ArticleData in brief2020
Dataset of annotated food crops and weed images for robotic computer vision control.
Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- TomatoweedDet: a real-field multi-class weed detection dataset and YOLO benchmark for tomato production systems.Frontiers in plant science · 2026Article
- YOLOv12-BDA: A Dynamic Multi-Scale Architecture for Small Weed Detection in Sesame Fields.Sensors (Basel, Switzerland) · 2025Article
- FSEA: Incorporating domain-specific prior knowledge for few-shot weed detection.Plant phenomics (Washington, D.C.) · 2025Article
- Detection of weeds in teff crops using deep learning and UAV imagery for precision herbicide application.Scientific reports · 2025Article
- Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm.Scientific reports · 2025Article
- Agentic AI for smart and sustainable precision agriculture.Frontiers in plant science · 2025Article
- A comprehensive dataset of rice field weed detection from Bangladesh.Data in brief · 2024Article
- Sensing and Perception in Robotic Weeding: Innovations and Limitations for Digital Agriculture.Sensors (Basel, Switzerland) · 2024Review
- YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion.Sensors (Basel, Switzerland) · 2024Article
- Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision.Scientific data · 2024Article
- Crop Identification Using Deep Learning on LUCAS Crop Cover Photos.Sensors (Basel, Switzerland) · 2023Article
- Galinsoga parviflora (Cav.): A comprehensive review on ethnomedicinal, phytochemical and pharmacological studies.Heliyon · 2023Review
- Weed25: A deep learning dataset for weed identification.Frontiers in plant science · 2022Article
- Multi-modal and multi-view image dataset for weeds detection in wheat field.Frontiers in plant science · 2022Article
- Review of Current Robotic Approaches for Precision Weed Management.Current robotics reports · 2022Review
- Resources for image-based high-throughput phenotyping in crops and data sharing challenges.Plant physiology · 2021Article
- Review of Weed Detection Methods Based on Computer Vision.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development of precisely targeted and autonomous robotic weed management systems. A prerequisite of such systems is to create robust and reliable object detection that can unambiguously distinguish weed from food crops. One of the essential steps towards precision agriculture is using annotated images to train convolutional neural networks to distinguish weed from food crops, which can be later followed using mechanical weed removal or selected spraying of herbicides. In this data paper, we propose an open-access dataset with manually annotated images for weed detection. The dataset is composed of 1118 images in which 6 food crops and 8 weed species are identified, altogether 7853 annotations were made in total. Three RGB digital cameras were used for image capturing: Intel RealSense D435, Canon EOS 800D, and Sony W800. The images were taken on food crops and weeds grown in controlled environment and field conditions at different growth stages.
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