Smart Agriculture, My Favorite IoT Success Story Yet

One thing that I’m hearing in many briefings and research interviews is that smart agriculture is becoming one of the most fast-moving verticals for the Internet of Things. Hyper-local soil and climate analysis is being used to optimize planting, and more recently also irrigation has started attracting similar problem-solving attention. For instance, Orange’s M2M/IoT folks announced this week a solution they’ve deployed with Dacom to enhance, among other things, tulip farming in the Netherlands. It reminded me of this study from Chile I saw a while ago. In that, researchers of a Chilean university reported that in blueberry farming a sensor-based approach to irrigation could cut water consumption by 70%, which does sound like a lot.

This matters because agriculture accounts for nearly two-thirds of the water that is extracted globally. Moreover, two-thirds of that agricultural water is actually consumed, unlike in domestic and industrial use where the extracted water can be these days reused very intensively. So in other words, agriculture sucks most of the world’s water and once it does so that water is then gone for ever. I’m a total layman in this field, but to me this appears to make agricultural irrigation a perfect target for exactly the kind of inefficiency-eliminating, waste-busting impact that the IoT is primarily about.

If you need a cheap allegory to remember how this “More for Less” aspect works then I like to use the following. First, imagine a heavily secured black box in a dark basement. The box represents the physical activities that we traditionally haven’t been able to measure and quantify too well – e.g. farming, energy consumption, asset maintenance, and so forth. These activities are thus varyingly data-opaque, but what they also have in common is that if we do them wrong it can all get pretty darn wasteful, often with far-reaching and even irreversible consequences. The IoT in this example is the picklock kit needed for unlocaking the said box – involving networks, sensors, platform software, among others. With these tools the box can be finally opened, and you’ll get an idea of its contents.

But see, this is a three-factor allegory: the basement is poorly lit, so you do need a flashlight to fully understand the contents of the box. That figurative flashlight is analytics, which is needed to shed light on the previously quantified activities. Once this is done, the decisions on those activities can be done more informedly than what used to be possible, which in many cases may allow to achieve more output for less input. Agriculture is a case in point.

As the IoT is starting to visibly transform areas where the stakes are exceptionally high – such as preventing the food and water crises – there’s always a risk of falling into a sort of techno-utopian trap in which each problem appears solvable if one can just throw a right measure of sensors and analytics at it. That may also be the case when it comes to agriculture, which besides technology has also extensive cultural, social and economic dimensions to define the outcomes, and it would be naïve to assume that the technological progress could take place in a vacuum, separate from such complexities.

Still, the potential that the intersection of the IoT and analytics represents in this sector is simply huge. I would love to see more independent cross-disciplinary studies trying to measure its actual impact. How much water could be saved by data-driven irrigation? Or how much could smart agriculture as a whole, realistically, increase the global food output? Ping me on Twitter or drop us a line if you come across any good answers!