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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Blue agave is an important commercial crop in Mexico, and it is the main source of the traditional mexican beverage known as tequila. The variety of blue agave crop known as Tequilana Weber is a crucial element for tequila agribusiness and the agricultural economy in Mexico. The number of agave plants in the field is one of the main parameters for estimating production of tequila. In this manuscript, we describe a mathematical morphology-based algorithm that addresses the agave automatic counting task. The proposed methodology was applied to a set of real images collected using an Unmanned Aerial Vehicle equipped with a digital Red-Green-Blue (RGB) camera. The number of plants automatically identified in the collected images was compared to the number of plants counted by hand. Accuracy of the proposed algorithm depended on the size heterogeneity of plants in the field and illumination. Accuracy ranged from 0.8309 to 0.9806, and performance of the proposed algorithm was satisfactory.

Details

Title
An Agave Counting Methodology Based on Mathematical Morphology and Images Acquired through Unmanned Aerial Vehicles
Author
Calvario, Gabriela 1   VIAFID ORCID Logo  ; Alarcón, Teresa E 2 ; Dalmau, Oscar 3   VIAFID ORCID Logo  ; Sierra, Basilio 4   VIAFID ORCID Logo  ; Hernandez, Carmen 5 

 Department of Electronics, Systems, and Informatics, ITESO—The Jesuit University of Guadalajara, Tlaquepaque, Jalisco 45604, Mexico; [email protected] 
 Departamento de Ciencias Computacionales e Ingenierías, Centro Universitario de los Valles, Ameca, Jalisco 46600, Mexico 
 Centro de Investigación en Matemáticas, Guanajuato 36023, Mexico; [email protected] 
 Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco UPV/EHU, 20018 Donostia-San Sebastián, Spain; [email protected] (B.S.); [email protected] (C.H.) 
 Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco UPV/EHU, 20018 Donostia-San Sebastián, Spain; [email protected] (B.S.); [email protected] (C.H.); Centre for the Research and Technology of Agro-Environmental and Biological Sciences, CITAB, Universidade de Trás-os-Montes e Alto Douro, UTAD, 5000-801 Vila Real, Portugal 
First page
6247
Publication year
2020
Publication date
2020
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2550457661
Copyright
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.