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Updated: Dec 17, 2025
08:02
Robotic Sensing and Stimuli Provision for Guided Plant Growth
Published on: July 1, 2019
8.4K
Dataset of annotated food crops and weed images for robotic computer vision control
Kaspars Sudars1, Janis Jasko2, Ivars Namatevs1
1Institute of Electronics and Computer Science, Dzērbenes str.14, Riga LV-1006, Latvia.
Data in Brief
|June 25, 2020
Summary
Researchers developed an open-access dataset of annotated images for weed detection. This computer vision resource aids in training AI for autonomous robotic weed management systems in precision agriculture.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Precision agriculture requires advanced weed identification for autonomous systems.
- Current AI solutions need robust datasets for accurate weed-crop discrimination.
- Object detection is crucial for targeted weed removal and herbicide application.
Purpose of the Study:
- To introduce a novel, open-access dataset for weed detection.
- To facilitate the training of machine learning models for distinguishing weeds from crops.
- To support the development of AI-driven precision agriculture technologies.
Main Methods:
- Collected 1118 images using three RGB digital cameras (Intel RealSense D435, Canon EOS 800D, Sony W800).
- Manually annotated images, identifying 6 food crops and 8 weed species.
- Captured images under controlled and field conditions across various growth stages.
Main Results:
- Generated a dataset with a total of 7853 annotations.
- The dataset includes diverse visual data for 14 distinct plant species.
- Provides a valuable resource for developing and validating weed detection algorithms.
Conclusions:
- The proposed dataset is essential for advancing AI in weed management.
- Enables the creation of more accurate and reliable autonomous weeding systems.
- Contributes to the broader goals of sustainable and efficient agriculture.
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