The content of stone cells is an important factor for pear breeding as a high content indicates severely reduced fruit quality in terms of fruit taste.Although the frozen-HCI method is currently a common method used t...The content of stone cells is an important factor for pear breeding as a high content indicates severely reduced fruit quality in terms of fruit taste.Although the frozen-HCI method is currently a common method used to evaluate stone cell content in pears,it is limited in incomplete separation of stone cell and pulp and is time consuming and complicated.Computeraided research is a promising strategy in modern scientific research for phenotypic data collect!on and is increasingly used in studying crops.Thus far,we lack a quantitative tool that can effectively determine stone cell content in pear fruit.We developed a program,Pearprocess,based on an imaging protocol using computer vision and image processing algorithms applied to digital images.Using photos of hand-cut sections of pear fruit stained with phloroglucin-HCI(Wiesner's reagent),Pearprocess can extract and analyze image-based data to quantify the stone cell-related traits measured in this study:number,size,area and density of stone cell.We quantified these traits for 395 pear accessions by Pearprocess and revealed large variation in different pear varieties and species.The number of stone cells varied greatly from value of 138 to 2866,the density of stone cells ranged from 0.0019 to 0.0632 cm2 cm-2,the distribution of stone cell area ranged from 0.06 to 2.02 cm2,and the stone cell size was between 2e-4 and 1e-3 cm2.Moreover,trait data were correlated with fruit taste data.We found that stone cell density is likely the most important factor affecting the taste of pear fruit.In summary,Pearprocess is a new cost-effective web-application for semi-automated quantification of two-dimensional phenotypic traits from digital imagery using an easy imaging protocol.This simpler,feasible and accurate method to evaluate stone cell traits of fruit is a promising new tool for use in evaluating future germplasms for crop breeding programs.展开更多
基金This work was supported by the National Natural Science Foundation of China(31725024),the National Key Research and Development Program of China(2018YFD1000200),the earmarked fund for China Agriculture Research System(CARS-28),and the earmarked fund for Jiangsu Agricultural Industry Technology System,China(JATS 2018-277).
文摘The content of stone cells is an important factor for pear breeding as a high content indicates severely reduced fruit quality in terms of fruit taste.Although the frozen-HCI method is currently a common method used to evaluate stone cell content in pears,it is limited in incomplete separation of stone cell and pulp and is time consuming and complicated.Computeraided research is a promising strategy in modern scientific research for phenotypic data collect!on and is increasingly used in studying crops.Thus far,we lack a quantitative tool that can effectively determine stone cell content in pear fruit.We developed a program,Pearprocess,based on an imaging protocol using computer vision and image processing algorithms applied to digital images.Using photos of hand-cut sections of pear fruit stained with phloroglucin-HCI(Wiesner's reagent),Pearprocess can extract and analyze image-based data to quantify the stone cell-related traits measured in this study:number,size,area and density of stone cell.We quantified these traits for 395 pear accessions by Pearprocess and revealed large variation in different pear varieties and species.The number of stone cells varied greatly from value of 138 to 2866,the density of stone cells ranged from 0.0019 to 0.0632 cm2 cm-2,the distribution of stone cell area ranged from 0.06 to 2.02 cm2,and the stone cell size was between 2e-4 and 1e-3 cm2.Moreover,trait data were correlated with fruit taste data.We found that stone cell density is likely the most important factor affecting the taste of pear fruit.In summary,Pearprocess is a new cost-effective web-application for semi-automated quantification of two-dimensional phenotypic traits from digital imagery using an easy imaging protocol.This simpler,feasible and accurate method to evaluate stone cell traits of fruit is a promising new tool for use in evaluating future germplasms for crop breeding programs.
