arXiv Machine Learning August 19, 2026 By Shrey Gupta, Yi Ming, George Mohler

Evaluating and improving crop-yield forecasting methods during extreme drought

Read the original on arXiv Machine Learning →

arXiv:2608. 17971v1 Announce Type: new Abstract: The impact of climate variability on food production has led to the creation of various forecasting models that uses machine learning (ML), numerical weather predictors (NWP) or a hybrid of ML-NWP models to identify structural and physical relationships between meteorological drivers and crop growth, in order to predict crop yield.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

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multimodal

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llms benchmarks

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