Hybrid CNN-LSTM-attention model for enhanced reference crop evapotranspiration estimation toward optimized irrigation scheduling

This study developed an artificial intelligence-based optimization framework for irrigation management to address the challenges of insufficient integration between reference crop evapotranspiration (ETo) metrics and irrigation decision-making systems. First, a hybrid deep learning architecture comb...

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Dades bibliogràfiques

Autors principals:	Junlong Chen, Kai Chen, Yonghuai Zhu
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2026-02-01
Col·lecció:	Journal of Agriculture and Food Research
Matèries:	Reference crop evapotranspiration Irrigation scheduling CNN-LSTM-Attention model
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S2666154325009160
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