Bibliographic citations
Solano, C., Caballero, I. (2022). Modelo de predicción de plagas en el cultivo de palto utilizando metodología de aprendizaje automático supervisado, empresa Virú S.A., 2019-2021 [Tesis, Universidad Privada Antenor Orrego]. https://hdl.handle.net/20.500.12759/8559
Solano, C., Caballero, I. Modelo de predicción de plagas en el cultivo de palto utilizando metodología de aprendizaje automático supervisado, empresa Virú S.A., 2019-2021 [Tesis]. PE: Universidad Privada Antenor Orrego; 2022. https://hdl.handle.net/20.500.12759/8559
@misc{renati/372715,
title = "Modelo de predicción de plagas en el cultivo de palto utilizando metodología de aprendizaje automático supervisado, empresa Virú S.A., 2019-2021",
author = "Caballero Cruz, Ivonne del Pilar",
publisher = "Universidad Privada Antenor Orrego",
year = "2022"
}
The use of technology in agricultural management is a strong pillar for the development of productivity in Peru. Although in recent years this sector has implemented technologies such as drones, weather stations, among others, to control many factors that involve this sector, the information generated from it is not always exploited. The objective of this work was to develop a pest prediction model in avocado cultivation using the supervised machine learning methodology. A data set of climatological variables and the appearance of pests in the avocado crop corresponding to the years 2019-2020 was analyzed, in the Virú SA Company The investigation was descriptive and the phytosanitary cards and meteorological stations of the company, to collect data on pests and weather variables respectively. A database was built, data frame cleaning and data filtering was performed. Python, jupyterlab, Transt sql and Excel were used for data processing. Descriptive statistics, inferential statistics and linear regression techniques were used in the data analysis. It was found that the pests of the avocado crop: Oligonychus punicae, Oligonychus yothersi, Bemisia Tabaci and Trips Tabaci present significant high and moderate correlations with the climatological variable’s temperature and average humidity. It was concluded that the prediction models based on supervised machine learning that were estimated predict the appearance of these pests in the avocado crop, with a precision of less than 90%
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