Weed-Datasets-Survey-2023

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# Weed-Datasets-Survey-2023
 Weeds are a major threat to the productivity of many agronomic and vegetable crops. Compared to diseases (25%) and insect pests (20%), weeds cause over 45% of yield losses for field crops (Monteiro and Santos 2022). Deep learning has promised to detect weeds in diverse field conditions, accentuating sufficient data was provided for training.

An updated survey with more up-to-date weed datasets is being moved to the repository: https://github.com/AgFood-Sensing-and-Intelligence-Lab/Weed-Datasets-Survey

## Citation
Please consider cite our work if you find this repo is helpful.
```
@article{deng2024weed,
  title={Weed database development: An updated survey of public weed datasets and cross-season weed detection adaptation},
  author={Deng, Boyang and Lu, Yuzhen and Xu, Jiajun},
  journal={Ecological Informatics},
  volume={81},
  pages={102546},
  year={2024},
  publisher={Elsevier}
}
```

## Contents

* [Weed datasets in Agriculture](#applications-of-gans-in-agriculture)
  * [Public Datasets with its paper](#precision-agriculture)
  * [Public Datasets without paper](#precision-agriculture)

# Weed datasets in Agriculture
## Public Datasets with its paper

**2023**

Rahman, A., Lu, Y., & Wang, H. (2023). Performance evaluation of deep learning object detectors for weed detection for cotton. Smart Agricultural Technology, 3, 100126. 
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=80000007&as_ylo=2020&as_yhi=2023&q=Performance+evaluation+of+deep+learning+object+detectors+for+weed+detection+for+cotton&btnG=)
[[paper]](https://www.sciencedirect.com/science/article/pii/S2772375522000910)
[[dataset]](https://www.kaggle.com/datasets/yuzhenlu/cottonweeddet3)

dataset info: 848 images, 3 (weeds), RGB

Weyler, J., Magistri, F., Marks, E., Chong, Y. L., Sodano, M., Roggiolani, G., ... & Behley, J. (2023). PhenoBench--A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain. arXiv preprint arXiv:2306.04557. [[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=80000007&as_ylo=2020&as_yhi=2023&q=PhenoBench+%E2%80%94+A+Large+Dataset+and+Benchmarks+for+Semantic+Image+Interpretation+in+the+Agricultural+Domain&btnG=)  [[paper]](https://arxiv.org/pdf/2306.04557.pdf)  [[dataset]](https://www.phenobench.org/dataset.html)

dataset info: 2,872 images, 5 (1 weed), RGB

Olaniyi, O. M., Salaudeen, M. T., Daniya, E., Abdullahi, I. M., Folorunso, T. A., Bala, J. A., ... & Macarthy, O. M. (2023). Development of maize plant dataset for intelligent recognition and weed control. Data in Brief, 47, 109030. 
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=Development+of+maize+plant+dataset+for+intelligent+recognition+and+weed+control&btnG=)
[[paper]](https://www.sciencedirect.com/science/article/pii/S2352340923001488)
[[dataset]](https://data.mendeley.com/datasets/jjbfcckrsp/2)

dataset info: 36,374 images/500 annotated images, 2 (weeds), RGB

Kitzler, F., Barta, N., Neugschwandtner, R. W., Gronauer, A., & Motsch, V. (2023). WE3DS: An RGB-D Image Dataset for Semantic Segmentation in Agriculture. Sensors, 23(5), 2713.
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=WE3DS%3A+An+RGB-D+image+dataset+for+semantic+segmentation+in+agriculture&btnG=)
[[paper]](https://doi.org/10.3390/s23052713)
[[dataset]](https://zenodo.org/record/7457983)

dataset info: 2,568 images/annotated images, 18 (10 weeds), RGB-D

Güldenring, R., Van Evert, F. K., & Nalpantidis, L. (2023). RumexWeeds: A grassland dataset for agricultural robotics. Journal of Field Robotics. 
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=RumexWeeds%3A+A+grassland+dataset+for+agricultural+robotics&btnG=) 
[[paper]](https://onlinelibrary.wiley.com/doi/10.1002/rob.22196)
[[dataset]](https://dtu-pas.github.io/RumexWeeds/)

dataset info: 5,510 images, 2 (weeds), RGB

Steininger, D., Trondl, A., Croonen, G., Simon, J., & Widhalm, V. (2023). The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3729-3738). 
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=CropAndWeed+%28Steininger+et+al.+2023%29&btnG=)
[[paper]](https://openaccess.thecvf.com/content/WACV2023/papers/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.pdf)
[[dataset]](https://github.com/cropandweed/cropandweed-dataset)

dataset info:  43,814 images / 8,034 annotated images, 74 (58 weeds), RGB

Dang, F., Chen, D., Lu, Y., & Li, Z. (2023). YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture, 205, 107655. 
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=CottonWeedDet12+%28Dong+et+al.+2023%29&btnG=)
[[paper]](https://www.sciencedirect.com/science/article/pii/S0168169923000431)
[[dataset]](https://doi.org/10.5281/zenodo.7535814)

dataset info:  5,648 images, 12 (weeds), RGB

Gaidel, A. V., Podlipnov, V. V., Ivliev, N. A., Paringer, R. A., Ishkin, P. A., Mashkov, S. V., & Skidanov, R. V. (2023). Agricultural plant hyperspectral imaging dataset. Компьютерная оптика, 47(3), 442-450. 
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=Sorghumweeddataset_Classification+and+Sorghumweeddataset_Segmentation+Datasets+for+Classification%2C+Detection%2C+and+Segmentation+in+Deep+Learning&btnG=)
[[paper]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4593178)
[[dataset]](https://pypi.org/project/HSI-Dataset-API/)

dataset info:  385 images, 16 (weeds), HSI

Michael, J. Sorghumweeddataset_Classification and Sorghumweeddataset_Segmentation Datasets for Classification, Detection, and Segmentation in Deep Learning. 
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=80000007&as_ylo=2020&as_yhi=2023&q=Agricultural+plant+hyperspectral+imaging+dataset&btnG=)
[[paper]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4593178)
[[dataset]](https://data.mendeley.com/datasets/y9bmtf4xmr/1)

Goyal, R., Nath, A., & Utkarsh. (2023, May). IndianPotatoWeeds: An Image Dataset of Potato Crop to Address Weed Issues in Precision Agriculture. In International Conference on Agriculture-Centric Computation (pp. 116-126). Cham: Springer Nature Switzerland. 
[[scholar]](https://scholar.google.com/scholar?hl=en&scisbd=1&as_sdt=0%2C23&q=IndianPotatoWeeds%3A+An+Image+Dataset+of+Potato+Crop+to+Address+Weed+Issues+in+Precision+Agriculture&btnG=)
[[paper]](https://link.springer.com/chapter/10.1007/978-3-031-43605-5_9)
[[dataset]](https://www.kaggle.com/datasets/rajni88/indianpotatoweed-dataset)

Rai, N., Mahecha, M. V., Christensen, A., Quanbeck, J., Zhang, Y., Howatt, K., ... & Sun, X. (2023). Multi-format open-source weed image dataset for real-time weed identification in precision agriculture. Data in Brief, 51, 109691.
[[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=Multi-format+open-source+weed+image+dataset+for+real-time+weed+identification+in+precision+agriculture&btnG=)
[[paper]](https://www.sciencedirect.com/science/article/pii/S2352340923007709)
[[dataset]](https://data.mendeley.com/datasets/8kjcztbjz2/2)

**2022**

Krestenitis, M., Raptis, E. K., Kapoutsis, A. C., Ioannidis, K., Kosmatopoulos, E. B., Vrochidis, S., & Kompatsiaris, I. (2022). CoFly-WeedDB: A UAV image dataset for weed detection and species identification. Data in Brief, 45, 108575. [[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=CoFly-WeedDB%3A+A+UAV+image+dataset+for+weed+detection+and+species+identification&btnG=)  [[paper]](https://www.sciencedirect.com/science/article/pii/S235234092200782X)  [[dataset]](https://zenodo.org/record/6697343#.YrQpwHhByV4)

dataset info: 201 images, 3 (weeds), RGB

Alam, M. S., Alam, M., Tufail, M., Khan, M. U., Güneş, A., Salah, B., ... & Khan, M. T. (2022). TobSet: A new tobacco crop and weeds image dataset and its utilization for vision-based spraying by agricultural robots. Applied Sciences, 12(3), 1308. [[scholar]](https://scholar.google.com/scholar?q=info:SEolHh2fB0AJ:scholar.google.com/&oi=gsb&lookup=0&hl=en) [[paper]](https://www.mdpi.com/2076-3417/12/3/1308) [[dataset]](https://github.com/mshahabalam/TobSet)

dataset info:  8,000 images, 2 (1 weed), RGB

Teimouri, N., Jørgensen, R. N., & Green, O. (2022). Novel assessment of region-based CNNs for detecting monocot/dicot weeds in dense field environments. Agronomy, 12(5), 1167. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Novel+Assessment+of+Region-Based+CNNs+for+Detecting+Monocot%2FDicot+Weeds+in+Dense+Field+Environments&btnG=) [[paper]](https://www.mdpi.com/2073-4395/12/5/1167) [[dataset]](https://weed-ai.sydney.edu.au/datasets/aa0cb351-9b5a-400f-bb2e-ed02b2da3699)

dataset info:  1,147 images, 2 (weeds), RGB

Wang, P., Tang, Y., Luo, F., Wang, L., Li, C., Niu, Q., & Li, H. (2022). Weed25: A deep learning dataset for weed identification. Frontiers in Plant Science, 13, 1053329. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Weed25%3A+A+deep+learning+dataset+for+weed+identification&btnG=) [[paper]](https://www.frontiersin.org/articles/10.3389/fpls.2022.1053329/full) [[dataset]](https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw) (dataset password is rn5h)

Chen, D., Lu, Y., Li, Z., & Young, S. (2022). Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems. Computers and Electronics in Agriculture, 198, 107091. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Performance+evaluation+of+deep+transfer+learning+on+multi-class+identification+of+common+weed+species+in+cotton+production+systems&btnG=) [[paper]](https://www.sciencedirect.com/science/article/pii/S0168169922004082)  [[dataset]](https://www.kaggle.com/yuzhenlu/cottonweedid15)

Mylonas, N., Malounas, I., Mouseti, S., Vali, E., Espejo-Garcia, B., & Fountas, S. (2022). Eden library: A long-term database for storing agricultural multi-sensor datasets from uav and proximal platforms. Smart Agricultural Technology, 2, 100028. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Eden+Library%3A+A+long-term+database+for+storing+agricultural+multi-sensor+datasets+from+UAV+and+proximal+platforms&btnG=) [[paper]](https://www.sciencedirect.com/science/article/pii/S2772375521000289)  [[dataset]](https://edenlibrary.ai/datasets)

Xu, K., Jiang, Z., Liu, Q., Xie, Q., Zhu, Y., Cao, W., & Ni, J. (2022). Multi-modal and multi-view image dataset for weeds detection in wheat field. Frontiers in Plant Science, 13, 936748. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Multi-modal+and+multi-view+image+dataset+for+weeds+detection+in+wheat+field&btnG=) [[paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9443486) [[dataset]](https://github.com/cocococoxu/MMDWWF)

Du, Y., Zhang, G., Tsang, D., & Jawed, M. K. (2022, May). Deep-cnn based robotic multi-class under-canopy weed control in precision farming. In 2022 International Conference on Robotics and Automation (ICRA) (pp. 2273-2279). IEEE. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Deep-CNN+based+Robotic+Multi-Class+Under-Canopy+Weed+Control+in+Precision+Farming&btnG=#d=gs_cit&t=1688155970376&u=%2Fscholar%3Fq%3Dinfo%3AhJxcbQNBK9MJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den) [[paper]](https://ieeexplore.ieee.org/abstract/document/9812240/) [[dataset]](https://github.com/duyayun/AIWeeds-Realisitic-Multi-label-Weed-Dataset-and-deep-CNN-RealTime-Robotic-Weed-Classification)

**2021**

Salazar-Gomez, A., Darbyshire, M., Gao, J., Sklar, E. I., & Parsons, S. (2021). Towards practical object detection for weed spraying in precision agriculture. arXiv preprint arXiv:2109.11048. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Towards+practical+object+detection+for+weed+spraying+in+precision+agriculture&btnG=) [[paper]](https://arxiv.org/abs/2109.11048) [[dataset]](https://github.com/LAR/lincolnbeet_dataset)

Beck, M. A., Liu, C. Y., Bidinosti, C. P., Henry, C. J., Godee, C. M., & Ajmani, M. (2021). Presenting an extensive lab-and field-image dataset of crops and weeds for computer vision tasks in agriculture. arXiv preprint arXiv:2108.05789. [[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=An+extensive+lab-+and+field-image+dataset+of+crops+and+weeds+for+computer+vision&btnG=)  [[paper]](https://www.cs.usask.ca/faculty/stavness/cvppa2021/abstracts/Beck_07.pdf)  [[dataset]](https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/TerraByte_PlantLabImages_2021)

dataset info: over 1.2 million images (indoor) + 540,000 images (farmland) / published 14,000 images, 14 (including crops), RGB

Fawakherji, M., Potena, C., Pretto, A., Bloisi, D. D., & Nardi, D. (2021). Multi-spectral image synthesis for crop/weed segmentation in precision farming. Robotics and Autonomous Systems, 146, 103861. [[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=80000007&as_ylo=2020&as_yhi=2023&q=+Multi-Spectral+Image+Synthesis+for+Crop%2FWeed+Segmentation+in+Precision+Farming&btnG=)  [[paper]](https://www.sciencedirect.com/science/article/pii/S0921889021001469)  [[dataset]](http://www.diag.uniroma1.it/labrococo/fsd/sunflowerdatasets.html)

Ahmad, A., Saraswat, D., Aggarwal, V., Etienne, A., & Hancock, B. (2021). Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems. Computers and Electronics in Agriculture, 184, 106081. [[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=80000007&as_ylo=2020&as_yhi=2023&q=Performance+of+deep+learning+models+for+classifying+and+detecting+common+weeds+in+corn+and+soybean&btnG=)  [[paper]](https://www.sciencedirect.com/science/article/pii/S0168169921000995)  [[dataset]](https://github.com/DigitalAgricultureDiscovery/AgBotSamples)

dataset info: 462 images with bounding box annotations, 4 (weeds), RGB

RV, N., Krishnan, A., Krishnan K, V., & Haritha ZA, S. A. (2021, February). Southern Pea/Weed Field Image Dataset for Semantic Segmentation and Crop/Weed Classification using an Encoder-Decoder Network. In Proceedings of the International Conference on Systems, Energy & Environment (ICSEE). [[scholar]](https://scholar.google.com/scholar?hl=en&as_sdt=80000007&as_ylo=2020&as_yhi=2023&q=Southern+PeaWeed+Field+Image+Dataset+for+Semantic+Segmentation+and+CropWeed+Classification+using+an+Encoder-Decoder+Network&btnG=)  [[paper]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3781351)  [[dataset]](https://github.com/NachiRV/PWFID-Pea-Weed-Field-Image-Dataset)

**2020**

Sudars, K., Jasko, J., Namatevs, I., Ozola, L., & Badaukis, N. (2020). Dataset of annotated food crops and weed images for robotic computer vision control. Data in brief, 31, 105833. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=WeedCrop+%28Sudars+et+al.+2020%29&btnG=) [[paper]](https://www.sciencedirect.com/science/article/pii/S2352340920307277)

Leminen Madsen, S., Mathiassen, S. K., Dyrmann, M., Laursen, M. S., Paz, L. C., & Jørgensen, R. N. (2020). Open plant phenotype database of common weeds in Denmark. Remote Sensing, 12(8), 1246. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Open+Plant+Phenotype+Database+%28OPPD%29+%28Madsenet+al.+2020%29&btnG=) [[paper]](https://www.mdpi.com/2072-4292/12/8/1246) [[dataset]](https://vision.eng.au.dk/open-plant-phenotyping-database/)

Bosilj, P., Aptoula, E., Duckett, T., & Cielniak, G. (2020). Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture. Journal of Field Robotics, 37(1), 7-19. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Crop+weed+discrimination+%28Bosilj+et+al.+2020%29&btnG=) [[paper]](https://onlinelibrary.wiley.com/doi/full/10.1002/rob.21869) [[dataset]](https://lcas.lincoln.ac.uk/wp/research/data-sets-software/crop-vs-weed-discrimination-dataset/)

**2019**

Olsen, A., Konovalov, D. A., Philippa, B., Ridd, P., Wood, J. C., Johns, J., ... & White, R. D. (2019). DeepWeeds: A multiclass weed species image dataset for deep learning. Scientific reports, 9(1), 2058. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=DeepWeeds+%28Olsen+et+al.+2019%29&btnG=) [[paper]](https://www.nature.com/articles/s41598-018-38343-3) [[dataset]](https://github.com/AlexOlsen/DeepWeeds)

dataset info: 17,509, 8 (weeds), RGB

**2018**

Sa, I., Popović, M., Khanna, R., Chen, Z., Lottes, P., Liebisch, F., ... & Siegwart, R. (2018). WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming. Remote Sensing, 10(9), 1423. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Weed+Map+%28Sa+et+al.+2018%29&btnG=) [[paper]](https://www.mdpi.com/1424-8220/18/5/1580) [[dataset]](https://goo.gl/ZsgeCV)

dataset info: 18,746, 3 (1 weed), MSI

Teimouri, N., Dyrmann, M., Nielsen, P. R., Mathiassen, S. K., Somerville, G. J., & Jørgensen, R. N. (2018). Weed growth stage estimator using deep convolutional neural networks. Sensors, 18(5), 1580. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Leaf+counting+%28Teimouri+et+al.+2018%29&btnG=) [[paper]](https://www.nature.com/articles/s41598-018-38343-3) [[dataset]](https://vision.eng.au.dk/leaf-counting-dataset/)

**2017**

Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter, W., Burgard, W., & Stachniss, C. (2017). Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. The International Journal of Robotics Research, 36(10), 1045-1052. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Sugar+Beets+2016+%28Chebrolu+et+al.+2017%29&btnG=#d=gs_cit&t=1688160527334&u=%2Fscholar%3Fq%3Dinfo%3AlqCy_hjSkMIJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den) [[paper]](https://journals.sagepub.com/doi/full/10.1177/0278364917720510) [[dataset]](http://www.ipb.uni-bonn.de/data/sugarbeets2016/)

dos Santos Ferreira, A., Freitas, D. M., da Silva, G. G., Pistori, H., & Folhes, M. T. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 143, 314-324. [[scholar]](https://scholar.google.com/scholar?q=Weed+detection+in+soybean+crops+using+ConvNets&hl=en&as_sdt=0%2C23&as_ylo=2015&as_yhi=) [[paper]](https://www.sciencedirect.com/science/article/pii/S0168169917301977) [[dataset]](https://data.mendeley.com/datasets/3fmjm7ncc6/2) [[dataset]](https://www.kaggle.com/datasets/fpeccia/weed-detection-in-soybean-crops)

dataset info: 400 images (>10,000 image patches), 4 (1 weed), RGB

Giselsson, T. M., Jørgensen, R. N., Jensen, P. K., Dyrmann, M., & Midtiby, H. S. (2017). A public image database for benchmark of plant seedling classification algorithms. arXiv preprint arXiv:1711.05458. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Plant+seedlings+%28Giselsson+et+al.+2017%29&btnG=#d=gs_cit&t=1688160704759&u=%2Fscholar%3Fq%3Dinfo%3AWfmeYzSo0jUJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den) [[paper]](https://arxiv.org/pdf/1711.05458.pdf) [[dataset]](https://vision.eng.au.dk/plant-seedlings-dataset/)

Lameski, P., Zdravevski, E., Trajkovik, V., & Kulakov, A. (2017). Weed detection dataset with RGB images taken under variable light conditions. In ICT Innovations 2017: Data-Driven Innovation. 9th International Conference, ICT Innovations 2017, Skopje, Macedonia, September 18-23, 2017, Proceedings 9 (pp. 112-119). Springer International Publishing. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Weed+detection+dataset+with+RGB+images+taken+under+variable+light+conditions&btnG=) [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-67597-8_11) [[dataset]](https://github.com/lameski/rgbweeddetection)

**2015**

Haug, S., & Ostermann, J. (2015). A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. In Computer Vision-ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part IV 13 (pp. 105-116). Springer International Publishing. [[scholar]](https://scholar.google.com.sg/scholar?hl=en&as_sdt=0%2C23&q=Haug%2C+S.%2C+Ostermann%2C+J.%2C+2015.+A+crop%2Fweed+field+image+dataset+for+the+evaluation+of+computer+vision+based+precision+agriculture+tasks.+In%3A+Agapito%2C+L.%2C+Bronstein%2C+M.%2C+Rother%2C+C.+%28Eds.%29%2C+Computer+Vision+-+ECCV+2014+Workshops.+Springer%2C+Switzerland%2C+pp.+105%E2%80%93116.&btnG=) [[paper]](https://link.springer.com/chapter/10.1007/978-3-319-16220-1_8) [[dataset]](http://github.com/cwfid)

## Public Datasets without paper

**2023**

Amsinckia in chickpeas (Plant et al. 2023) [[dataset]](https://weed-ai.sydney.edu.au/datasets/21675efe-9d25-4096-be76-3a541475efd4)

**2022**

Brownlow Hill Fireweed (Coleman et al. 2022) [[dataset]](https://weed-ai.sydney.edu.au/datasets/24b34712-c31b-4efc-9790-406d1f14d840)

RadishWheatDataset (Rayner et al. 2022) [[dataset]](https://weed-ai.sydney.edu.au/datasets/8b8f134f-ede4-4792-b1f7-d38fc05d8127)

Emerging ryegrass seedlings in a fallow soi (Leon et al. 2022) [[dataset]](https://weed-ai.sydney.edu.au/datasets/c828f20d-9b3b-451a-b1a3-eb35398760da)

**2021**

Coleman 2021 Datasets (Coleman et al. 2021), including 10 datasets. [[dataset]](https://weed-ai.sydney.edu.au/datasets)

Wild carrot flowers in canola (Leon et al. 2021) [[dataset]](https://weed-ai.sydney.edu.au/datasets/c4a80379-afda-4972-b274-82a544addd0d)

Precision Sustainable Ag 2021 OpenCV Competition (Precision Sustainable Ag. 2021) [[dataset]](https://github.com/precision-sustainable-ag/OpenCV_Competition2021/)

Weed-Datasets-Survey-2023

Weed-Datasets-Survey-2023 최신버젼 다운로드

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Weed-Datasets-Survey-2023

Weed-Datasets-Survey-2023 최신버젼 다운로드

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