Identificación y conteo de aceitunas en imágenes digitales tomadas en el olivar mediante morfología matemática y redes neuronales convolucionales
- Arturo Aquino 1
- Juan Manuel Ponce 1
- Borja Millan 1
- Diego Tejada-Guzmán 1
- José Manuel Andújar 1
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1
Universidad de Huelva
info
- Jose Luis Calvo Rolle (coord.)
- Jose Luis Casteleiro Roca (coord.)
- María Isabel Fernández Ibáñez (coord.)
- Óscar Fontenla Romero (coord.)
- Esteban Jove Pérez (coord.)
- Alberto José Leira Rejas (coord.)
- José Antonio López Vázquez (coord.)
- Vanesa Loureiro Vázquez (coord.)
- María Carmen Meizoso López (coord.)
- Francisco Javier Pérez Castelo (coord.)
- Andrés José Piñón Pazos (coord.)
- Héctor Quintián Pardo (coord.)
- Juan Manuel Rivas Rodríguez (coord.)
- Benigno Rodríguez Gómez (coord.)
- Rafael Alejandro Vega Vega (coord.)
Editorial: Servizo de Publicacións ; Universidade da Coruña
ISBN: 978-84-9749-716-9
Any de publicació: 2019
Pàgines: 818-827
Congrés: Jornadas de Automática (40. 2019. Ferrol)
Tipus: Aportació congrés
Resum
Early and accurate yield estimation is a very valued objective for modern agriculture. In the case of oliviculture, it is especially relevant due to the high economic value of its production. This paper presents a methodology aimed at achieving that end. Concretely, it comprises an artificial vision algorithm able to detect those olives that are visible in a digital image of an olive tree, captured directly in the field, at night-time and with artificial illumination. First, the image is preprocessed by means of mathematical morphology techniques and statistical filtering to, from this output, generate a subset of images with high probability of containing an olive. Thus, this preprocessing reduces the search space in a magnitude of 103. Next, these subimages are classified by a convolutional neural network as ‘olive’ or ‘discarded’. From a total of 304,483 subimages, extracted from 21 images, the net correctly classified 98.23% of cases, and gave a coefficient of determination R2 of 0.9875 when facing the number of detected olives to the real one. This achieved accuracy indicates that the found algorithm constitutes a solid step towards the implementation of a future system for early yield estimation of olive orchards