ArticleScientific reports2022
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- Sequential Soybean Phenotypes for Grain Yield Prediction in Reciprocal Two-Season Validation.Plants (Basel, Switzerland) · 2026Article
- A multi-environment dataset consisting of official field trials for three major crops in the Nordic countries.Scientific data · 2026Article
- Design and evaluation of bayesian optimized hybrid deep learning model for forecasting crop yields using climate dynamics.Scientific reports · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- An interdisciplinary machine learning-based approach for predicting corn grain yield under biofertilizer applications.Scientific reports · 2026Article
- A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the US from 2018-2022.Scientific data · 2026Article
- A hybrid SERWI ensemble model for crop yield prediction using an inverse RMSE weighting strategy.Scientific reports · 2025Article
- Research on the impact of climate change on food security in Africa.Scientific reports · 2025Article
- Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy.Scientific reports · 2025Article
- Optimizing potato yield predictions in Uttar Pradesh, India: a comparative analysis of machine learning models.Scientific reports · 2025Article
- AI-enabled Barilai-Borwein-Blinder-Oaxaca-Bernoulli Deep Classifier for Enhanced Crop Yield Prediction.Scientific reports · 2025Article
- Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques.Scientific reports · 2025Article
- Maize yield estimation in Northeast China's black soil region using a deep learning model with attention mechanism and remote sensing.Scientific reports · 2025Article
- Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective Observational Study by Using the Common Data Model.Diagnostics (Basel, Switzerland) · 2025Article
- Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Frontiers in plant science · 2025Article
- Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage.Frontiers in plant science · 2025Article
- Article
- Yield estimation of winter wheat in the Huang-Huai-Hai region using MODIS and meteorological data: spatio-temporal analysis and county-level modeling.Frontiers in plant science · 2025Article
- Review
- Seasonal Maize yield forecasting in South and East African Countries using hybrid Earth observation models.Heliyon · 2024Article
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8 authors.
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Abstract
Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.
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