CLASSIFICATION OF SUGARCANE YIELDS ACCORDING TO SOIL FERTILITY PROPERTIES USING SUPERVISED MACHINE LEARNING METHODS

CLASSIFICATION OF SUGARCANE YIELDS ACCORDING TO SOIL FERTILITY PROPERTIES USING SUPERVISED MACHINE LEARNING METHODS

ABSTRACT Action planning and decision-making in the sugarcane management chain depend on yield estimates, which, in turn, vary with the soil. This study aimed to describe an applicable method of classifying sugarcane productivity into three categories, based on soil properties (medium, low, and high...

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Bibliographic Details
Main Authors: Jhonnatan Yepes, Gian Oré, Marlon S. Alcântara, Hugo E. Hernandez-Figueroa, Bárbara Teruel
Format: Article
Language:English
Published: Sociedade Brasileira de Engenharia Agrícola 2022-11-01
Series:Engenharia Agrícola
Subjects:
random forest
naïve bayes
decision trees
multi-class classification
classification techniques
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000500209&lng=en&tlng=en

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