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Big Data in Agriculture: Are we moving towards the fourth agricultural revolution?

Big Data in Agriculture: Are we moving towards the fourth agricultural revolution?

Writing a blog article about big data, -and everything that thrives with them like AI, IoT, ML, etc-, coupled with agriculture has proven to be much more challenging than expected.

This is not because it is complicated, nor because there is a lack of examples and paradigms. This time the “problem” is that in cases like this, “one person’s imagination” can be said to set the limit. For the sake of numbers and visualisation, a quick Google Scholar search (as seen below in the figure) provides more than 4.5 million results on the wider subject, limiting them to almost 2 thousand when wanting the term to be exactly mentioned, and moving up to a quarter of a million when searching for articles that include those terms explicitly. As is understood, there is a vast literature and a great variety to choose from.

For this, we will not focus on specific applications, nor on technological advancements or innovations that will shape the how and challenge the who? The purpose of this article is to share some thoughts and insight(s) on the why?

More than 10 thousand years ago Agricultural Revolution (First, or Neolithic Revolution) transformed humans from hunters and nomads to producers and settlers. Slowly within some thousands of years -but in the blink of an eye for history- this spread globally and allowed humans to increase in population and start to dominate their environments.

Let’s not forget, as well as highlight, that this first Agriculture Revolution, almost immediately gave us wine production.

More recently, a couple of hundred years ago, the Industrial Revolution affected agriculture to an extent that was unthinkable until then. It skyrocketed mass production, allowed storage and conservation of an enormous number of different products, and by turn allowed us to grow in population even more, rapidly, and with high quality of life and high life expectancy too.

Nevertheless, as early as this development is, we have started to experience the trade-offs that this “move” brought on the evolution “chessboard”. Land and ocean pollution, climate change, air pollution, unhealthy and/or low-quality food products, together with a fast-growing and demanding global population accelerates problems.

This brought skepticism to people, a call for action to the governing authorities, and a higher demand for quality and sustainable food products in societies and the economy. Bio, organic, natural, protected areas agriculture, name type, and geographical protection products have emerged and promoted and accepted by high shares of the population. The EU implements the “farm to fork strategy” to ensure quality and healthy food production and consumption in the EU, and companies follow or are eager to follow this paradigm-shifting their practices quicker than ever before. Technology and research have a share of the lion on how we got here, however, as written at the beginning this is not the purpose of this article… a Google Scholar search can provide a lot of material to dive into this.

This small trip down Highlander’s[1] memory lane brings us today. Big data and the applications that come with them are identified to be a turning point for agriculture and food production. Smart farming is in the epicenter of sustainable and adequate production, as well as improved quality food with the minimum environmental footprint (minimum negative trade-off). This goes for institutions, organisations/companies, and researchers of course; but what about the key enablers, what about the producers? Farmers in the end are the ones that will decide!

Today the ball is in the hands of the producers. Farmers can take advantage of the momentum and the actual technological developments to improve their product and production, and to ease their (commonly accepted) hard line of work. However, there are key challenges to be addressed before this is remotely possible to be implemented at a large scale. With one psychological and one practical being high on the list.

The fear of change (fear of new), as the psychological challenge to address is linked to habits, lack of knowledge, but also to experience from past “mistakes” as briefly noted before about the trade-offs that came from the Industrial Revolution (with focus on the agricultural sector). Just imagine a salesperson, or even a researcher, or even his/her favourite son or niece, going to a farmer and telling him to adopt Artificial Intelligence practices, satellite data, and so on, to improve production. I, who am just writing these thoughts felt skeptical for a moment, how this will go with a person who works with the land and animals in specific ways and practices daily for decades? For this to be addressed, information and education, as well as rules and limits is the simple answer. When Prometheus[2] brought fire to the people from the Gods to improve their lives, ignorance got them burned, and misuse made bombs. However, the fire itself brought evolution and survival.

The ability to use, as the practical challenge to address I would say, is the most important and the one that bears the weight to everyone except the farmers. All the developments that come from research and innovation, simply set, need to be user-friendly and user-oriented, and this is the main target of project SmartVitiNet among numerous others that are supported by the EU/EC.

For the reader who reaches the end of this article and is left with gaps, questions, willingness to know more, and/or criticism the author will be happy to facilitate further discussion(s) on the subject.

Nevertheless, some terms, assumptions, and influences can assist and enrich this opinion blog article’s purpose, quoted/cited below:

  • Big Data: “The idea is that we can learn from a large body of information things that we could not comprehend when we used only smaller amounts[3].” Basically, the breakthrough is not the existence of the data, but rather the ability technology gives us nowadays to gather and analyse them.

  • Artificial Intelligence and Machine Learning (AI/ML): “The capacity of computers or other machines to exhibit or simulate intelligent behaviour; the field of study concerned with this. In later use also: software used to perform tasks or produce output previously thought to require human intelligence, esp. by using machine learning to extrapolate from large collections of data. Also, as a count noun: an instance of this type of software; a (notional) entity exhibiting such intelligence. Abbreviated AI[4].”

  • Agricultural revolution's role in humankind: “The agricultural revolution gave birth to civilization. Agriculture, being the primary keystone of human existence, has played an important role in global economic development. The contribution of agriculture to the total world economic output is 4%. Additionally, it is a major source of employment for approximately 1 billion people worldwide, the primary source of raw materials for various industries, and has an important role in controlling environmental conditions on a global scale[5]

  • Industrial Revolution role in agriculture: “During the last 200 years, industrial production methods became the mainstay of agriculture. Machines such as tractors began to undertake tasks that were previously performed by muscle power, or not performed at all. Fields and animals became vastly more productive thanks to artificial fertilizers, industrial insecticides, and an entire arsenal of hormones and medications. Refrigerators, ships, and airplanes have made it possible to store produce for months and transport it quickly and cheaply to the other side of the world. Europeans began to dine on fresh Argentine beef and Japanese sushi[6].”

  • Perception of farmers to new technologies: “Adoption of new agricultural technologies is always at the center of policy interest in developing countries. In reality, despite the visible benefits of many of the new agricultural technologies, including machinery and management practices, farmers either do not adopt them or it takes a long time to begin the adoption process and scaling up[7].”


More links (in the periodical & Web) to know more about big data in agriculture cases, research, and potentials:

· the-agricultural-industry? 


· with biased emphasis

here on WaltR’s GEMS project 

· with rich information and many more

developments to come.


Information on the author:

Petros Masouridis is a senior Business Development and Project Manager at WaltR (FR). Economist, MSc in Agricultural Economics.



[1]famous 90s movie about immortal with Christopher Lambert, and Sir Thomas Sean Connery 

[3] The Rise of Big Data: How It’s Changing the Way We Think About the World, Kenneth Cukier and Viktor Mayer-Schoenberger 

[5] Girma and Kuma, 2021 Cited in Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence  Ch-15 Machine learning approach for climate change impact assessment in agricultural production 

[6] Chapter 17 – Sapiens: A Brief History of Humankind 

[7] Perception and adoption of a new agricultural technology: Evidence from a developing country, A. Mottaleb 


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