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Improving agriculture and sustainability with AI

Updated: Jan 31

I grew up in an agricultural family in the 70’s and 80’s, in a world that was already losing touch with some of the basic concepts on where our food comes from and how it is produced. I later became a farmer myself, and I found this increasing distance between food producers and consumers difficult to reconcile. However, I drew comfort from the discovery that in many smaller communities, the bond between ordinary people and where their food came from still existed.


Today, the problem is much worse. Very few people have any real appreciation of how and where their food is produced. I keep asking myself, how are people are going to make and enjoy great food, if they have no connection to the people who are growing that food?


We have become so distant from fundamentals of food production that we start to think of it as a commodity. For goodness sake, we are talking about our nourishment as human beings.


So I began to think about how I could help to make a difference. How I could help to connect myself and others to the people who are actually keeping me alive.


I wanted to help make the process more transparent so that everyone can know the answers to What am I eating, How was I produced, Where and When?


This connection is important for farmers, but it is perhaps more important for the customers, for the ordinary people when buy these products.


Farming is a high stakes profession with many constantly changing variables. Success can be dependent on monitoring those variables that directly influence plant health. For that we need access to the right information to make good decisions. We can only manage well what we can measure.


Measuring and monitoring crop nutrition requirements can be a key difference between an average crop and an exceptional crop. If we don’t track nutritional shortfalls and learn what is happening over the course of the season, it is like having one year of experience thirty times, instead of having thirty years of experience. If we do not understand the reasons for crop success or failure, there is no way we can improve our decisions in the future.


The powerful insights that our technology can provide can be fully unleashed only by tracking nutrient flow to plants throughout the growing season at different growth stages. By monitoring a crop throughout the entire season, we can clearly see when nutrient shortfalls occur, and how those nutrient shortfalls are connected with specific growth stages and/or weather conditions. This information can help us to both react to events faster, and to proactively manage our activities in the future.


Continuous monitoring is imperative to get the greatest benefits from this technology. Data needs to be collected continuously and in a timely sequence to map nutrient flow.


Our approach is to unlock real-time, continuous monitoring to give farmers the information they need to be in the drivers seat, and to be in control over plant health and nutritional integrity.


What does our technology deliver?


  • A breakthrough way to detect mineral and nutrient deficiencies in plants 3-4 weeks before any visible symptoms. This creates the opportunity to apply the precise amount of a needed nutrient before a deficiency manifests as a disease or weakness.


  • Allows you to supply plants with the minerals and nutrients needed for optimum health and growth throughout the entire growing and harvesting season.


  • A proactive solution to help you stay informed about the health and nutrition of your crop through the growing season. With real-time sensors, you will be able to detect nutritional imbalances much earlier than with typical dry matter tissue analysis, and manage your crops nutrition before it begins to demonstrate any challenges.


  • To proactive communicate with your customers, and give them more detailed nutrient information about what you produce.


[This article was ubsequently published by the Data Science Foundation: Improving Agriculture and Sustainability with AI by Nuno Silva (datascience.foundation)]