The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Deep learning in agriculture: A survey
Open accessAbstract
Abstract Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
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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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
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Deep learning-based classification of hyperspectral data
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Cited by
A comprehensive review on ensemble deep learning: Opportunities and challenges
Mohammed, A., & Kora, R. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.014 · Cited by 1,127
Using deep transfer learning for image-based plant disease identification
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105393 · Cited by 1,052
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.01.012 · Cited by 1,043
Barbedo, J. G. A. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.08.013 · Cited by 750
The digitization of agricultural industry – a systematic literature review on agriculture 4.0
Abbasi, R., Martinez, P., & Ahmad, R. (2022) · Smart Agricultural Technology · doi:10.1016/j.atech.2022.100042 · Cited by 531
Metadata
- Authors
- Kamilaris, A., & Prenafeta-Boldú, F. X.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 147
- Pages
- 70-90
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2790979755
- Cited by
- 5,133
- References
- 6
- FWCI
- 246.60
- Type
- journal article
- Language
- en
- Open access
- green
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