Detection of Irrigated and Non-Irrigated Soybeans Using Hyperspectral Data in Machine-Learning Models

Detection of Irrigated and Non-Irrigated Soybeans Using Hyperspectral Data in Machine-Learning Models

The objectives of this work are (i) to classify soybean cultivars under different irrigation managements using hyperspectral data, looking for the best machine-learning algorithm for the classification and the input that improves the performance of the models. The experiment was implemented in the 2...

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Bibliographic Details
Main Authors: Izabela Cristina de Oliveira, Ricardo Gava, Dthenifer Cordeiro Santana, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Mayara Favero Cotrim, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Algorithms
Subjects:
algorithms
spectral bands
vegetation indices
computational intelligence
<i>Glycine max</i>
Online Access:https://www.mdpi.com/1999-4893/17/12/542
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