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Related Experiment Video

Updated: Jun 26, 2026

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

Dataset for weed detection in fruit orchards.

Andoni Salcedo-Navarro¹, Guillem Montalban-Faet¹, Miguel Garcia-Pineda¹

¹Computer Science Department, ETSE-UV, Universitat de València, València, Spain.

|December 9, 2025

Summary

This summary is machine-generated.

CampanetaWeed is a new multispectral dataset for detecting weeds in orchards. This resource aids in developing advanced machine learning models for sustainable precision agriculture.

Keywords:

Araujia sericifera Cortaderia selloana Multispectral imaging Precision agriculture Rubus ulmifolius UAV

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Last Updated: Jun 26, 2026

Fruit Volatile Analysis Using an Electronic Nose

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Published on: March 30, 2012

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Published on: February 2, 2019

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Published on: July 28, 2023

Area of Science:

Agricultural Science
Computer Vision
Remote Sensing

Background:

Weed control in permanent crops is expensive and environmentally challenging.
Limited public datasets hinder machine learning model development for weed detection in orchards, especially with multispectral and multi-temporal data.
Existing resources lack the comprehensive data needed for robust cross-spectral and seasonal generalization.

Purpose of the Study:

To introduce CampanetaWeed, a novel multispectral image dataset for weed detection in orchard environments.
To provide a valuable resource for advancing machine learning models in precision agriculture.
To facilitate research on cross-spectral adaptation and seasonal generalization for weed identification.

Main Methods:

Acquisition of multispectral images using a DJI Mavic 3 Multispectral UAV over a commercial fruit orchard.
Dataset includes three flights (October 2023, December 2023, April 2025) with pixel-aligned RGB and four narrow-band images (R, G, Red-Edge, NIR).
Images annotated in YOLOv5 format, covering six weed species and ground disturbance, totaling over 10,000 images and 271,000 labeled objects.

Main Results:

The CampanetaWeed dataset offers high-resolution (0.65 cm/pixel GSD) multispectral and multi-temporal imagery.
Comprehensive annotations enable detailed analysis of weed species and ground disturbance.
The dataset is designed to support the development of robust weed detection models.

Conclusions:

CampanetaWeed provides a unique, multispectral, and multi-season dataset crucial for advancing weed detection in orchards.
The dataset will enable the development of more accurate and robust machine learning models for sustainable precision agriculture.
This resource addresses the scarcity of specialized datasets, fostering innovation in automated weed management.