Efficient fertilization in open-field leafy vegetable farming is a challenge of modern agriculture. Uniform nutrient applications often result in waste, reduced profitability, and environmental losses. Additionally, frequent monitoring requires time and expertise. Farmers need smarter, data-driven tools to optimize fertilizer use and maintain high yields while minimizing environmental impact.
Pilot Case 3 of the AgRibot project is testing a robotic and AI-based system for precision fertilisation in leafy vegetable crops. This system integrates mobile robots equipped with advanced sensors and AI algorithms for predicting nutrient demands. The platform enables variable-rate fertiliser applications that are dependent on the specific needs of each crop.
On the left, the mock-up structure equipped with advanced sensors including RGB-D cameras, a multispectral camera, LiDAR, GPS, and IMU, forming the first integrated data acquisition system. On the right, the field testing site where the setup is being trialed in real-world agricultural conditions.
Ongoing field trials in Italy are demonstrating that robotic sensing combined with AI modelling can optimise fertiliser application, improving nutrient use efficiency and reducing inputs without compromising yield. Robust connectivity, reliable sensor data and collaboration with farmers are fundamental assets for the success of the pilot. The solution must be practical and adaptable to existing farm routines.
The final system will lead to cost savings by reducing fertilizer use and ensuring compliance with environmental regulations. It will also enhance yield consistency. Additionally, field monitoring and decision-making will become less time-consuming, increasing the farmers’ confidence in sustainable nutrient management. This approach is scalable to other crops and regions, working in the direction of fully data-driven, robot-assisted, and sustainable European agriculture.