Agronomical and Qualitative Evaluation of Processing Tomato Cultivars Grown "In Piana Del Sele"

D'Amore, R.; Palumbo, A.D.; Morra, Luigi; Sozzi, A.; Fusco, A.; Lahoz, E.

doi:10.17660/actahortic.1994.376.4

Cited by 1 publication

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“…Tomatoes were subjected to analysis in order to detect any damage or defect (Kurita et al, 2006). The amount of oil contained in the pulp of the fruit micro carp is directly related with certain optical properties observed in the band of red, blue, green, as in the case of the palm (D'Amore et al, 1993). One of the most interesting works, confronting the problem of selecting the optimal harvesting time for olive in order to maximize oil content, is proposed by Ram et al (2009).…”

Section: Introductionmentioning

confidence: 99%

ANN-based method for olive Ripening Index automatic prediction

Furferi

Governi

Volpe

2010

Journal of Food Engineering

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The influence of ripening degree of drupes during the harvesting period is well established in olive oil sector. A range of methods for expressing the stage of maturity of olives have been proposed in scientific literature. One of the most commonly adopted methods provides the evaluation of a Ripening Index (RI) on the basis of olive skin and pulp colour. Such an index, usually performed by a panel of experts, is indicative of the ripening degree of olive varieties and may be helpful for increasing repeatability in obtaining desired quail-quantitative characteristics of olive oil.Unfortunately, the RI evaluation technique is time-consuming, subjective (depending on expert skill) and depends on environmental conditions that may affect colour appearance of olives. This work describes a novel method for rapid, automatic and objective prediction of the Ripening Index of an olive lot. The method integrates a Machine Vision system, capable of performing a colourbased raw prediction of RI, with an Artificial Neural Network (ANN) based algorithm to refine it.Experimental results demonstrate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%