Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery-Reference-Cited by-同舟云学术

Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery

Published:2023-11-15 Issue:22 Volume:44 Page:7017-7032
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Sánchez Abraham¹,Nanclares Raúl¹,Pelagio Ulises²^ORCID,Quevedo Alexander¹^ORCID,Calvario Gabriela³^ORCID,Aguilar Alejandra⁴,Moya Sánchez E. Ulises¹⁵^ORCID

Affiliation:

1. Coordinación General de Innovación Gubernamental del Gobierno de Jalisco, Independencia 55, Guadalajara, México

2. Posgrado en Geografía, Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México, Morelia, México

3. Department of Electronics, Systems, and Informatics, ITESO The Jesuit University of Guadalajara, Tlaquepaque, Jalisco, Mexico

4. Secretaría de Medio Ambiente y Desarrollo Territorial, Dirección de Geomática e Información ambiental Territorial, Guadalajara Jal, Mexico

5. Tecnológico Nacional de México, Instituto Tecnológico José Mario Molina Pasquel Hernríquez, Unidad académica Zapopan, Zapopan, Jalisco, México

Funder

Consejo Nacional de Ciencia y Tecnología

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2275320

Reference32 articles.

1. ARA. 2022. “Tequila libre de deforestación; acciones ante el cambio climático.” https://siga.jalisco.gob.mx/comunicacionsemadet/Mecanismo_de_Solucion_ARA.pdf.

2. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network

3. An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles

4. A Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVs

5. Chen L. Y. Zhu G. Papandreou F. Schroff and H. Adam. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” CoRr Abs/1802.02611. http://arxiv.org/abs/1802.02611.