ArticleCommunications biology2023
A data-driven crop model for maize yield prediction.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- UAV-based temporal synergistic estimation of multiple alfalfa qualities integrating physics-informed network and 3D allometric operator.Plant phenomics (Washington, D.C.) · 2026Article
- Harvesting insights: interpretable machine learning to understand environmental drivers of U.S. maize and soybean yield.Scientific reports · 2026Article
- Comparison of multi-stress resilience in wild and domesticated Cowpea.Scientific reports · 2026Article
- Characterizing the COPhotosynthetica · 2026Article
- Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.Scientific reports · 2025Article
- Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic.Scientific reports · 2025Article
- Current impacts of elevated COStress biology · 2025Review
- In-Season Predictions Using ChlorophyllPlants (Basel, Switzerland) · 2025Article
- A bi-stage data-driven process-based model for sorghum breeding and yield prediction: coupling explainable artificial intelligence and crop modeling.Frontiers in plant science · 2025Article
- A data-driven crop model for biomass sorghum growth process simulation.Frontiers in plant science · 2025Article
- How does climate change affect potential yields of four staple grain crops worldwide by 2030?PloS one · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The proposed model tracks the daily biomass accumulation process during the maize growing season and uses daily produced biomass to estimate the final grain yield. Computational studies using crop yield, field location, genotype and corresponding environmental data were conducted in the US Corn Belt region from 1981 to 2020. The results suggest that the proposed model can achieve an accurate prediction performance with a 7.16% relative root-mean-square-error of average yield in 2020 and provide scientifically explainable results. The model also demonstrates its ability to detect and separate interactions between genotypic parameters and environmental variables. Additionally, this study demonstrates the potential value of the proposed model in helping farmers achieve higher yields by optimizing seed selection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.