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Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Sensor architecture and task classification for agricultural vehicles and environments.

Francisco Rovira-Más¹

¹Departamento de Ingeniería Rural y Agroalimentaria, Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain. frovira@dmta.upv.es

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

Autonomous agricultural vehicles are advancing rapidly due to GPS and electronics. This study proposes a sensor architecture for enhanced safety and operation in smart farming.

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Area of Science:

Agricultural Engineering
Robotics
Sensor Systems

Background:

The increasing autonomy of agricultural vehicles is driven by advancements in global positioning satellite (GPS) systems and computing technology.
Agricultural vehicles are at the forefront of integrating commercial automatic navigation systems among self-propelled ground machines.
Despite progress, challenges remain in sensor selection, data processing, and control strategies for intelligent agricultural vehicles.

Purpose of the Study:

To analyze typical agricultural environments and propose an optimized sensor architecture for autonomous vehicles.
To address the complexity of intelligent vehicle design by focusing on sensor networks and coordination.
To develop a sensor architecture supporting safety, operational information, and automatic actuation layers.

Keywords:

intelligent vehicles off-road autonomous vehicles precision agriculture robotics sensor architecture

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Last Updated: May 26, 2026

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Main Methods:

Analysis of diverse agricultural field environments and operational situations.
Proposal of a sensor architecture integrating four subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception.
Design of an architecture aligned with three functional layers: safety, operative information, and automatic actuation.

Main Results:

A novel sensor architecture tailored for autonomous agricultural vehicles has been designed and analyzed.
The proposed architecture effectively groups sensors into specialized subsystems for comprehensive vehicle awareness.
The architecture supports layered vehicle tasks, enhancing safety, providing operational insights, and enabling automatic control.

Conclusions:

The developed sensor architecture is crucial for the future deployment of advanced autonomous agricultural vehicles.
The architecture's success is demonstrated by its implementation and testing in various agricultural vehicles over ten years.
Key strengths include its capacity for redundancy integration and practical incorporation of emerging technologies.