ANTONIO Project // ERA-NET ICT-AGRI-FOOD project
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About

The overall goal of ANTONIO project is the development and implementation of multi-sensor systems and sensor processing algorithms to enable agri-robots to perform plant phenotyping and
precision agriculture tasks, such as precise local application of pesticides/fertilizers and yield estimation. The envisaged idea is based on an integrated sensor network, including mobile sensors mounted on board of ground robots and drones. Information coming from the fixed sensing devices will flag “attention spots” in the crop for further local investigation by the robotic platforms.
ANTONIO's approach will lead to in-field high-throughput crop assessment, and this narrow temporal and spatial scale of detection ability can enable precision farming applications that rely on accurate high-resolution local maps

Variable rate ​
​applications

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​The ANTONIO system will help to apply pesticides or fertilizers 
where it can be seen to be needed, that is treat the specific site instead of the entire crop or field.

Crop monitoring and yield estimation

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Sensing technologies will be applied to monitor 
qualitative and morphometric parameters related to crop composition and development, through spectral analysis and 3D reconstruction to enable closer monitoring of plant health, as well as for yield mapping and yield forecasting.

Controlled traffic
​farming
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Automated online estimation of key parameters of the terrain that affect its ability to support vehicular traffic (e.g., soil compaction, friction, longitudinal and lateral grade, etc.). Such properties are collectively called “trafficability.” Measuring real-time terrain properties make it possible for a vehicle to adapt to the site-specific environment by varying its velocity and suspension system configuration or tire pressure and adjusting the parameters of onboard control and stability systems.

Unmanned Arial Vehicle, UAV


Using a flying vehicle (UAV) to inspect more remote parts of the field enables closer monitoring 
of plant health while minimizing track use.
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Keywords:
  • Precision agriculture
  • Field robotics
  • Crop sensing
  • Sensor fusion
  • UAV
  • Situation awareness
  • Deep learning
  • Decision support system (DSS)
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