Machine vision system for automatic quality grading of fruit
Fruit and vegetables are normally presented to consumers in batches. The homogeneity and appearance of these have significant effect on consumer decision. For this reason, the presentation of agricultural produce is manipulated at various stages from the field to the final consumer and is generally...
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| Format: | article |
| Language: | Inglés |
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2017
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| Online Access: | http://hdl.handle.net/20.500.11939/4867 |
| _version_ | 1855032189187522560 |
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| author | Blasco, José Aleixos, Nuria Moltó, Enrique |
| author_browse | Aleixos, Nuria Blasco, José Moltó, Enrique |
| author_facet | Blasco, José Aleixos, Nuria Moltó, Enrique |
| author_sort | Blasco, José |
| collection | ReDivia |
| description | Fruit and vegetables are normally presented to consumers in batches. The homogeneity and appearance of these have significant effect on consumer decision. For this reason, the presentation of agricultural produce is manipulated at various stages from the field to the final consumer and is generally oriented towards the cleaning of the product and sorting by homogeneous categories. The project ESPRIT 3, reference 9230 'Integrated system for handling, inspection and packing of fruit and vegetable (SHIVA)' developed a robotic system for the automatic, non-destructive inspection and handling of fruit. The aim of this paper is to report on the machine vision techniques developed at the Instituto Valenciano de Investigaciones Agrarias for the on-line estimation of the quality of oranges, peaches and apples, and to evaluate the efficiency of these techniques regarding the following quality attributes: size, colour, stem location and detection of external blemishes. The segmentation procedure used, based on a Bayesian discriminant analysis, allowed fruits to be precisely distinguished from the background. Thus, determination of size was properly solved. The colours of the fruits estimated by the system were well correlated with the colorimetric index values that are currently used as standards. Good results were obtained in the location of the stem and the detection of blemishes. The classification system was tested on-line with apples obtaining a good performance when classifying the fruit in batches, and a repeatability in blemish detection and size estimation of 86 and 93% respectively. The precision and repeatability of the system, was found to be similar to those of manual grading. (C) 2003 Silsoe Research Institute. All rights reserved. Published by Elsevier Science Ltd. |
| format | article |
| id | ReDivia4867 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia48672025-04-25T14:44:48Z Machine vision system for automatic quality grading of fruit Blasco, José Aleixos, Nuria Moltó, Enrique Fruit and vegetables are normally presented to consumers in batches. The homogeneity and appearance of these have significant effect on consumer decision. For this reason, the presentation of agricultural produce is manipulated at various stages from the field to the final consumer and is generally oriented towards the cleaning of the product and sorting by homogeneous categories. The project ESPRIT 3, reference 9230 'Integrated system for handling, inspection and packing of fruit and vegetable (SHIVA)' developed a robotic system for the automatic, non-destructive inspection and handling of fruit. The aim of this paper is to report on the machine vision techniques developed at the Instituto Valenciano de Investigaciones Agrarias for the on-line estimation of the quality of oranges, peaches and apples, and to evaluate the efficiency of these techniques regarding the following quality attributes: size, colour, stem location and detection of external blemishes. The segmentation procedure used, based on a Bayesian discriminant analysis, allowed fruits to be precisely distinguished from the background. Thus, determination of size was properly solved. The colours of the fruits estimated by the system were well correlated with the colorimetric index values that are currently used as standards. Good results were obtained in the location of the stem and the detection of blemishes. The classification system was tested on-line with apples obtaining a good performance when classifying the fruit in batches, and a repeatability in blemish detection and size estimation of 86 and 93% respectively. The precision and repeatability of the system, was found to be similar to those of manual grading. (C) 2003 Silsoe Research Institute. All rights reserved. Published by Elsevier Science Ltd. 2017-06-01T10:11:13Z 2017-06-01T10:11:13Z 2003 AUG 2003 article Blasco, J., Aleixos, N., Moltó, E. (2003). Machine vision system for automatic quality grading of fruit. Biosystems Engineering, 85(4), 415-423. 1537-5110 http://hdl.handle.net/20.500.11939/4867 10.1016/S1537-5110(03)00088-6 en openAccess Impreso |
| spellingShingle | Blasco, José Aleixos, Nuria Moltó, Enrique Machine vision system for automatic quality grading of fruit |
| title | Machine vision system for automatic quality grading of fruit |
| title_full | Machine vision system for automatic quality grading of fruit |
| title_fullStr | Machine vision system for automatic quality grading of fruit |
| title_full_unstemmed | Machine vision system for automatic quality grading of fruit |
| title_short | Machine vision system for automatic quality grading of fruit |
| title_sort | machine vision system for automatic quality grading of fruit |
| url | http://hdl.handle.net/20.500.11939/4867 |
| work_keys_str_mv | AT blascojose machinevisionsystemforautomaticqualitygradingoffruit AT aleixosnuria machinevisionsystemforautomaticqualitygradingoffruit AT moltoenrique machinevisionsystemforautomaticqualitygradingoffruit |