Farming’s digital revolution has potential for both business and planet to benefit alike. The aim of the transformation is to produce more food from farming with less. That's lower volumes of agrochemicals, less heavy machinery, less water, no additional land, and crucially for the farmers themselves, less time.
Three big developments have rung in the start of the agricultural digital revolution. First, the development of sensor technology that is both astonishingly small and, crucially, cheap. Secondly, the communications technologies required to move data between the field and the computational cloud, and third, the technology to process mind-boggling volumes of information with artificial intelligence (AI) and machine learning.
“This may help a farmer know there is something brewing in the herd or the orchard that needs attention,” says Susan McCouch, director of the Cornell Institute for Digital Agriculture. McCouch predicts that sensors in irrigation systems could be designed to receive signals broadcast from satellites to make decisions about whether to water crops – but only if both the land is dry and no rain is forecast.
“This is how you marry the Internet of Things with the Internet of Living Things, and that needs massive data interpretation capabilities,” she says. “For example, we are currently working with dairy cows, placing nanosensors in the rumen of the cow so that when it is not ruminating in a healthy way, the farmer and the vet can identify which animals are having problems before there are symptoms.”
Up until now most advances in the industry have fallen under what is loosely termed “precision agriculture”, where the advent of GPS and advances in farm machinery have allowed farmers to more accurately sow, treat, and harvest crops, though the focus has typically been on large-scale commodity arable crops such as wheat, soy, and canola (also known as rapeseed). This technology has now been coupled with satellite and drone imagery to monitor weed levels and canopy coverage, soil analysis, weather patterns and historic crop yield data from specific fields which are then fed into data crunching systems which use AI and machine learning to guide on-farm decision making.