The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Artificial neural networks for corn and soybean yield prediction
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
Cites
Fundamentals of Biostatistics.
(1969) · Annals of Internal Medicine · doi:10.7326/0003-4819-70-4-873 · Cited by 4,575
Fundamentals of Biostatistics.
Barath, E., & Rosner, B. A. (1992) · Biometrics · doi:10.2307/2532371 · Cited by 1,183
Sanjeev, B. (2006) · Jaypee Brothers Medical Publishers (P) Ltd. eBooks · doi:10.5005/jp/books/10313 · Cited by 995
Artificial Neural Networks to Estimate Soil Water Retention from Easily Measurable Data
Pachepsky, Y. A., Timlin, D., & Varallyay, G. (1996) · Soil Science Society of America Journal · doi:10.2136/sssaj1996.03615995006000030007x · Cited by 365
Crop Simulation Models in Agronomic Systems
Whisler, F. D., Acock, B., Baker, D. N., Fye, R. E., Hodges, H. F., Lambert, J. R., Lemmon, H. E., McKinion, J. M., & Reddy, V. R. (1986) · Advances in Agronomy · doi:10.1016/s0065-2113(08)60282-5 · Cited by 340
Modeling water retention curves of sandy soils using neural networks
Schaap, M. G., & Bouten, W. (1996) · Water Resources Research · doi:10.1029/96wr02278 · Cited by 278
Spatial validation of crop models for precision agriculture
Basso, B., Ritchie, J. T., Pierce, F. J., Braga, R. P., & Jones, J. W. (2001) · Agricultural Systems · doi:10.1016/s0308-521x(00)00063-9 · Cited by 221
Introduction to Neural Networks
(1991) · Elsevier eBooks · doi:10.1016/c2009-0-13851-5 · Cited by 182
Introduction to Neural Networks
Baughman, D. R., & Liu, Y. (1995) · Elsevier eBooks · doi:10.1016/b978-0-12-083030-5.50007-2 · Cited by 98
Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean
Elizondo, D. A., McClendon, R. W., & Hoogenboom, G. (1994) · Transactions of the ASAE · doi:10.13031/2013.28168 · Cited by 93
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
Crop yield prediction with deep convolutional neural networks
Nevavuori, P., Narra, N., & Lipping, T. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.104859 · Cited by 586
Forecasting yield by integrating agrarian factors and machine learning models: A survey
Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., & Srinivasan, K. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.10.024 · Cited by 346
Big Data in Agriculture: A Challenge for the Future
Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018) · Applied Economic Perspectives and Policy · doi:10.1093/aepp/ppx056 · Cited by 325
A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0
Raj, M., Gupta, S., Chamola, V., Elhence, A., Garg, T., Atiquzzaman, M., & Niyato, D. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103107 · Cited by 283
Metadata
- Authors
- Kaul, M., Hill, R. L., & Walthall, C.
- Venue
- Agricultural Systems
- Publisher
- Elsevier BV
- Published
- 2005-07
- Volume
- 85
- Issue
- 1
- Pages
- 1-18
- ISSN
- 0308-521X (ISSN-L)
- OpenAlex
- W2116905012
- Cited by
- 373
- References
- 12
- FWCI
- 2.25
- Type
- journal article
- 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: Artificial neural networks for corn and soybean yield prediction. https://app.cochat.ai/papers/10.1016/j.agsy.2004.07.009
- The paper
- Kaul, M., Hill, R. L., & Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems. https://doi.org/10.1016/j.agsy.2004.07.009
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.
