Can IT Foster Sustainability In Agriculture?

3 agriculture digitalization projects I took part in

Mattia Bradley
Age of Awareness
Published in
5 min readMay 10, 2021


Photo by Jason Strull on Unsplash

As a matter of fact, we live in an era where having access to the internet makes the difference between making a living or not, for the majority of people. Despite the attempts of portraying life in the countryside as the last stand of a tech-free existence, chances are that nowadays even the remotest of hermits has a smartphone. Let’s get real! As I am writing this, someone somewhere is probably developing an App, which allows a shepherd to remotely herd a flock of sheep while peacefully minding his own business kilometers away. Maybe with a robot-dog in charge of guarding the animals.

Jokes aside, more and more farmers are starting to heavily rely on a variety of programs and models which promise to improve their business while making their life on the field easier than ever before. However, the benefits of this technological wave which is hitting the rural world, are not limited to increase the wealth of the individual farmers, but they are expected to considerably impact the environment as well. A positive impact, for once.

Digitalization in agriculture is mostly led by “Agtechs”, or agribusiness start-ups. The capital invested in 2018 in agriculture digitalization projects, accounted for 1.6 billion USD dollars across 209 deals in Agtech. Such an effort in transferring technological applications to the agricultural field (literally), is explained by the several working areas (spanning from plant to animal sciences and farm mechanization, to name a few) in which innovative technologies can be deployed.

According to Finistere Ventures, the new wave of investments in Agtechs can also be explained thanks to the

cost reductions across life sciences, imagery, computation and automation technologies, which have enabled previously cost-prohibitive toolsets to be applied to agricultural problems — Finistere Ventures, 2018

As we said, the applications are many, but here I would like to report the agriculture digitalization projects I had the pleasure to be part of, in order to give insights about their applicability, from a personal perspective.

1) A new type of modeling…crop modeling!

The Author in an experimental field for crop modeling in France — Photo by the Author.

When I first heard of crop modeling, the first thought that came to my mind was that of a corn plant catwalking and showing off the cobs, amidst a crowd of cheering vegetables. To my disappointment, crop modeling was not as funny, but it can indeed help both the farmers improving their business and, indirectly, protecting the environment as well.

In my experience, crop modeling was used to foresee the impacts of climate change on crop production in Central Europe, on plants like maize, barley and wheat, by giving insight on how crop growth and performance would have been affected. However, we also simulated possible adaptation strategies (for example, the use of different tillage depths, different types of cover crops to be used within an agricultural system etc..) that farmers could implement at plot level, to cope with the negative impacts of climate change.

In this view, the use of models in agriculture can not only foresee the expected yield of crops, based on the future environmental conditions under climate change scenarios, but also explore different field management strategies that can be adopted by the farmers. Strategies that can, for example, help preventing soil erosion, optimizing the crop fertilization or pointing out the best species to crop under the forecasted climate change scenario of a specific area.

2) Apps which save you money and limit pollution

Photo by the Author.

Let’s move on to another application of IT in agriculture, still linked to the developement of models.

If you were a farmer whose crop is being constantly ravaged by a pest, would you like to know when to act, before it is too late? I would!

The deployment on the field of models capable of tracking down the development of a pest, in order to know when it is the most effective time to spray the crop, can be a very useful tool for a farmer, and make the difference between a bad or a good growing season.

In our project, knowing the treatments’ timing was essential, because the product used to fight off the larval stage of the pest was a costly bacteria-based insecticide, which gets quickly inactivated by the sun’s ray and heat.

However, even if the farmer uses products which are not allowed in organic agriculture, by knowing exactly when to spray, he will save money and the environment will be spared the extra pollution resulting from indiscriminate (and most of the times useless) treatments.

3) Facial recognition? No! Weeds recognition!

Photo by Chris Ried on Unsplash

If we can use algorithms to recognize nearly everything, we can as well do so to identify weeds in a cropped field! Why would we?

If you ever had to weed a field, you would guess the answer in the blink of an eye. Weeding by hand is probably the WORST activity to do on a field, especially under a scorching summer sun. So, if there is a robot which can do it for you, why not using it?

However, a robot in charge of weeding a field, might have some problems distinguishing a lettuce from a weed, and end up digging out the wrong one. This means that the robot’s software has to be trained to “uderstand” this distinction and to pick up the right plants. Machine learning is the technique which would save the farmer the agony of watching his field completely destroyed by a botanically ignorant bot.



Mattia Bradley
Age of Awareness

Agronomist and traveller. Passionate about sustainability and philosophy. Admin of blog