Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform

The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated gr...

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Autores principales: Cubero, Sergio, Aleixos, Nuria, Albert, Francisco, Torregrosa, Antonio, Ortiz, Coral, García-Navarrete, Óscar L., Blasco, José
Formato: Artículo
Lenguaje:Inglés
Publicado: 2017
Acceso en línea:http://hdl.handle.net/20.500.11939/5059
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author Cubero, Sergio
Aleixos, Nuria
Albert, Francisco
Torregrosa, Antonio
Ortiz, Coral
García-Navarrete, Óscar L.
Blasco, José
author_browse Albert, Francisco
Aleixos, Nuria
Blasco, José
Cubero, Sergio
García-Navarrete, Óscar L.
Ortiz, Coral
Torregrosa, Antonio
author_facet Cubero, Sergio
Aleixos, Nuria
Albert, Francisco
Torregrosa, Antonio
Ortiz, Coral
García-Navarrete, Óscar L.
Blasco, José
author_sort Cubero, Sergio
collection ReDivia
description The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R-2 coefficient of 0.993 for size estimation and an R-2 coefficient of 0.918 for the colour index.
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spelling ReDivia50592025-04-25T14:45:16Z Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform Cubero, Sergio Aleixos, Nuria Albert, Francisco Torregrosa, Antonio Ortiz, Coral García-Navarrete, Óscar L. Blasco, José The mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R-2 coefficient of 0.993 for size estimation and an R-2 coefficient of 0.918 for the colour index. 2017-06-01T10:11:37Z 2017-06-01T10:11:37Z 2014 FEB 2014 article acceptedVersion Cubero, S., Aleixos, N., Albert, F., Torregrosa, A., Ortiz, C., Garcia-Navarrete, O., Blasco, J. (2014). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture, 15(1), 80-94. 1385-2256 http://hdl.handle.net/20.500.11939/5059 10.1007/s11119-013-9324-7 en Info:eu-repo/grantAgreement/MICINN/Programa Nacional de Investigación Fundamental/RTA2009-0018-C02-01 Info:eu-repo/grantAgreement/MICINN/Programa Nacional de Investigación Fundamental/RTA2009-0018-C02-02 This research work has been funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) and the European FEDER funds (projects RTA2009- 00118-C02-01 and RTA2009-00118-C02-02). openAccess Impreso
spellingShingle Cubero, Sergio
Aleixos, Nuria
Albert, Francisco
Torregrosa, Antonio
Ortiz, Coral
García-Navarrete, Óscar L.
Blasco, José
Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform
title Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform
title_full Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform
title_fullStr Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform
title_full_unstemmed Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform
title_short Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform
title_sort optimised computer vision system for automatic pre grading of citrus fruit in the field using a mobile platform
url http://hdl.handle.net/20.500.11939/5059
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