Segmentación en imagen de frutos de granado usando deep learning con aplicación en agricultura de precisión
- Giménez-Gallego, Jaime 1
- González-Teruel, Juan D. 1
- Toledo, Ana 1
- Jiménez Buendía, Manuel 1
- Soto-Valles, Fulgencio 1
- Torres-Sánchez, Roque 1
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1
Universidad Politécnica de Cartagena
info
- Carlos Balaguer Bernaldo de Quirós (coord.)
- José Manuel Andújar Márquez (coord.)
- Ramon Costa Castelló (coord.)
- Carlos Ocampo Martínez (coord.)
- Jesús Fernández Lozano (coord.)
- Matilde Santos Peñas (coord.)
- José Enrique Simó Ten (coord.)
- Montserrat Gil Martínez (coord.)
- Jose Luis Calvo Rolle (coord.)
- Raúl Marín Prades (coord.)
- Eduardo Rocón de Lima (coord.)
- Elisabet Estévez Estévez (coord.)
- Pedro Jesús Cabrera Santana (coord.)
- David Muñoz de la Peña Sequedo (coord.)
- José Luis Guzmán Sánchez (coord.)
- José Luis Pitarch Pérez (coord.)
- Oscar Reinoso García (coord.)
- Oscar Déniz Suárez (coord.)
- Emilio Jiménez Macías (coord.)
- Vanesa Loureiro Vázquez (coord.)
Publisher: Servizo de Publicacións ; Universidade da Coruña
ISBN: 978-84-9749-841-8
Year of publication: 2022
Pages: 1001-1006
Congress: Jornadas de Automática (43. 2022. Logroño)
Type: Conference paper
Abstract
In precision agriculture, to automatically monitor the state of the crop using images, processing tools are needed to extract the information of interest. In this study, a Deep Learning model is developed for image segmentation to discriminate pomegranate fruits. Results of Intersection over Union (IoU)=0.71 and mean Average Precision (mAP)=0.82 are achieved. Subsequently, an algorithm for estimating the size of the fruit in pixels is presented, with an average relative error of 5.4%.