Swingle citrumelo seed vigor and storability associated with fruit maturity classes based on RGB parameters
DOI:
https://doi.org/10.1590/1678-992x-2016-0173Palabras clave:
image analysis, computer vision, fruit maturation, citrus rootstock, seedling vigorResumen
Citrus seeds normally have low storability. Identifying an optimal fruit harvest time for production of high vigor seeds is important for nurseries; however, identifying this stage for Swingle citrumelo fruits has been based only on visual color examination, and research related to this parameter has been inconsistent. The main objective of this study was to evaluate a red-green-blue (RGB) color measurement system for successful identification of Swingle citrumelo fruits possessing seeds with maximum physiological potential and storability. Fruits were harvested at three ripening stages identified as green (G), greenish-yellow (GY), and yellow (Y) pericarp, photographed, and the images processed using ImageJ software. Data were expressed as the average pixel value of R, G and B color components and a mean RGB pixel value (R+G+B/3). After harvest, seeds were evaluated for water content, germination, seedling emergence, length, dry mass and vigor as measured by the Seed Vigor Imaging System - SVIS (uniformity and vigor indexes) after 0 and 5 months storage (5 °C and 65 % relative humidity). Percentage of ruptured coat seeds was also evaluated after extraction. The R color component provided the best identification of each G, GY and Y fruits. Seeds extracted from GY fruits had higher storage potential compared with seeds from G and Y fruits. Thus, precise fruit ripening classification can be generated using the RGB color system to identify the best time for harvest to obtain seeds with greater physiological and storage potential.Descargas
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Publicado
2017-10-01
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Crop Science
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Todo el contenido de la revista, excepto donde esté identificado, está protegido por el Creative Commons del tipo BY-NCCómo citar
Swingle citrumelo seed vigor and storability associated with fruit maturity classes based on RGB parameters. (2017). Scientia Agricola, 74(5), 357-363. https://doi.org/10.1590/1678-992x-2016-0173