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  4. Study of the effectiveness of ML algorithms in predicting the probability of crop disease occurrence
 
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Study of the effectiveness of ML algorithms in predicting the probability of crop disease occurrence

Date Issued
2023
Author(s)
Vizniuk, Artem 
Laktionov, Ivan 
Diachenko, Grygorii 
Abstract
The aim of this research is to study the effectiveness of ML algorithms in predicting the probability of the corn disease «Fusarium Head Blight» in Dnipro region of Ukraine. Linear regression, feedforward neural networks and random forest models were considered for prediction. The random forest model obtained the best metric score on the testing set: R2=0.965, RMSE=3.44. Directions for further research were substantiated in the given subject area.
Subjects

regression

ML

time series

disease prediction

crops

agriculture

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2023_12_ConfProced_.pdf

Size

409.51 KB

Format

Adobe PDF

Checksum

(MD5):ba57913637f9da6bff703008221acb8e

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