A NEURAL NETWORK FOR SETTING TARGET CORN YIELDS
Abstract
Setting a realistic yield goal in each part of the field is one of the critical problems in precision agriculture. Factorsaffecting crop yields, such as soil, weather, and management, are so complex that traditional statistics cannot give accurateresults. As an automatic learning tool, the artificial neural network (ANN) is an attractive alternative for processing themassive data set generated by precision farming production and research. A feedforward, completely connected,backpropagation ANN was designed to approximate the nonlinear yield function relating corn yield to factors influencingyield. By stratified sampling based on rainfall, some of the data were excluded from the training set and used to verify theyield prediction accuracy of the ANN. The RMS error for 60 verification patterns was about 20%. After the ANN wasdeveloped and trained, three aspects of the input factors were investigated: (1) yield trends with 4 input factors, (2) interactionbetween nitrogen application rate and late July rainfall, and (3) optimization of the 15 input factors with a genetic algorithmto determine maximum yield. The model was then used on another field, and preliminary results of the latter study are given.
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Neural networks have been used for yield prediction since at least 2001.
“While neural networks have been applied to the problem of predicting yield since at least 2001 (Liu et al. 2001)”
From Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning (2023) · cites this paper as Liu et al. 2001
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ANALYSIS OF SPATIAL YIELD VARIABILITY USING A COMBINED CROP MODEL-EMPIRICAL APPROACH
Ирмак, А., Jones, J. W., Batchelor, W. D., Irmak, S., Paz, J. O., & Boote, K. J. (2006) · Transactions of the ASABE · doi:10.13031/2013.20464 · Cited by 5 · see citing works
Gholizadeh, A., Mehravi, S., Hanifei, M., & Akbarpour, O. (2024) · Acta Physiologiae Plantarum · doi:10.1007/s11738-024-03754-5 · Cited by 4 · see citing works
Barikloo, A., Alamdari, P., Moravej, K., & Servati, M. (2022) · Arabian Journal of Geosciences · doi:10.1007/s12517-022-09868-9 · Cited by 4 · see citing works
Aziz, A., Ariyanto, K., Arıyanto, D. P., & Ariyanto, S. (2023) · Scientific Horizons · doi:10.48077/scihor7.2023.149 · Cited by 4 · see citing works
- Authors
- Liu, J., Goering, C. E., & Tian, L.
- Venue
- Transactions of the ASAE
- Published
- 2001
- Volume
- 44
- Issue
- 3
- ISSN
- 0001-2351 (ISSN-L)
- 2151-0059
- OpenAlex
- W2250636764
- Cited by
- 136
- References
- 8
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 8 · 2023: 9 · 2022: 14 · 2021: 14 · 2020: 6 · 2019: 14 · 2018: 6 · 2017: 2 · 2016: 3 · 2015: 2 · 2014: 6 · 2013: 3 · 2012: 4
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transactions of the ASAE (publishedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Crop Yield and Soil Fertility
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- corn yield prediction, artificial neural networks, backpropagation, precision agriculture, rainfall, nitrogen application rate, genetic algorithm optimization
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