Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted In this study, a grading system based on machine vision was developed for grading figs.An image processing algorithm was introduced to extract fig marketing features such as color, size and split size.An algorithm for detecting the whole of fig in every trigger of camera was developed.This systems fabricated feeding unit, simulation unit, machine vision and separation unit.This system shod which has good ability for grading figs into the five classes. Fig is a horticultural product which requires sorting at the postharvest stage before being marketed. In this study, a grading system based on machine vision was developed for grading figs. The system hardware was composed of a feeder, a belt conveyor, a CCD camera, a lighting system, and a separation unit. Three quality indices, namely color, size, and split size, re first classified by fig-processing experts into the five classes. Then, the images of the fig samples re captured using a machine vision system. First, the length of pixels in each image and longitudinal coordinates of the center of gravity of fig pixels re extracted for calculating the nozzle eject time. For extracting the three quality indices of each class, a machine vision algorithm was developed. This algorithm determined color intensity and diameter of each fig as the indicators of its color and size, respectively. For calculating the split area, the images re first binarized by using the color intensity difference beten the split and other parts of the fruit in order to determine the area of the split section. A grading algorithm was also coded in Lab-VIEW for sorting figs based on their quality indices extracted by the image processing algorithm into five qualitative grades. In the grading algorithm, the values of these features re compared with the threshold value that was predetermined by an expert. Results shod that the developed system improved the sorting accuracy for all the classes up to 95.2%. The system's mean rate was 90kg/h for processing and grading figs.
['Mehrdad Baigvand', 'Ahmad Banakar', 'S. Minaei', 'Jalal Khodaei', 'Nasser Behroozi-Khazaei']
I am writing a research paper on a idea Machine vision system for grading of dried figs
[{"from": "human", "value": "I am writing a research paper on a idea Machine vision system for grading of dried figs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted In this study, a grading system based on machine vision was developed for grading figs.An image processing algorithm was introduced to extract fig marketing features such as color, size and split size.An algorithm for detecting the whole of fig in every trigger of camera was developed.This systems fabricated feeding unit, simulation unit, machine vision and separation unit.This system shod which has good ability for grading figs into the five classes. Fig is a horticultural product which requires sorting at the postharvest stage before being marketed. In this study, a grading system based on machine vision was developed for grading figs. The system hardware was composed of a feeder, a belt conveyor, a CCD camera, a lighting system, and a separation unit. Three quality indices, namely color, size, and split size, re first classified by fig-processing experts into the five classes. Then, the images of the fig samples re captured using a machine vision system. First, the length of pixels in each image and longitudinal coordinates of the center of gravity of fig pixels re extracted for calculating the nozzle eject time. For extracting the three quality indices of each class, a machine vision algorithm was developed. This algorithm determined color intensity and diameter of each fig as the indicators of its color and size, respectively. For calculating the split area, the images re first binarized by using the color intensity difference beten the split and other parts of the fruit in order to determine the area of the split section. A grading algorithm was also coded in Lab-VIEW for sorting figs based on their quality indices extracted by the image processing algorithm into five qualitative grades. In the grading algorithm, the values of these features re compared with the threshold value that was predetermined by an expert. Results shod that the developed system improved the sorting accuracy for all the classes up to 95.2%. The system's mean rate was 90kg/h for processing and grading figs."}]