BLOOD, SWEAT, & BIG DATA by Matthew Peroni
Regardless of profession or personal interest, it has become almost impossible to avoid the buzzwords circulating the tech industry. Artificial Intelligence, the Internet of Things (IoT), Drones, and Big Data are all common and largely misused or misunderstood terms meant to sound important. Cornell’s department of Computer and Information Science (CIS), like similar departments across the world, has invested time and money into the advancement of these technologies, as evident in the creation of Gates Hall. Recently, these technologies have found remarkable success in one of the oldest and most familial industries to the Cornell community: agriculture. The emergence of “Smart Farming” is coming at a time when human population growth projections estimate a 70 percent increase in the next thirty years. Given the growing world population, and the upward trend in urban living the next decade’s farms will need to be increasingly efficient. This efficiency can only be obtained through the application of modern technologies to farming. Emerging technologies and rigorous engineering research are kickstarting a Green Revolution that will stabilize as it is challenged by the flow of workers to urban living spaces and a growing world population. Farming, as an occupancy, saw its peak in the United States during the
1930s. During this decade, there were over six million farms in the states, averaging about 155 acres per farm. Since then, farming has seen a sharp drop in participation. In 2012, there were approximately 2.1 million farms in the United States, with an average size of 430 acres. As the number of farms has dropped by over 75 percent, the remaining farms have acquired the land left behind by the others, resulting in the emergence of massive farms owned by one family or company. Of course, any given farm can now be more profitable than it could have been eighty years ago, but the managerial responsibilities of such large farms is daunting. Enter Smart Farming. The scope of Smart Farming is quite broad, so this writer caught up with Professor Kifle Gebremedhin of Cornell’s Department of Biological and Environmental Engineering to discuss the topic. The intriguing strangeness of his work is immediately apparent upon seeing his office walls decorated with research papers donning titles such as “The Thermal Equilibrium of Goats.” Originally a civil engineer, Dr. Gebremedhin has found bioengineering to be extremely rewarding because “innovation is at interface of biology and engineering —bringing engineering to life.” While the statement may seem like a bit of haughty hyperbole, Dr. Gebremedhin provided a strikingly convincing argument
regarding the relevancy of his field of study. “The human heart, on average, successfully pumps blood through the body for about 80 years, that’s billions of beats,” he began, “if we try to make a machine to do that, it will break in less than five years.” To understand and model these cellular processes, then, seems immediately relevant to our own engineering design processes. Dr. Gebremedhin’s work falls under the first primary category of smart farming: sensors and continuous data analytics. For example, Dr. Gebremedhin recognized that, in prior research, the sweating rate of cattle has been recorded rather poorly, with measurements being taken every hour or every few minutes. Instead of following this method, his graduate students created a sensor that could be attached to a cow’s skin that would measure the sweating rate continuously. The data collected from this experiment showed something previously undocumented—the sweating rate under constant heat source exposure was periodic. This can potentially impact the way farmers try to cool their herd, making it more efficient, cost effective, and conducive to milk production. Sensors and data analytics are the backbone of precision agriculture. Instead of using the data to design better systems, as in bioengineering, precision agriculture aims to create