GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction

GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction

A robust maize yield prediction framework is proposed to counter major problems predominantly present in Agri-tech analytics, like scarcity of data, class imbalance, redundant features, and model interpretability. This research is motivated by the need for accurate crop forecasting to ensure food se...

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Bibliographic Details
Main Authors: Ishaan Seshukumar Pothapragada, Sujatha R
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Smart Agricultural Technology
Subjects:
Maize yield prediction
Generative adversarial networks
Ensemble learning
Convolutional neural networks
Explainable AI
Stacked model
Online Access:http://www.sciencedirect.com/science/article/pii/S2772375525002254
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