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Título : | Coffee rust detection and recommendations module through deep learning algorithms |
Autor : | Eduardo De Felipe Rendón David Cruz Flores Aldair Barojas Jiménez Edgar Jahir Hernández Andrade Erick de Jesús Flores Acosta Luis Rolando Guarneros Nolasco |
Palabras clave : | Artículo |
Fecha de publicación : | nov-2023 |
Editorial : | Universidad Nacional Autonoma de México |
Resumen : | In recent years, coffee has faced a global increase in plagues and diseases, impacting the quality and profitability of its benefits. One of the most devastating and common diseases is coffee rust, which has caused a significant decrease in coffee production at the national and state levels by causing the falling of mature and young leaves, reducing production. In this work, we present a mobile application development using a deep learning algorithm model to identify and predict the level of coffee rust infection, providing treatment and prevention recommendations for coffee farmers. The model classified with an accuracy of 58% of rust diseased leaves in a dataset of diseased and healthy leaves. |
metadata.dc.identifier.*: | http://reini.utcv.edu.mx:80/handle/123456789/1415 |
metadata.dc.language: | spa |
Aparece en las colecciones: | Artículos arbitrados |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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19-Coffee-rust-detection-and-recommendations-module.pdf | 395.6 kB | Adobe PDF | Visualizar/Abrir |
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