An Input-Frugal Deep Learning Framework for Weather-Driven National Crop-Yield Forecasting: A Case Study of Brazilian Soybean
Reliable, timely crop-yield forecasts are essential for market stability and risk management, yet many approaches rely on costly or hard-to-scale inputs.
By Fernando Dupin da Cunha Mello (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil), Prashant Kumar (Global Centre for Clean Air Research), Erick G. Sperandio Nascimento (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil)arXiv cs.LG
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Originally published by arXiv cs.LG on Oct 1, 2026.
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