Estimación automatizada del peso y calibre de aceitunas mediante análisis de imagen

  1. Juan M. Ponce 1
  2. Arturo Aquino 1
  3. Francisca Segura 1
  4. Borja Millán 1
  5. J. M. Andújar 1
  1. 1 Universidad de Huelva
    info

    Universidad de Huelva

    Huelva, España

    ROR https://ror.org/03a1kt624

Liburua:
XXXIX Jornadas de Automática: actas. Badajoz, 5-7 de septiembre de 2018
  1. Inés Tejado Balsera (coord.)
  2. Emiliano Pérez Hernández (coord.)
  3. Antonio José Calderón Godoy (coord.)
  4. Isaías González Pérez (coord.)
  5. Pilar Merchán García (coord.)
  6. Jesús Lozano Rogado (coord.)
  7. Santiago Salamanca Miño (coord.)
  8. Blas M. Vinagre Jara (coord.)

Argitaletxea: Universidad de Extremadura

ISBN: 978-84-9749-756-5,  978-84-09-04460-3

Argitalpen urtea: 2018

Orrialdeak: 958-966

Biltzarra: Jornadas de Automática (39. 2018. Badajoz)

Mota: Biltzar ekarpena

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DOI: 10.17979/SPUDC.9788497497565.0958 DIALNET GOOGLE SCHOLAR lock_openRUC editor

Garapen Iraunkorreko Helburuak

Laburpena

The sizing and sorting of agricultural commodities is a high relevance activity in food industry. This study, focused on the olive farming sector, presents a solution based on image analysis which allows the automatic and non-invasive estimation of the weight and size (major and minor axis) of a set of olive fruits. Considering two different varieties of olive fruits (Arbequina and Picual), a segmentation algorithm, able to extract from images the needed information to compute the weight and size prediction models, was developed. The effectiveness of the proposed method was assessed by calculating the root-mean-square error (RMSE) produced by the models when applied to the corresponding external validation sets. The measured results show evidences of viability as a base to the development of a low-cost olive fruit grading system based on machine vision.

Datuen iturria: Dialnet