The DOI matches its Crossref record.
Estimating crop yields with deep learning and remotely sensed data
Abstract
This paper describes Illinois corn yield estimation using deep learning and another machine learning, SVR. Deep learning is a technique that has been attracting attention in recent years of machine learning, it is possible to implement using the Caffe. High accuracy estimation of crop yield is very important from the viewpoint of food security. However, since every country prepare data inhomogeneously, the implementation of the crop model in all regions is difficult. Deep learning is possible to extract important features for estimating the object from the input data, so it can be expected to reduce dependency of input data. The network model of two InnerProductLayer was the best algorithm in this study, achieving RMSE of 6.298 (standard value). This study highlights the advantages of deep learning for agricultural yield estimating.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Deep learning in environmental remote sensing: Achievements and challenges
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., et al. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111716 · Cited by 1,697
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Country-wide high-resolution vegetation height mapping with Sentinel-2
Lang, N., Schindler, K., & Wegner, J. D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111347 · Cited by 202
Metadata
- Authors
- Kuwata, K., & Shibasaki, R.
- Venue
- 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 858-861
- OpenAlex
- W2014847057
- Cited by
- 210
- References
- 0
- FWCI
- 46.96
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Estimating crop yields with deep learning and remotely sensed data. https://app.cochat.ai/papers/10.1109/igarss.2015.7325900
- The paper
- Kuwata, K., & Shibasaki, R. (2015). Estimating crop yields with deep learning and remotely sensed data. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/igarss.2015.7325900
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.
