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Updated: Dec 8, 2025

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields.

Vignesh Raja Ponnambalam¹, Marianne Bakken^1,2, Richard J D Moore²

¹Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway.

Sensors (Basel, Switzerland)

|September 17, 2020

Summary

This study introduces a visual guidance system for strawberry farming robots using AI. The system accurately navigates uneven terrain, improving precision agriculture and sustainable food production.

Keywords:

automation and robotics deep learning image processing navigation and guidance

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Last Updated: Dec 8, 2025

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Area of Science:

Agricultural Robotics
Computer Vision
Artificial Intelligence

Background:

Precision agriculture relies on automated robotic platforms for sustainable food production.
Agri-robots need accurate guidance systems for navigation, with machine vision offering a flexible alternative to lidar or RTK-GNSS.
Visual crop row guidance faces challenges due to variations in crop appearance, spacing, and row contours.

Purpose of the Study:

To develop a robust visual guidance pipeline for agri-robots in strawberry fields.
To address challenges posed by uneven crop row contours in hilly agricultural regions.
To improve the accuracy and reliability of autonomous navigation for agricultural robots.

Main Methods:

A visual guidance pipeline based on semantic segmentation using a Convolutional Neural Network (CNN).
Segmentation of RGB images into crop and drivable terrain regions.
Development of an adaptive multi-Region of Interest (ROI) method for trajectory fitting to uneven crop rows.

Main Results:

The developed pipeline effectively segments strawberry fields for robot navigation.
The adaptive multi-ROI method successfully fits trajectories to uneven crop row contours.
Open-loop trials with a real agri-robot demonstrated favorable performance compared to traditional guidance approaches.

Conclusions:

The proposed visual guidance pipeline offers a viable solution for agri-robot navigation in challenging environments.
The system enhances the capabilities of robots in precision agriculture, particularly in strawberry cultivation.
This approach contributes to the advancement of autonomous systems for sustainable farming.