The embedded systems in the industrial, especially image processing, is increasingly leading to the study of production automation systems such as fruit sorting. Post-harvest sorting system implemented by the industry is manual, so it’s not effective. The solution was to conduct research aimed at modifying post-harvest sorting tools by engineering tomato and orange sorting machines based on their color. The method uses image processing. It’s the most efficient alternative in terms of cost and complexity of hardware design, does not require many sensors, but produces an accurate output. The camera is placed on the mechanical sorting machine system, taking images to determine the sorting execution after the fruit color type are recognized. The results of the research were carried out through several tests, namely: light intensity, color image data, and organoleptics. Light intensity test showed that the position of the tool had a value of 0.78% of the outside light disturbance. Color image shows the range of ripeness values (R/G) for raw tomatoes 0<=1.04; half ripe tomatoes 1.04<=1.39; ripe tomatoes 1.39<=3.59; raw orange 0<=0.92; undercooked oranges 0.92<=0.98; and ripe oranges 0.98<=1.66. Organoleptic test from five observers had the same results as the reading on the fruit sorting tool. Keywords : engineering, fruit maturity, oranges, sorting machines, tomatoes
Development of a Fruit Sorting System using Statistical Image Processing
