ArticleFrontiers in plant science2022
Weed25: A deep learning dataset for weed identification.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Partially occluded weed classification using vision transformers and convolutional neural networks for precision agriculture.Scientific reports · 2026Article
- HybridViT-CAB: a vision transformer and convolutional attention network for precision weed detection in agricultural systems.Scientific reports · 2026Article
- OpenPlant: A Large-Scale Benchmark Dataset for Agricultural Plant Classification Using CNNs, ViTs, and VLMs.Plants (Basel, Switzerland) · 2026Article
- A real-time mobile aquatic plant recognition algorithm based on deep learning for intelligent ecological monitoring.Scientific reports · 2026Article
- The Fungus HL-29: A Promising Weed Pathogen with Bioherbicidal Potential and Crop Safety.Journal of fungi (Basel, Switzerland) · 2025Article
- YOLOv12-BDA: A Dynamic Multi-Scale Architecture for Small Weed Detection in Sesame Fields.Sensors (Basel, Switzerland) · 2025Article
- Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm.Scientific reports · 2025Article
- An Indian annotated weed dataset for computer vision tasks in precision farming.Data in brief · 2025Article
- Real time weed identification with enhanced mobilevit model for mobile devices.Scientific reports · 2025Article
- Aerial image segmentation of embankment dams based on multispectral remote sensing: a case study in the Belo Monte Hydroelectric Complex, Pará, Brazil.PeerJ. Computer science · 2025Article
- Automated weed monitoring and control: enhancing detection accuracy using a YOLOv7-AlexNet fusion network.Frontiers in plant science · 2025Article
- PD-YOLO: a novel weed detection method based on multi-scale feature fusion.Frontiers in plant science · 2025Article
- Manually annotated and curated Dataset of diverse Weed Species in Maize and Sorghum for Computer Vision.Scientific data · 2024Article
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7 authors.
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Abstract
Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models-YOLOv3, YOLOv5, and Faster R-CNN-were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h.
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