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(Reference retrieved automatically from SciELO through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Jhonnatan Yepes [1] ; Gian Oré [2] ; Marlon S. Alcântara [3] ; Hugo E. Hernandez-Figueroa [4] ; Bárbara Teruel [5]
Total Authors: 5
Affiliation:
[1] University of Campinas. School of Agricultural Engineering - Brasil
[2] University of Campinas. School of Electrical and Computer Engineering - Brasil
[3] University of Campinas. School of Electrical and Computer Engineering - Brasil
[4] University of Campinas. School of Electrical and Computer Engineering - Brasil
[5] University of Campinas. School of Agricultural Engineering - Brasil
Total Affiliations: 5
Document type: Journal article
Source: Engenharia Agrícola; v. 42, n. 5 2022-11-14.
Abstract

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), determining which is most associated with biomass production. To this end, we applied the machine learning methods Naïve Bayes, Decision Trees, and Random Forest, as they proved to be useful tools for faster and more accurate results. Our results indicate that Random Forest is the most suitable for classifying all yield categories, and Naïve Bayes had good results for classification into “medium” and “low” and potential for solving multiclass problems in agriculture. Organic matter was the property most closely related to sugarcane biomass yield by the Random Forest and Decision Trees algorithms. The methods described can be used to obtain subsidies for sugarcane chain management, contributing to more sustainable decisions. (AU)

FAPESP's process: 18/00601-8 - Remote sensing radar carried by drone
Grantee:Dieter Lubeck
Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
FAPESP's process: 17/19416-3 - Drone-borne radar for sugar cane precision agriculture
Grantee:Hugo Enrique Hernández Figueroa
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE