Prediction of Banana Production Using Epidemiological Parameters of Black Sigatoka: An Application with Random Forest

Barlin O. Olivares (), Andrés Vega, María A. Rueda Calderón, Edilberto Montenegro-Gracia, Miguel Araya-Almán and Edgloris Marys
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Barlin O. Olivares: Programa de Doctorado en Ingeniería Agraria, Alimentaria, Forestal y del Desarrollo Rural Sostenible, Universidad de Córdoba, Carretera Nacional IV, km 396, 14014 Córdoba, Spain
Andrés Vega: Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Av. Haya de la Torre s/n, Córdoba X5000HUA, Argentina
María A. Rueda Calderón: Laboratorio de Genética y Genómica Aplicada, Escuela de Ciencias del Mar, Pontificia Universidad Católica de Valparaíso, Chile. S/Brasil, Valparaíso 2950, Chile
Edilberto Montenegro-Gracia: Facultad de Ciencias Agropecuarias, Universidad de Panamá, CRUBO Bocas del Toro, Finca 15, Changuinola 01001, Panama
Miguel Araya-Almán: Departamento de Ciencias Agrarias, Universidad Católica del Maule, km 6 Camino Los Niches, Curicó 3466706, Chile
Edgloris Marys: Laboratorio de Biotecnología y Virología Vegetal, Centro de Microbiología y Biología Celular, Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas 1204, Venezuela

Sustainability, 2022, vol. 14, issue 21, 1-18

Abstract: Accurate predictions of crop production are critical to developing effective strategies at the farm level. Knowing banana production is due to the need to maximize the investment–profit ratio, and the availability of this information in advance allows decisions to be made about the management of important diseases. The objective of this study was to predict the number of banana bunches from epidemiological parameters of Black Sigatoka (BS), using random forests (RF) for its ability to predict crop production responses to epidemiological variables. Weekly production data (number of banana bunches) and epidemiological parameters of BS from three adjacent banana sites in Panama during 2015–2018 were used. RF was found to be very capable of predicting the number of banana bunches, with variance explained as 70.0% and root mean square error (RMSE) of 1107.93 ± 22 of the mean banana bunches observed in the test case. The site, week, youngest leaf spotted and youngest leaf with symptoms in plants with 10 weeks of physiological age were found to be the best predictor group. Our results show that RF is an efficient and versatile machine learning method for banana production predictions based on epidemiological parameters of BS due to its high accuracy and precision, ease of use, and usefulness in data analysis.

Keywords: Black Sigatoka; Musa; production; banana disease; random forest; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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