Potato Leaf Disease Detection: Comparative Study of the Performance of CNN Architectures - VGG19, ResNet50 and DenseNet201-Reference-Cited by-同舟云学术

Potato Leaf Disease Detection: Comparative Study of the Performance of CNN Architectures - VGG19, ResNet50 and DenseNet201

Published:2023-12-14 Issue: Volume: Page:
ISSN:
Container-title:2023 9th International Conference on Signal Processing and Intelligent Systems (ICSPIS)
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Author:

Rodríguez José Luis Ruíz¹,Rodríguez Jonathan Moises Ruíz²,Sánchez José María Pasco¹,Moreno Luis Antonio Orbegoso¹,Corro Isac Daniel Miñano³,Ramirez Edgar David Valverde¹

Affiliation:

1. National University of Trujillo,Department of Mechatronic Engineering,Trujillo,Peru

2. National University of Trujillo,Department of Agricultural Science,Trujillo,Peru

3. National University of Trujillo,Department of System Engineering,Trujillo,Peru

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10402599/10402616/10402706.pdf?arnumber=10402706

Reference27 articles.

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4. Manual Técnico: Tecnologías De Manejo De La “Rancha” O Tizón Tardío De La Papa (Phytophthora infestans (Mont) de Bary);Zúñiga López;National Institute of Agrarian Innovation,2019

5. Fungicidas para el cultivo de papa. Estación Experimental Agropecuaria Mendoza;Longone;National Institute of Agricultural Technology,2018

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1. A Comprehensive Review of Convolutional Neural Networks based Disease Detection Strategies in Potato Agriculture;Potato Research;2024-08-26