| dc.creator | Olivares, Barlin O. | |
| dc.creator | Vega, Andrés | |
| dc.creator | Rueda Calderón, María A. | |
| dc.creator | Montenegro-Gracia, Edilberto | |
| dc.creator | Araya-Almán, Miguel | |
| dc.creator | Marys, Edgloris | |
| dc.date | 2023-03-22T17:43:21Z | |
| dc.date | 2023-03-22T17:43:21Z | |
| dc.date | 2022 | |
| dc.date.accessioned | 2024-05-02T20:30:46Z | |
| dc.date.available | 2024-05-02T20:30:46Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/4556 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9274798 | |
| dc.description | 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. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | Sustainability, 14(21), 14123 | |
| dc.subject | Black Sigatoka | |
| dc.subject | Musa | |
| dc.subject | Production | |
| dc.subject | Banana disease | |
| dc.subject | Random forest | |
| dc.subject | Machine learning | |
| dc.title | Prediction of banana production using epidemiological parameters of black sigatoka: an application with random forest | |
| dc.type | Article | |
