One of the areas that are going through the most dramatic change in the break of the Fourth Industrial Revolution is agriculture. Today, more than ever, producers are under huge pressure to increase yields, reduce costs and grow quality products. This is not an easy task and requires dedication, hard work and investments, and the only way to succeed is to make the decisions exceptionally efficient and timely. In the era of satellites, powerful computers and the internet of things, we finally have an opportunity to make decisions based on facts, and the key to success lies in data.
It is astonishing how much data is generated today. In fact, 90% of all data in the world has been generated in the past 2 years. Agriculture is no exception, and in many ways, it is one of the sectors most affected by this data explosion.
A wide range of data sources is now available, and some are particularly valuable for agriculture, including drones, satellites, and ground-based sensors. Today, Earth observation satellites such as the Sentinel series operated by the European Space Agency and the Landsat program operated by NASA, along with numerous commercial satellites, continuously capture imagery across multiple spectral bands and spatial resolutions.
At the same time, sensors have become both affordable and widely adopted. These include instruments that measure soil moisture, weather parameters, leaf wetness, and the chemical composition of the soil, all of which transmit data regularly to the cloud.
Once this data reaches the cloud, the true potential emerges. Artificial intelligence (AI) enables the structuring of vast amounts of heterogeneous data and facilitates the modelling of crop growth under various environmental and management conditions. These models allow for the simulation of crop performance in different weather scenarios, such as typical years, droughts, or excessive rainfall, and enable yield prediction across a range of possibilities.
Beyond simulation, AI plays a vital role in the optimisation of field activities. This includes decisions regarding crop and variety selection, the timing and quantity of fertiliser and pesticide application, the choice of tillage methods, and more.
Importantly, AI is not only a tool for decision support, it also enables the discovery of new knowledge. With advanced model explanation techniques, it is possible to open the “black box” of AI systems and uncover hidden dependencies between key parameters, such as soil type, rainfall distribution, yield, and produce quality. In doing so, we enhance our understanding of plant biology and the intricate relationship between genetic potential and growing conditions, while simultaneously improving the efficiency and sustainability of agricultural production.
As the volume of agricultural data continues to grow at an unprecedented pace, the challenge is no longer simply collecting information, but making sense of it. Artificial intelligence stands at the forefront of this transformation, turning raw data into actionable insights, guiding more precise and sustainable farming practices, and revealing new scientific understanding of how crops interact with their environment. By integrating AI with satellite imagery, sensors, and ground truth data, agriculture is evolving from a practice rooted in experience to one empowered by evidence. This data-driven revolution offers not only the potential to increase productivity, but to do so in harmony with the planet’s ecological limits.
REFERENCES:
- https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
Author: Dr. Sanja Brdar, Cropt
