A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data

The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and...

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Detalles Bibliográficos

Principais autores:	Oussama M’hamdi, Sándor Takács, Gábor Palotás, Riadh Ilahy, Lajos Helyes, Zoltán Pék
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2024-03-01
Series:	Plants
Assuntos:	tomato quality extreme gradient boosting artificial neural network prediction shapley additive explanations
Acceso en liña:	https://www.mdpi.com/2223-7747/13/5/746
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