AI is proving to be a valuable asset in the field of agriculture, helping to optimise cultivation methods, improve the efficiency of farming operations and reduce waste while increasing yields.

Here are a few examples of how AI is being used in this sector :

Optimising crops and land management

AI can analyse data from satellites, drones and ground sensors to monitor crops in real time. This makes it possible to optimise the use of farmland by identifying areas that require more or less irrigation, fertilisation or specific treatments.

Weather forecasts and climate models

Using AI models, it is possible to predict future weather and climate conditions, helping farmers to plan their activities, anticipate droughts or floods, and make informed decisions about which crops to plant.

Decision support for agricultural practices

AI can provide farmers with personalised recommendations based on data specific to their farm. For example, by suggesting the crops best suited to their soil, recommending the optimum doses of fertiliser or pesticides, and suggesting effective crop rotations.

Early detection of diseases and pests

Using machine learning, AI can be trained to quickly detect signs of disease or pest infestation in crops. This enables a rapid response to control spread and reduce losses.

Agricultural robotics

AI can be integrated into autonomous agricultural machinery to enable more precise and efficient ploughing, sowing, harvesting and crop maintenance operations, providing farmers with advanced technological solutions to meet the food production challenges of the future.

Water management

AI can help monitor and manage water use based on actual crop needs, saving water and improving irrigation efficiency, contributing to more sustainable management of natural resources in modern agriculture.

Aquaponics and hydroponics systems

AI can be used to optimise growing conditions in aquaponics and hydroponics systems by controlling parameters such as lighting, temperature, oxygenation and so on.

Seed selection

AI makes it possible to analyse large quantities of genetic and environmental data, making it easier to identify the most desirable characteristics in plants. Thanks to this analysis, AI encourages the development of new, better-performing varieties.

Supply chain

AI can be involved in managing and optimising processes in the agricultural supply chain, from storage to distribution, ensuring better traceability of products.