Analysis and Forecasts of Sugarcane Production in Tons Using Machine Learning

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:54Z
Fecha de publicación2026-01-01
ResumenSugarcane is a cornerstone of Mexico’s rural economy, supporting over 2 million jobs and contributing 12% of agricultural GDP (Gross Domestic Product). Despite ranking as the world’s sixth-largest producer, the industry faces declining yields due to climate variability, structural challenges, and limited technology adoption among smallholders. To address critical forecasting gaps, we developed a predictive model using public mill data from CONADESUCA (47 mills) and compared machine learning approaches: Long Short-Term Memory (LSTM), Random Forest, and Dense Neural Networks. LSTM demonstrated superior performance in capturing production trends, with key mills achieving RMSE values as low as 0.153 and R<sup>2</sup> up to 0.981. While climate variables provided marginal improvements, results suggest operational factors may dominate yield variability. The study highlights both the potential of AI-driven forecasting for mill planning and persistent data limitations—particularly the lack of field-level records from small producers.es_ES
Doihttps://doi.org/10.1007/978-3-032-13497-4_2es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5665
Idiomaenes_ES
EditorialSpringer Science and Business Media Deutschland GmbHes_ES
RelaciónIntelligent Systems Reference Libraryes_ES
URL relacionadohttps://doi.org/10.1007/978-3-032-13497-4_2es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteIntelligent Systems Reference Library
Palabra claveAgricultural forecastinges_ES
Palabra claveArtificial neural networkes_ES
Palabra claveData sciencees_ES
Palabra claveLSTMes_ES
Palabra claveRandom forestes_ES
Palabra claveTime serieses_ES
TítuloAnalysis and Forecasts of Sugarcane Production in Tons Using Machine Learninges_ES
TipoCapítulo Libroes_ES
ArbitradoHa sido Arbitradoes_ES
AutorCavazos-Matsumoto, Adhara A.
AutorIbarra-Martinez, Salvador
AutorCastán-Rocha, José A.
AutorGarcía-Ruiz, Alejandro H.
AutorTerán-Villanueva, Jesús D.
AutorCavazos-Matsumoto, Adhara A.es_ES
AutorIbarra-Martinez, Salvadores_ES
AutorCastán-Rocha, José A.es_ES
AutorGarcía-Ruiz, Alejandro H.es_ES
AutorTerán-Villanueva, Jesús D.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Rango de páginas41-80es_ES
URL relacionadahttps://doi.org/10.1007/978-3-032-13497-4_2
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen283es_ES

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