The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.
A review of deep learning techniques used in agriculture
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Citations
Cites
1 retracted
RETRACTED ARTICLE: Towards secure deep learning architecture for smart farming-based applications
Udendhran, R., & -, M. B. (2020) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00225-5 · Cited by 32
RetractedReinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,133
Using Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,026
Deep Reinforcement Learning: A Brief Survey
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733
Deep learning models for plant disease detection and diagnosis
Ferentinos, K. P. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.01.009 · Cited by 3,390
Field high-throughput phenotyping: the new crop breeding frontier
Araus, J. L., & Cairns, J. E. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2013.09.008 · Cited by 1,817
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
Fuentes, A., Yoon, S., Kim, S., & Park, D. (2017) · Sensors · doi:10.3390/s17092022 · Cited by 1,572
Drought tolerance improvement in crop plants: An integrated view from breeding to genomics
Cattivelli, L., Rizza, F., Badeck, F.-W., Mazzucotelli, E., Mastrangelo, A. M., Francia, E., Marè, C., Tondelli, A., & Stanca, A. M. (2008) · Field Crops Research · doi:10.1016/j.fcr.2007.07.004 · Cited by 1,433
Metadata
- Authors
- Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P.
- Venue
- Ecological Informatics
- Publisher
- Elsevier BV
- Published
- 2023-11
- Volume
- 77
- Pages
- 102217
- ISSN
- 1574-9541 (ISSN-L)
- OpenAlex
- W4384701390
- Cited by
- 336
- References
- 111
- FWCI
- 47.30
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
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- This page
- CoChat. (2026). Citation check: A review of deep learning techniques used in agriculture. https://app.cochat.ai/papers/10.1016/j.ecoinf.2023.102217
- The paper
- Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. (2023). A review of deep learning techniques used in agriculture. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2023.102217
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.
