DOI: 10.5281/zenodo.18293250
title: Augmenta Tractor-Drone Co-Robotics Dataset for Weed Detection

description: 📌 Overview

This dataset contains telemetry and computer vision metrics collected from a Smart Agriculture co-robotics system developed by Augmenta (acquired by CNH Industrial). The system consists of a tractor equipped with a "Field Analyzer" and an autonomous drone (UAV).

The data was collected to train Machine Learning models (specifically XGBoost) to predict the should_fly event—a signal that triggers the drone to launch and assist the tractor when the tractor's onboard cameras are blinded by environmental factors (e.g., sun glare/lens flare).

This work was conducted as part of the MLSysOps project (EU Horizon Europe).

🚜 System Context

The Augmenta system automates the application of fertilizers and herbicides using Real-Time Computer Vision.

1. Normal Operation: The tractor cameras detect weeds and spray precisely.

2. The Problem: When the sun is at a specific angle (e.g., sunset/sunrise), it creates lens flare, blinding the tractor's camera ("Sensor Fault"). The system enters "Safe Mode" and sprays the whole field blindly, wasting chemicals.

3. The Solution: The system predicts this fault and deploys a Drone to fly ahead of the tractor. The drone sends clear weed detection coordinates back to the tractor, allowing precise spraying to continue.

📂 Data Dictionary

The dataset consists of time-series telemetry. The core goal is to predict should_fly using the sensor and performance metrics.




Column Name
Type
Description




timestamp
datetime
ISO 8601 Timestamp of the recording.


quality_indicator_1
int
Confidence metric: Number of data correspondences between samples.


quality_indicator_2
int
Confidence metric: Number of data points used for localization.


field_indicator_1
int
The number of detected weeds in the current frame.


field_indicator_2
float
Fraction of the field frame under environmental variation.


sensor_fault_probability_1
float
Key Feature: Probability of the camera sensor being blinded by the sun (0.0 to 1.0).


environment_sensor_1
float
Ambient light/environmental condition measurement.


processing_performance
float
Average processing speed/performance metric of the vision unit.


success_rate
float
Fraction of successfully processed image frames (0.0 to 1.0).


should_fly
int
Target Variable: Binary flag (0 or 1). 1 indicates the drone should be deployed.


heading
float
Instant heading of the vehicle (radians).


velocity
float
Instant velocity of the vehicle (m/s).


latitude
float
GNSS Latitude.


longitude
float
GNSS Longitude.


altitude
float
GNSS Altitude (meters).


time_since_sensor_fault
float
Time (seconds) elapsed since the last sensor fault




📊 Statistical Summary




Descriptor
Count
Mean
Std
Min
25%
50% (Median)
75%
Max




quality_indicator_1
1053
545.06
145.27
9.0
454.0
531.0
631.0
1051.0


quality_indicator_2
1053
413.88
119.38
64.0
324.0
396.0
492.0
835.0


field_indicator_1
1053
52.12
58.25
0.0
18.0
37.0
66.0
767.0


field_indicator_2
1053
0.015
0.010
0.001
0.008
0.013
0.021
0.086


sensor_fault_prob_1
1053
0.185
0.271
0.000
0.0002
0.086
0.384
0.999


environment_sensor_1
1053
10187.9
8416.8
808.5
2456.2
8959.3
20497.2
25678.0


processing_perf
1053
12.48
1.98
4.99
10.48
13.03
14.44
15.52


success_rate
1053
0.51
0.47
0.00
0.00
0.57
1.00
1.00


should_fly
1053
0.37
0.48
0.0
0.0
0.0
1.0
1.0


heading
1053
0.81
1.66
-3.13
-0.66
1.34
2.48
3.13


velocity
1053
2.72
0.99
0.01
2.22
2.78
3.33
5.25


altitude
1053
276.11
38.97
254.2
255.7
270.9
273.5
443.5




🧪 Collection Methodology





Location: Perivlepto, Volos, Greece (Augmenta Test Field).




Conditions: Data was specifically collected during sunset/sunrise to induce lens flare and trigger the "Safe Mode" (sensor fault) scenarios.




Equipment:



Tractor Node: Standard agricultural tractor with Augmenta Field Analyzer (Cameras + Edge Compute).

Drone Node: Custom UAV integrated with the Augmenta control stack.





Protocol: The tractor performed "Back-and-Forth" scanning of the field. As the tractor turned into the sun, the sensor_fault_probability spiked, triggering the should_fly signal for the drone.



📜 Citation

If you use this dataset in your research, please use the citation generated by Zenodo (located in the right sidebar of this record).

🤝 Acknowledgements & Funding

This work is part of the MLSysOps project, funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101092912.

More information about the project is available at https://mlsysops.eu/