predicting pest and disease pressures. In Taiwan, it’s being used to combat oriental fruit flies. ec2ce, a small company based in Seville, Spain, is a pioneer in the field of predictive AI technology for agri- culture. It uses proprietary software analytics and numerical algorithms to analyze field data from individual farms to predict the future. Ricardo Arjona Antolin, ec2ce’s chief technology officer, says his company’s system is unique because it provides a complete methodology that can be adapted to the specific needs and characteristics of each of its customers’ operations and improve overall perfor- mance. In addition, ec2ce’s AI technol- ogy can create predictive scenarios for trading and hedging decisions, gener- ating farm management simulators to Artificial intelligence is an increasingly popular tool in the agricultural sector. Discover what one company is doing to make the technology more accessible for growers. optimize productivity and predictive scenarios to optimize commercial and logistical planning. The company has completed several commercial projects including corn yield forecasts in 13 U.S. states, green- house pepper productivity forecasts in Spain and olive fly pest predictions in Spain and Portugal. Antolin says AI predictive technology can be extremely accurate. ec2ce’s olive fly pest models were applied to more than 50,000 acres this year and had an accuracy rate of over 95 per cent while anticipating the develop- ment of pests by up to four weeks. “Right now, we can say that we are surpassing our customers’ expecta- tions,” he says. “Customers are really satisfied with the results of having such an accurate predic- tive tool, as they can make smarter deci- sions and anticipate the risk,” he says. In addition to being highly accurate, Antolin says the system is scalable, meaning it can be easily customized for both large and small operations. Although he declines to discuss costs, CEO Pedro Carrillo says the system is affordable. He says the cost of the system is far lower than the value of the benefits that it provides to users. “Good decisions increase productiv- ity and minimize the cost of inputs,” Carrillo says. “We see the product evolving from predictive modelling to a decision tool and, in certain appli- cations, as a base to automate deci- sions within greenhouses and trading.” Jim Timlick An Intelligent Solution MENTIONthe term artificial intelligence and it usually conjures up memories of HAL from 2001: A Space Odyssey or R2-D2 from Star Wars. Artificial intelligence, though, is becoming an increasingly popular tool. It’s used on farms to monitor soil moisture, manage crop quality and operate automated irrigation systems. In short, artificial intelligence, or AI, is the use of computational models to replicate some aspects of human intel- ligence and problem-solving. Intelligent