The pressure put on the agriculture sector is larger than ever. Global demand for food is rapidly increasing. Climate change is an ever growing concern. Farmers are now looking for any possible way to increase their efficiency and sustainability. Big data plays a big part in this transformation.
Big data is defined as an extremely large data set that is used to identify patterns, trends, and associations. For example, Amazon collects data on its millions of users every day. It is constantly analyzing that data and using it to present shoppers with products they may be interested in.
When it comes to agriculture, agtech sensors and software all collect data on farming operations. Whether that be yield information, input usage, weather forecasts, or more. That data is then analyzed to present farmers with data driven suggestions to improve their operation.
John Deere is one of the pioneers in big data for agriculture. Each of its large agricultural machines are equipped with sensors that collect all kinds of data. Millions of data points on machine health, crops, weather, and soil are stored and analyzed to provide farmers with valuable insights. John Deere’s semi-autonomous machinery is all run by big data. Artificial intelligence guides the drivers down precisely defined lines so there is no overlap and no wasted efficiency.
Schneider Electric and SCADAfarm partnered to develop an IoT system to cut back water and electricity usage on a farm while increasing yields. The system connects to weather stations, irrigators, and sprinklers, allowing farmers to monitor and control their equipment from anywhere. It gathers mounds of data on equipment performance, moisture levels, and weather forecasts. The data is all analyzed to give farmers insight into which areas need attention. Farmers can then control the entire watering cycle from their computer or device.
IBM has built a suite of agtech tools to bring artificial intelligence to farming. The system aggregates terabytes of geospatial data and analyzes it to present farmers with data driven decisions. One of the features boasts the ability to predict yields for corn crops up to three months in advance. Another feature uses hyper-local weather forecasts to predict the risks of disease in a farmer’s corn fields. There is even a tool that uses soil moisture, satellite imagery, and weather models to simulate changes in soil moisture. These massive sets of data are analyzed with artificial intelligence and machine learning to help farmers grow more sustainably.
In the feedlot industry, HerdWhistle is the only system capable of collecting feeding behaviour data on a full operation. It collects millions of data points on feeding and drinking behaviour and presents feedlot operators with data driven insights into cattle health, production, and day-to-day operations. Feedlot operators can then use the insights to drive performance throughout the operation.
