Reference health
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
33/33 checkable references clean · checked 2026-07-26
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
12 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 33 checked references that resolve
resolves10.1007/s11119-016-9476-3
Fleets of robots for environmentally-safe pest control in agriculture
resolves10.1111/wre.12307
Is the current state of the art of weed monitoring suitable for site‐specific weed management in arable crops?
resolves10.1016/j.compag.2017.12.032
Evaluation of support vector machine and artificial neural networks in weed detection using shape features
resolves10.1017/S2040470017000206
RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network
resolves10.1109/ISSPIT.2007.4458016
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
resolves10.1007/978-3-642-33709-3_36
Leafsnap: A Computer Vision System for Automatic Plant Species Identification
resolves10.1109/WACV.2015.111
Evaluation of Features for Leaf Classification in Challenging Conditions
resolves10.1016/j.patcog.2017.05.015
How deep learning extracts and learns leaf features for plant classification
resolves10.1109/ICIP.2015.7350839
Deep-plant: Plant identification with convolutional neural networks
resolves10.1186/s12862-017-1014-z
Going deeper in the automated identification of Herbarium specimens
resolves10.1016/j.biosystemseng.2018.04.019
Weed species discrimination based on SIMCA analysis of plant canopy spectral data
resolves10.3390/rs10050761
Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information
resolves10.3390/su9081335
Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features
resolves10.1016/j.jspr.2015.01.006
Hyperspectral imaging to classify and monitor quality of agricultural materials
resolves10.1109/DICTA.2015.7371274
In Situ Leaf Classification Using Histograms of Oriented Gradients
resolves10.1007/s11831-016-9206-z
Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review
resolves10.1007/978-3-319-24027-5_46
LifeCLEF 2015: Multimedia Life Species Identification Challenges
resolves10.1007/978-3-319-44564-9_26
LifeCLEF 2016: Multimedia Life Species Identification Challenges
resolves10.1007/978-3-319-65813-1_24
LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges
resolves10.1016/j.asoc.2015.08.027
A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method
resolves10.1016/j.compag.2015.12.016
Weed detection using image processing under different illumination for site-specific areas spraying
resolves10.1109/STSIVA.2016.7743317
Weed detection in rice fields using aerial images and neural networks
The 12 references without a DOI — listed, not checked
no DOI — not checkedCommonwealth of Australia. Agricultural competitiveness white paper. ISBN: 978-1-925237-73-3 (2015).
no DOI — not checkedLi, L., Wei, X., Mao, H. & Wu, S. Design and application of spectrum sensor for weed detection used in winter rape field. Transactions Chin. Soc. Agric. Eng. 33, 127–133 (2017).
no DOI — not checkedKrizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS), vol. 1, 1097–1105 (Lake Tahoe, USA, 2012).
no DOI — not checkedAustralian Weeds Committee. Weeds of national significance 2012 ISBN: 978 0 9803249 3 8 (Department of Agriculture, Fisheries and Forestry, Canberra, ACT, Australia, 2012).
no DOI — not checkedGoodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).
no DOI — not checkedChollet, F. et al. Keras, https://keras.io (2015).
no DOI — not checkedAbadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems, https://www.tensorflow.org (2015).
no DOI — not checkedSimonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 2015 International Conference on Learning Representations (ICLR) (San Diego, USA, 2015).
no DOI — not checkedHu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. arXiv preprint arXiv 1709, 01507 (2017).
no DOI — not checkedGlorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 9, 249–256 (Sardinia, Italy, 2010).
no DOI — not checkedKingma, D. P. & Ba, J. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR) (San Diego, USA, 2015).
no DOI — not checkedNVIDIA Corporation. Tensor RT, https://developer.nvidia.com/tensorrt (2018).
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-07-26 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.1038/s41598-018-38343-3"><img src="https://citestamp.com/citestamped/10.1038/s41598-018-38343-3/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.1038/s41598-018-38343-3)