Thanks to Big Data in Agriculture, BIM Makes Its Way to the (Coffee) Farm
In Colombia, the coffee industry accounts for 25% of gross domestic product in the agricultural sector and generates about 2 million jobs. One of the country’s most important coffee-growing regions is Manizales, located in the foothills of the Nevado del Ruiz volcano in the famous Colombian coffee-growing axis—an area many connoisseurs say produces the best coffee in the world.
This lush landscape is also home to Juan David Hurtado’s family estate. Hurtado is the founder and manager of Blue Bird Coffee, a brand that grows and sells two highly coveted varieties: Castillo and Gesha. Hurtado has a background in engineering, and he wondered if methods from the construction industry could boost his agriculture business. “Every industry has to find answers to the same questions to ensure a business can be profitable,” he says. “Things like space, time, cost, and other variables, such as the environment.”
Hurtado started to build these concepts into his coffee production. “I relied on things like machine learning and business intelligence,” he says. “They basically gather data, turn it into information, and then transform it into knowledge.”
For more than 40 years, Hurtado’s father documented all of the farm’s data by hand—literally with a pen and paper. This information included production figures and prices, so there was plenty of data available to select, analyze, and digitize.
Digitizing Decades of Data
First to be digitized was the map of the estate. Hurtado’s father, who is now 67, kept a hand-drawn map in his pocket, which he used to get his bearings and keep track of which crops were planted in each spot.
Hurtado replaced the pen and paper with Autodesk Revit, and, with the help of his architect brother, he surveyed the topography of their 18-acre property, identifying the altitude of each zone and how many hours of sun and shade each zone gets.
They worked with Autodesk Forge to compile the data that the senior Hurtado had collected on handwritten notes for the past four decades. “The older generations still have the agricultural expertise, and that’s why it’s important to start with what they know,” Hurtado says.
“There are a lot of variables that give each coffee its flavor,” Hurtado says. “You can drink different batches of Colombian coffee and find that each has a distinct taste, even if it’s from the same estate. So you also have to talk to other specialists, like agronomists, to find out the smartest way to process this data.” By combining databases, it’s possible to determine whether coffee that has a better flavor was harvested in a batch that had less shade or some other factor.
This approach improves accuracy in calculating the amount of products required for farm work. “For example, as I know the area and the number of trees, I can calculate how much fungicide I need for a certain area,” Hurtado says.
From Construction to Countryside
The next step was developing an application to gather all of the information and analyze the data: The amount of coffee grown each week, the batch numbers, images, and geolocations. “This way, we bring together geometric and spatial data in a photograph that can be easily stored on a cellphone and in the app,” Hurtado says.
“With Autodesk BIM 360, the data can be saved on a tablet or cellphone, and the software allows me to access the model of the estate,” he continues. “With Revit, we can identify the best places to build certain projects: For example, there are spots where much more sun is needed for drying the coffee beans, which is one of the production phases that characterizes Colombian coffee.”
Hurtado can also consider variables such as the distance between the crops among the trees and the variety of coffee in each zone, as well as the area occupied by the crops. Combining data from other sources, such as weather data, allows for higher-quality production.
“All of this data will allow me to analyze sundeck patterns over the years, the intensity of the rain, relative humidity, and even the temperature,” he says. “The data has to be digital, which makes it a real innovation.”
Next-Gen Single Source
Hurtado’s next goal is to achieve a better interface in his application, allowing for clearer data that’s easier for farmers to use. “The major challenge is to make it more intuitive,” he says. “It’s used by people from a range of backgrounds and isn’t just something for people who work with technology day in, day out.”
He also considers how to handle all this data to upload it to the cloud so that his team can process it and perform relevant analyses. “This way, if I’m in Bogota, and my dad or someone else wants to show me things from the farm, they can send me pictures and a record of what’s being done,” he says.
But this information doesn’t do much good if it stays on the farm, so the next level is sharing it. “This requires more people working on the project to ensure a better end product, with the goal of offering it to the farmers at a reasonable price so as to generate value and make an environmental and social impact.
“All of this is simply taking something that already exists and putting it where it wasn’t before,” he continues. “Agriculture is already moving in a different direction. Today, we want to have better products that are more natural, so technology is increasingly important to take care of crops, especially with issues like global warming.”
Colombia is currently experiencing a third-wave coffee movement that seeks to produce more unique, higher-quality coffees. The digitalization process can help generate increasingly sophisticated blends.
“All of this also helps to change the minds of young people so that they no longer see the countryside as something boring, but as a place full of technology and opportunity,” Hurtado says. “We have to start cultivating this knowledge and take care of it. This is where our greatest challenge lies.”