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.
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@article{Update soon,
title={Weed Image Database Development towards Robust Weed Recognition},
author={Lu, Yuzhen and Deng, Boyang},
journal={Update soon},
volume={Update soon},
pages={Update soon},
year={2023},
publisher={update soon}
}
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [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] [paper] [dataset]
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] [paper] [dataset]
dataset info: 385 images, 16 (weeds), HSI
Michael, J. Sorghumweeddataset_Classification and Sorghumweeddataset_Segmentation Datasets for Classification, Detection, and Segmentation in Deep Learning. [scholar] [paper] [dataset]
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] [paper] [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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset] (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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [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] [paper]
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] [paper] [dataset]
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] [paper] [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] [paper] [dataset]
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] [paper] [dataset]
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] [paper] [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] [paper] [dataset]
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] [paper] [dataset] [dataset]
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] [paper] [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] [paper] [dataset]
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] [paper] [dataset]
2023
Amsinckia in chickpeas (Plant et al. 2023) [dataset]
2022
Brownlow Hill Fireweed (Coleman et al. 2022) [dataset]
RadishWheatDataset (Rayner et al. 2022) [dataset]
Emerging ryegrass seedlings in a fallow soi (Leon et al. 2022) [dataset]
2021
Coleman 2021 Datasets (Coleman et al. 2021), including 10 datasets. [dataset]
Wild carrot flowers in canola (Leon et al. 2021) [dataset]
Precision Sustainable Ag 2021 OpenCV Competition (Precision Sustainable Ag. 2021) [dataset]
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