Prediction of maize yield in Uganda using CNN-LSTM architecture on a multimodal climate and remote sensing dataset
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Date
2026-01
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Maize is a staple crop in Uganda, underpinning both food security and rural
livelihoods. Accurate forecasting of maize yields is therefore crucial for guiding
agricultural planning, resource allocation, and policy design. Yet traditional
statistical methods are often limited by low accuracy, poor scalability, and weak
integration of diverse inputs, leaving them unable to capture complex, nonlinear,
and spatiotemporal dynamics of crop growth. To overcome these constraints, we
developed a hybrid convolutional neural network and long short-term memory
(CNN-LSTM) model. This model integrates remotely sensed climatic variables
and vegetation indices with biannual maize yield records from Uganda’s Zonal
Agricultural Research and Development Institute (ZARDI) zones for the period
2018–2020. Due to the scarcity of high-quality yield data, we applied the Synthetic
Minority Oversampling Technique for Regression (SMOGN) alongside feature selection
to balance the dataset and improve predictive robustness. The CNN-LSTM model’s
ability to select features and perform extensive hyperparameter tuning enabled it
to outperform baseline models. It achieved a Mean Squared Error (MSE) of 0.107
tonnes2
, a Mean Absolute Error (MAE) of 0.267 tonnes, a Root Mean Squared Error
(RMSE) of 0.327 tonnes, and an R2
score of 0.783. A comparative analysis revealed
that the CNN+Random Forest (RF) model achieved an MSE of 0.137 tonnes2
, a MAE
of 0.281 tonnes, an RMSE of 0.370 tonnes, and an R2
score of 0.722. These results
outperformed the standalone CNN (MSE=0.216, R2=0.562) and RF (MSE=0.211,
R2=0.573) models, underscoring the advantage of combining spatial–temporal
learning for improved predictive accuracy. Residual analysis further confirmed the
model's stability, showing minimal bias and close agreement between observed
and predicted yields. These findings highlight the potential for integrating spatial–
temporal deep learning and ensemble methods to deliver accurate crop yield
forecasts in data-limited smallholder systems. By offering a scalable framework for
evidence-based farm planning and food security policy, our study demonstrated that
advanced machine learning can directly support sustainable development in subSaharan Africa. Future research will extend the framework to incorporate Transformer
architectures, high-resolution satellite imagery, and explainable AI, further enhancing
accuracy, interpretability, and decision-support capacity.
Article highlights
• Developed a hybrid CNN-LSTM model that integrates remotely sensed climatic
and vegetation indices to predict maize yields across Uganda’s ZARDI zones
(2018–2020).
• Achieved high predictive accuracy (MSE=0.107, explaining 78% of yield
variation), outperforming standalone CNN, ensemble models such as RF, and
CNN-RF.
• Introduced SMOGN-based data augmentation and feature selection techniques
to overcome data sparsity, a novel approach for yield forecasting in smallholder,
data-limited systems.
• Demonstrated that hybrid DL frameworks can inform scalable, data-driven
agricultural planning, with potential to guide policymakers and strengthen food
security strategies in SSA.
• Future work will focus on integrating Transformer architectures for improved
sequence modelling alongside high-resolution imagery and explainable AI to
enhance accuracy, interpretability, and practical decision support.
Description
Keywords
Maize yield prediction, Ensemble learning, Precision agriculture, CNN-LSTM, Vegetation indices
Citation
Taremwa, D., Ahishakiye, E., Obbo, A. et al. Prediction of maize yield in Uganda using CNN-LSTM architecture on a multimodal climate and remote sensing dataset. Discov Artif Intell 6, 164 (2026). https://doi.org/10.1007/s44163-026-00855-7