Citas bibligráficas
Tapia, J., Gustavo, Jose, Luis (2024). Detección de enfermedades y plagas en cultivos de tomate mediante el análisis de imágenes con Deep Learning [Trabajo de investigación, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/675351
Tapia, J., Gustavo, Jose, Luis Detección de enfermedades y plagas en cultivos de tomate mediante el análisis de imágenes con Deep Learning [Trabajo de investigación]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2024. http://hdl.handle.net/10757/675351
@mastersthesis{renati/1298925,
title = "Detección de enfermedades y plagas en cultivos de tomate mediante el análisis de imágenes con Deep Learning",
author = "Luis Alberto Castro De La Cruz",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2024"
}
Agriculture in Peru faces significant challenges due to pests and diseases, largely stemming from limitations in accessing essential tools and technologies for monitoring and preserving crop health. This issue is exacerbated in a global context, especially in the face of the El Niño phenomenon, which increases the need for care on the part of farmers. This study focuses on exploring various Deep Learning models applied to an extensive dataset consisting of photographs of tomato crop leaves. These images have been meticulously categorized according to the type of disease and pest they exhibit. In addition to this analysis, the project's investment cost is thoroughly examined, and the break-even point necessary for its long-term sustainability is determined. A crucial component of the research includes a comprehensive analysis of precision and loss ratios obtained through the application of these Deep Learning models. This analysis not only provides valuable insights into the effectiveness of the models but also serves as a basis for an informed recommendation on selecting the most suitable model to address the specific challenges of tomato crops in the Peruvian agricultural context. In summary, this study seeks not only to understand the complexities of diseases and pests in tomato crops but also to offer practical solutions backed by rigorous analysis and concrete data.
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