Clasificación de zonas afectadas por la marchitez en banano: una aplicación con algoritmos de Machine Learning en Venezuela

Barlin Orlando Olivares; Angélica Rueda Calderón; Juan Carlos Rey

Submitted October 8, 2021

Published 2021-01-28

Artículos

Vol. 1 No. 1 (2021): REICIT

Classification of areas affected by banana wilt: an application with Machine Learning algorithms in Venezuela

Barlin Orlando Olivares⁺⁻
Angélica Rueda Calderón⁺⁻
Juan Carlos Rey⁺⁻

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DOI: ND

Published: 2021-01-28

How to Cite

Olivares, B. O., Rueda Calderón, A. and Rey, J. C. (2021) “Classification of areas affected by banana wilt: an application with Machine Learning algorithms in Venezuela”, REICIT, 1(1), pp. 1–17. Available at: https://revistas.up.ac.pa/index.php/REICIT/article/view/2440 (Accessed: 27 September 2024).

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Abstract

Agricultural production systems have millions of data that Artificial Intelligence (AI) can transform into information to promote accuracy in the producer's decision-making and, thus, maximize production in a sustainable way. The objective of this work is to classify areas affected by wilt in banana in Venezuela using Machine Learning algorithms such as: Random Forest (RF), Support Vector Machines (SVM), classification trees (CART), the Decision trees algorithm (C5.0) and linear discriminant analysis (ADL), likewise different resampling techniques were applied: subsampling, oversampling, random oversampling (ROSE) and synthetic minority oversampling technique (SMOTE). To do this, a systematic soil sampling was carried out in the 39 banana lots and the incidence was evaluated during the years 2016 and 2017. The results indicate that RF through the subsampling technique can be an effective algorithm to make decisions in affected banana areas. from diseases such as banana wilt. The sensitivity, specificity, accuracy and Kappa coefficient statistics were 1.0, 0.94, 0.96 and 0.91 respectively, without the resampling technique. RF would help prevent and reduce the effect of banana diseases and their impact on production. In conclusion, Machine Learning in agriculture could offer an advance that would guarantee decision-making with the aim of achieving sustainability.

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