Within Agrifood01, we are developing an advanced system of autonomous agricultural vehicles equipped with AI-driven sensors and algorithms designed to perform agricultural tasks with high precision and minimal human intervention. By optimizing these autonomous vehicles, we not only increase the efficiency of food production but also contribute to more sustainable farming practices. This project is a crucial step in establishing global standards for sustainable agriculture and improving labor efficiency within the sector.

In Agrifood03, we are developing an advanced, electrically powered, and autonomously operating tractor-implement combination to address sustainability and rising labor costs in the agri-tech market. The consortium is working on building 'E-tractors' that we aim to introduce both nationally and internationally. The first step involves building electric prototypes that are extensively tested in a real-world environment. Subsequently, we will further research and develop autonomous techniques. This initiative will not only achieve significant CO2 reduction—estimated at 32,000 tons per year by 2030 for 1,000 tractors, each with 1,000 operating hours—but also ensure a healthy profitability for users and applicators of this new technology, essential for sustainable impact in the sector.

Agrifood05 focuses on the development of a robot that can not only automatically pick apples in orchards but also perform automated pruning. This initiative aims to export a large number of these robotic systems, with the goal of increasing revenue for strategic partners and production companies in the fruit cultivation sector. For distribution and market reach expansion, we will utilize our existing dealer network, which serves as a foundation and will be further expanded to effectively support this technological advancement.

Within this project, we are developing an autonomous fruit cultivation system where tractors navigate orchards independently, equipped with sensors to monitor each tree individually. It uses artificial intelligence to analyze key growth parameters such as growth, blooming, and production and makes real-time decisions for optimal orchard management. The ultimate goal is fully autonomous orchard management, minimizing human intervention. Initial applications focus on monitoring blossom, growth, and production, and experimental research into disease and pest detection and advanced pruning techniques.

The Towered Imaging Sensors (TOIS) project introduces a new method for crop monitoring using cameras from fixed installations. This approach avoids the limitations of satellites (such as cloud cover and sun synchronization) and drones (regulatory and maintenance issues) by utilizing advanced multi- or hyperspectral cameras. TOIS provides high-quality data that are immediately useful for simulations and decision-support calculations. As a proof of concept, this technology is first tested on sports fields in collaboration with the KNVB to improve grass management, and subsequently applied to four demo companies for specific crop monitoring.