What is Augmented Farming?

George Varvarelis
3 min readMay 24, 2018


On top of the tractor cabin is Augmenta’s flagship hardware

Taking root around 12,000 years ago, farming has been a major reason for the existence of man. For many many years, farming activities were carried out using crude implements such as; the hoes and cutlasses.

These were able to cater for the world as we know it but, as the years go by, there is need for more food production to meet up with the rapidly growing population. As a result, there is the need to develop high capacity technology that will optimize the quantity and quality of every acre while minimizing the amount of chemicals that go into the fields. This brought about Augmented Farming.

Augmented farming (AF) is a term created by our technology startup “Augmenta and is a farming management concept which is solely based on 1)high definition 2)close range 3)hyper-spectral imagery that can digitally model fields. Augmented Farming(AF) tries to combine Augmented Reality with Precision Farming and is the only method to date that can provide to the farmers precise information such as; fungus appearance, biomass indices, leaf patterns/number and height of plants among others, in a sustainable way.

What instantly come to mind is whether we need yet another subsection in order to describe something which is pretty similar, at least in theory, to precision agriculture. It is a common misconception that, Precision Agriculture is about high technology and next generation gadgets of all sorts that help optimize agricultural tasks.

Although this is accurate to a certain extent, Precision Agriculture is much more than that; it is first, a way of thinking and then anything else. It is the different treatment that our forefathers applied to each piece of their small fields, because they knew their land inside and out without having any high-tech equipment whatsoever.

After the industrial revolution, the farms became bigger and bigger and the “Precision Agriculture” as it was, seized to exist, due to inefficiency. Instead, tractors, agricultural machinery emerged and provided to the farmer a way to actually exploit all this land at once, in a sustainable way and ultimately make a great profit.

At this period, farmers were irrigating, fertilizing and spraying pesticides with averaging techniques to their whole farms, which was still very profitable. After the years passed, in the 90’s, due to the constant increase in demand, farmers had to figure out ways to increase their yield quantity even more and that is where Precision Agriculture as we know it today was born! Drones, Satellites, various sensors across the field provided spatial variability data which in some cases were translated in real time control of agricultural machinery.

For instance, Variable Rate Fertilizer Applications (VRFA) like N-Sensor, giving a certain amount of precision in applying water and chemicals to the field, not in an average way, but in zones of different crop population/condition. Additionally, recent work in Precision Agriculture has used yield maps as keys to identifying spatial variability in crop production systems without generally questioning the resolution at which yield maps are generated. Present day yield maps that use the Global Positioning System (GPS) are generated at a resolution of approximately 30x30 ft.

The exact size is defined by the width of the combine header. In general, the Precision Farming resolution has not been driven by what was agronomically needed or economically advantageous, but by what was technologically available.

Furthermore, using conventional sensors, someone needs a lot of units to scan a wide area while with imaging spectroscopy you could use computer vision processing to split the image in fields of view and obtain composite information. After 2015, when all these technologies had been tested and used by many farmers, there was still the problem of the rapid increase in the demand of food production and along with the rapid advancement of Machine Learning and Big Data processing technologies, it has become possible to consider that high precision and sustainability could in fact be together for the first time.

We are now able to replace conventional sensors which are needed in volume in order to scan wide area of the field with imaging spectroscopy and computer vision so that the images can be split in different fields of view and obtain composite information more quickly and efficiently than ever before.

That is exactly what Augmented Farming (AF) is and it is the future of Agriculture.