Modelo de predição da ocorrência de ferrugem asiática na cultura da soja a partir de variáveis climáticas e clusterização
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2022-02-24
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Universidade Federal de Goiás
Resumo
Asian rust is a disease with a significant impact on soybean in Brazil. Despite the great economic
relevance of soybeans for Brazilian agribusiness, there are few studies on the conditions that cause
the disease. This work aimed to create a predictive model considering the influence of climatic
variables (temperature, precipitation, humidity and solar radiation), based on a dataset of rust
occurrence, using the decision tree induction technique and logistic regression . The model was
created with data on the occurrence of the disease in the cities of Cristalina, Jataí and Rio Verde -
GO in the harvests from 2004/05 to 2016/17. For each occurrence record (detection), a
corresponding “non-occurrence” was generated, this being the thirtieth day prior to the day of
detection, assuming that on this date there would be no inoculum present in the field. The training
set for the modeling has 10 variables totaling 393 records. The predictive model was proposed from
the comparison of the best performance between the decision tree and logistic regression
algorithms. After the accuracy results obtained (decision tree 77.88%, against 56.53% of the logistic
regression algorithm), we used the clustering algorithm to group the data in the data preparation
phase, again comparing the result between decision tree and logistic regression. With the support
of clustering, we obtained the average accuracy in the range between 99 and 100% for decision
tree and 66.75 and 100% for logistic regression.
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CÔRTES, F. S. Modelo de predição da ocorrência de ferrugem asiática na cultura da soja a partir de variáveis climáticas e clusterização. 2022. 56 f. Dissertação (Mestrado em Agronegócio) - Universidade Federal de Goiás, Goiânia, 2022.