dor_id: 4148923

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/2026/1034

100.1.#.a: Kokate, Jayesh Krishnarao; Kumar, Sunil; Kulkarni, Anant G

524.#.#.a: Kokate, Jayesh Krishnarao, et al. (2023). Deep Learning Based Leaf Disease Classification. Journal of Applied Research and Technology; Vol. 21 Núm. 5, 2023; 764-771. Recuperado de https://repositorio.unam.mx/contenidos/4148923

245.1.0.a: Deep Learning Based Leaf Disease Classification

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2023

264.#.1.c: 2023-10-30

653.#.#.a: CNN; Plant diseases; Computer vision; classification

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-ND 4.0 Internacional, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/2026

001.#.#.#: 074.oai:ojs2.localhost:article/2026

041.#.7.h: eng

520.3.#.a: A plant in its healthy state can produce its crops to the utmost of its genetically defined potential. However, on the other hand, a plant which is infected by an infection causing agent which directly or indirectly interferes with the plants’ growth or its functioning. A disease may interfere several processes in a plant’s metabolism. Continuous manual monitoring of farm and leaves of the trees by an expert is not possible, as it would be very expensive and time-consuming. Identification of correct disease accurately when it first appears on the plant is a decisive footstep for proper managing and diseases control in fields. Thus, an automated way of identifying the diseases and accurate classification of disease will play an important role in taking appropriate action for stopping the further crop and yield damage. This paper presents a Xception model of deep learning for identification and classification of the leaf diseases, average accuracy of 98% is resulted in this work. Xception model is utilised in this work and results are presented in terms of precision, recall, and f1-score for the 11 disease classes of tomato leaf.

773.1.#.t: Journal of Applied Research and Technology; Vol. 21 Núm. 5 (2023); 764-771

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 764-771

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2023.21.5.2026

harvesting_date: 2023-11-08 13:10:00.0

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file_creation_date: 2023-10-09 21:24:55.0

file_modification_date: 2023-10-09 21:24:59.0

file_creator: Yolanda G.G.

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