Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning

Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning

Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to verify accurate ML models for predicti...

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Main Authors: Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:AgriEngineering
Subjects:
<i>Ananas comosus</i>
artificial intelligence
food computing
food component prediction
hyperspectral imaging
Online Access:https://www.mdpi.com/2624-7402/7/6/170
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