Artificial intelligence has moved beyond being a laboratory curiosity and is now entering, quite literally in field boots, the heart of agribusiness. In the agricultural sector, AI is changing how decisions are made about when to irrigate, when to treat crops, when to harvest, and even when to sell. In the olive sector, a strategic industry for Portugal, this transformation can have a particularly significant impact: higher productivity, less waste, and faster decision-making in a context of unpredictable climate, rising costs, and fierce international competition.
The logic is simple: those who produce in the field can increasingly rely on more than experience or a “trained eye”. Decisions are increasingly data driven. Soil sensors, satellite imagery, drones, climate histories, market data, and predictive models all help anticipate problems and act before losses occur. AI does not replace the farmer but amplifies their ability to interpret reality.
One of the most illustrative cases is irrigation management. In several farms, intelligent systems analyze soil moisture, temperature, evapotranspiration, and weather forecasts to determine the optimal moment for irrigation. This allows water savings, a increasingly scarce resource, and reduced energy costs. In perennial crops such as olive groves, where the balance between productivity and water efficiency is decisive, the impact can be substantial. In Portugal, where water stress is a central issue, this application of AI is more than innovation: it is strategy.
Another concrete application is early detection of pests and diseases. Using images captured by drones or by field-installed cameras and sensors, computer vision algorithms identify abnormal patterns in leaves, fruit, or tree vigor. In olive groves, this can mean detecting water stress, pest attacks, or disease signals before they become visible to the human eye. The value of this early detection is significant: it reduces losses, avoids unnecessary spraying, and protects the final quality of the olive oil.
There is also a third area of growing importance: yield and quality forecasting. In some international initiatives linked to olive cultivation, AI models combine satellite data, soil sensors, and agronomic information to estimate the optimal harvest moment and productive potential. For cooperatives and producers, this information helps organize labor, logistics, olive processing capacity, and sales. In olive oil, where timing influences both quality and price, being able to predict accurately is almost as important as producing well.
The olive oil sector therefore offers a particularly interesting case. In Spain, Portugal (although to a lesser extent), and other Mediterranean countries, projects are already testing AI-based tools to predict oil content, monitor grove conditions, and support farm management. The idea is to transform traditional olive groves into precision olive groves, where each decision is adjusted to the actual behavior of the plant and the ecosystem, thereby saving resources, improving sustainability, and increasing final quality. In Portugal, this path may be especially relevant in regions with higher levels of intensive and super-intensive olive production, but also in traditional groves, where maintaining profitability remains a constant challenge.
However, the adoption of AI in agribusiness is neither automatic nor straightforward. There are clear barriers. Data collection is the first, where independent entities such as producer associations or academic institutions could play a supporting role. Ensuring sufficient data availability is essential to obtain reliable results.
In addition, data quality is a major concern: intelligent models are only as good as the information they receive. Incomplete, inconsistent, or poorly calibrated data can lead to incorrect decisions.
Another barrier is the initial cost, including sensors, software, and technical expertise, which is not always accessible to small and medium-sized producers, a challenge amplified by the large number of smaller-scale farmers. Nevertheless, AI can be particularly beneficial for these producers, as it allows them, with fewer resources, to compete with larger operators.
Still, the opportunities are strong. AI can help Portugal gain competitiveness at several levels: productivity (producing more with less water, less energy, and less waste), sustainability (reducing environmental impact and supporting more efficient agricultural practices), resilience (responding better to droughts, heatwaves, diseases, and price volatility), and international positioning (a country that combines agricultural tradition with technological innovation can add value to its products and strengthen the global image of Portuguese olive oil).
In Portugal’s case, this potential is particularly relevant because the country already has important assets: technical expertise in the agri-food sector, innovative companies, research centers, and a growingly recognized olive oil value chain. If AI is integrated intelligently, Portugal can increasingly compete on precision, quality, and differentiation. This applies to olive groves, but also to other areas of agribusiness, from vineyards to horticulture, livestock farming to forestry management.
The key question, therefore, is not whether AI will enter the field. It already has. The question is who will use it best. In agribusiness, as in the rest of the economy, technology only creates value when it solves concrete problems. And in agriculture that means something very simple: helping the producer make better decisions, earlier, and with less risk. In the Portuguese olive sector, that may be the difference between harvesting olives and harvesting value.
Rute Xavier, Professor at Católica Lisbon School of Business & Economics