Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision

Blasco, José; Cubero, Sergio; Gómez-Sanchís, Juan; Mira, Pamella Mattar; Moltó, Enrique

doi:10.1016/j.jfoodeng.2008.05.035

Cited by 139 publications

(54 citation statements)

References 25 publications

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“…This classification was performed using a Bayesian discriminant analysis (Chou & Brown, 1990;Blasco et al, 2009). A set of neighbouring pixels of the same class were taken to be an object.…”

Section: Fruit Identificationmentioning

confidence: 99%

Analysis of the detachment of citrus fruits by vibration using artificial vision

Torregrosa

Albert

Aleixos

et al. 2014

Biosystems Engineering

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ElsevierAlbert Gil, FE.; Aleixos Borrás, MN.; Torregrosa Mira, A.; Ortiz Sánchez, MC.; Blasco Ivars, J. (2014). Analysis of the detachment of citrus fruits by vibration using artificial vision. Biosystems Engineering. 119:1-12. doi:10.1016/j.biosystemseng.2013.12.010. Analysis of the detachment of citrus fruits by vibration using artificial vision AbstractThe vibratory behaviour of citrus fruits is studied using slow-motion cameras in order to gain a better understanding of the parameters involved in fruit detachment when mechanical harvesting is done using shakers. Single citrus fruits with a small portion of stem were vibrated using strokes from 60 mm to 180 mm and frequencies from 3 Hz to 18 Hz. The movement was recorded at 300 fps and the main parameters considered for fruit detachment were determined through the analysis of the video sequences. Image processing algorithms created for this purpose were applied to the automated estimation of the centroid of the fruit, the angle of the stem-pistil axis, and the position of some selected points in the fruit in each frame of the video sequences to obtain dynamic parameters such as the position, speed and acceleration of the fruit during the movement until it is detached. The signals obtained from the image processing were filtered, providing results in accordance with the calibration systems. In general, results suggest that the inertial forces transmitted to the fruit were lower than the tensile forces required to detach the fruit by pulling it in the stem-pistil direction. The largest peaks were observed using long strokes that required fewer cycles for detachment. On the other hand, short strokes combined with high frequencies needed more cycles, and thus a fatigue phenomenon was present. Short strokes and low frequencies were unable to detach some fruit.

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Section: Fruit Identificationmentioning

confidence: 99%

Analysis of the detachment of citrus fruits by vibration using artificial vision

Torregrosa

Albert

Aleixos

et al. 2014

Biosystems Engineering

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“…are not capable of handling and sorting pomegranate arils, thus making it necessary to build specific equipment. This work of J. Blasco et al, [22] describes the development of a computer vision-based machine to inspect the raw material coming from the extraction process and classifies it in four categories. The machine is capable of detecting and removing unwanted material and sorting the arils by colour.…”

Section: Pomegranatementioning

confidence: 99%

“…Heinemann et al, [19] assessed the quality features of the common white Agaricus bisporus mushroom using image analysis in order to inspect and grade the Pomegranate Sorting (Arils) 90% [22] mushrooms by an automated system. Of the 25 samples examined misclassification by the vision system ranged from 8 to 56% depending upon the quality feature evaluated, but averaged about 20%.…”

Section: Mushroomsmentioning

confidence: 99%

Quality Inspection and Grading of Agricultural and Food Products by Computer Vision-a Review

Narendra¹,

Hareesh²

2010

IJCA

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The paper presents the recent development and application of image analysis and computer vision system in quality evaluation of products in the field of agricultural and food. It is very much essential to through light on basic concepts and technologies associated with computer vision system, a tool used in image analysis and automated sorting and grading is highlighted.In India the ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards, there is a need for the growth of accurate, fast and objective quality determination of food and agricultural products. Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy everincreasing production and quality requirements, hence aiding in the development of totally automated processes. This non-destructive method of inspection has found applications in the agricultural and food industry, including the inspection of quality and grading of fruit and vegetable. It has also been used successfully in the analysis of grain characteristics and in the evaluation of foods such as potato chips, meats, cheese and pizza. This paper reviews the progress of computer vision in the agricultural and food field then explores different possible areas of research having a wider scope to enhance the existing algorithms to meet the today"s challenges.

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“…Image analysis represents an important characterization technique in food science and technology (Fernandes et al 2013), not only due to its precision / accuracy, but also because of its non-invasive feature. These characteristics, coupled to negligible time delay, provide a wide range of applications, as characterization occurs by the analysis of image graphical elements, named pixels (Lupetti et al 2005;Blasco et al 2009). Some important applications regarding the use of image analysis for fruit characterization include fruits and vegetables classification in/during the storage (Perera, 2010;Cubero et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Use of image analysis for monitoring the dilution of Physalis peruviana pulp

Licodiedoff

Ribani

Camlofski

et al. 2013

Braz. arch. biol. technol.

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The aim of this work was to develop linear models using the image analysis coupled with density measurements to monitor the dilution of the Physalis juice in the concentrations ranging from 0 to 100% in mass of juice pulp. A sample corresponding to 20% in the mass of juice pulp was for validating purposes and a prediction of 19.9±0.3%. The models with three parameters showed the best predictions, providing this technique with a promising future for the monitoring the dilution of fruit juices.

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Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision

Cited by 139 publications

References 25 publications

Analysis of the detachment of citrus fruits by vibration using artificial vision

Analysis of the detachment of citrus fruits by vibration using artificial vision

Quality Inspection and Grading of Agricultural and Food Products by Computer Vision-a Review

Use of image analysis for monitoring the dilution of Physalis peruviana pulp

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