The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Weed detection in soybean crops using ConvNets
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Cited by
A survey of deep learning techniques for weed detection from images
Hasan, A. S. M. M., Sohel, F., Diepeveen, D., Laga, H., & Jones, M. G. K. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106067 · Cited by 478
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments
Saleem, M. H., Potgieter, J., & Arif, K. M. (2021) · Precision Agriculture · doi:10.1007/s11119-021-09806-x · Cited by 434
A survey of public datasets for computer vision tasks in precision agriculture
Lu, Y., & Young, S. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105760 · Cited by 382
Review of Weed Detection Methods Based on Computer Vision
Wu, Z., Chen, Y., Zhao, B., Kang, X., & Ding, Y. (2021) · Sensors · doi:10.3390/s21113647 · Cited by 303
Detection and classification of soybean pests using deep learning with UAV images
Tetila, E. C., Machado, B. B., Astolfi, G., Belete, N. A. D. S., Amorim, W. P., Roel, A. R., & Pistori, H. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105836 · Cited by 283
Self-citation
Metadata
- Authors
- dos Santos Ferreira, A., Matte Freitas, D., Gonçalves da Silva, G., Pistori, H., & Theophilo Folhes, M.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 143
- Pages
- 314-324
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2767767563
- Cited by
- 581
- References
- 7
- FWCI
- 24.91
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Weed detection in soybean crops using ConvNets. https://app.cochat.ai/papers/10.1016/j.compag.2017.10.027
- The paper
- dos Santos Ferreira, A., Matte Freitas, D., Gonçalves da Silva, G., Pistori, H., & Theophilo Folhes, M. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2017.10.027
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
