STONE FRUIT: USE OF DIGITAL IMAGE FOR POSTHARVEST QUALITY EVALUATION

• Grant number: 13/25728-7
• Support Opportunities: Regular Research Grants
• Start date: May 01, 2015
• End date: April 30, 2017
• Field of knowledge: Agronomical Sciences - Agronomy
• Principal Investigator: Juliana Sanches de Laurentiz
• Grantee: Juliana Sanches de Laurentiz
• Host Institution: Instituto Agronômico (IAC). Agência Paulista de Tecnologia dos Agronegócios (APTA). Campinas , SP, Brazil
• City of the host institution: Campinas
• Associated researchers: Antonio Carlos Loureiro Lino ; Marcos Valério Gebra da Silva

Abstract

The development of science and technology, especially information technology, has made possible the use of many non-destructive methods for the analysis of materials and can also be applied on fruits. Nowadays, the fruit industry needs non-destructive techniques for the selection and certification online high quality fruit. However, there is a gap between what the industry wants and determining which is currently evaluated by the existing non-destructive methods. In destructive methods, a sample of fruit should be analyzed to estimate the quality of a lot. Besides the economic loss due to the destruction of part of the fruit, there is the problem of representativeness of the sample in relation to the lot as a whole, distributional and compositional heterogeneity. Non-destructive methods outweigh these problems because they can be applied individually to each fruit in the process, eliminating the possible discrepancies between batches and samples of fruit without destroying the amount of fruit required in the sampling process. One of the non-destructive techniques that have been studied in the agricultural area is machine vision, whose purpose is to promote the objectivity of the classification of products. Within this context, this project aims to develop methodologies for nondestructive evaluation to determine the postharvest quality of stone fruit using digital images. The goal has been the development of non-destructive methodologies for evaluating the quality of fruit and technological innovation for sorting and classification. (AU)