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An efficient IoT-based crop damage prediction framework in smart agricultural systems

Abstract This paper introduces an efficient IoT-based framework for predicting crop damage within smart agricultural systems, focusing on the integration of Internet of Things (IoT) sensor data with advanced machine learning (ML) and ensemble learning (EL) techniques. The primary objective is to dev...

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Hoofdauteurs: Nermeen Gamal Rezk, Abdel-Fattah Attia, Mohamed A. El-Rashidy, Ayman El-Sayed, Ezz El-Din Hemdan
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Nature Portfolio 2025-07-01
Reeks:Scientific Reports
Onderwerpen:
Crop damage
Missing data
Imputation technique
Machine learning
Ensemble learning
Prediction
Online toegang:https://doi.org/10.1038/s41598-025-12921-8
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