Device and method for classifying fruits

1 . A fruit identification device comprising:

a fruit inserting unit configured to align a plurality of fruits in a row and to rotate the aligned fruits;

a fruit sorting unit configured to sequentially classify the aligned fruits under control of a control unit;

an image acquisition unit configured to generate pieces of image data for one fruit by sequentially capturing the rotated fruits; and

wherein the control unit configured to:

generate pieces of fruit image data by extracting only a fruit from the pieces of image data;

segment each piece of fruit image data into a plurality of localized image fragments corresponding to different surface regions of the fruit;

determine, using an artificial-intelligence model, a defect probability value for each of the localized image fragments;

aggregate the defect probability values of the localized image fragments to generate a plurality of defect feature certainties for the fruit;

compare the defect feature certainties with a predetermined reference defect feature certainty;

determine that the fruit is in a defective state or a normal state based on the comparison result;

determine a weight of the fruit by using the pieces of fruit image data;

determine a grade of the fruit by using the weight of the fruit;

when the fruit is in the defective state, move the fruit to a defective region through the fruit sorting unit, and generate a visual projection of defect regions onto the localized image fragments for display; and

when the fruit is in the normal state, move the fruit to a normal region corresponding to the grade of the fruit among a plurality of normal regions through the fruit sorting unit.

2 . The fruit identification device of claim 1 , wherein the control unit is configured to:

when at least one of the defect feature certainties is greater than or equal to the reference defect feature certainty, determine that the fruit is in the defective state; and

when all of the defect feature certainties are less than the reference defect feature certainty, determine that the fruit is in the normal state.

3 . The fruit identification device of claim 1 , wherein the defective state includes at least one of a wound, a bruise, a spoilage, or pest infestation of a fruit.

4 . The fruit identification device of claim 1 , further comprising:

a display unit,

wherein the control unit is configured to:

when the fruit is in the defective state, display the grade of the fruit, the defect feature certainties, and the pieces of fruit image data, onto which a defect feature is projected, through the display unit; and

when the fruit is in the normal state, display the grade of the fruit and the defect feature certainties through the display unit.

5 . The fruit identification device of claim 1 , further comprising:

a communication unit,

wherein the control unit is configured to:

generate identification result data for the fruits; and

transmit the identification result data to a mobile terminal of a user through the communication unit.

6 . A method for identifying a fruit, the method comprising:

aligning, by a control unit, a plurality of fruits in a row and rotating the aligned fruits, through a fruit inserting unit;

generating, by the control unit, pieces of image data for one fruit by sequentially capturing the rotated fruits through an image acquisition unit;

generating, by the control unit, pieces of fruit image data by extracting only a fruit from the pieces of image data;

segmenting, by the control unit, each piece of fruit image data into a plurality of localized image fragments corresponding to different surface regions of the fruit;

determining, by the control unit and using an artificial-intelligence model, a defect probability value for each of the localized image fragments;

aggregate, by the control unit, the defect probability values of the localized image fragments to generate a plurality of defect feature certainties for the fruit;

compare, by the control unit, the defect feature certainties with a predetermined reference defect feature certainty;

determine, by the control unit, that the fruit is in a defective state or a normal state based on the comparison result;

determining, by the control unit, a weight of the fruit by using the pieces of fruit image data and determining a grade of the fruit by using the weight of the fruit;

when the fruit is in the defective state, moving, by the control unit, the fruit to a defective region, and generating a visual projection of defect regions onto the localized image fragments for display; and

when the fruit is in the normal state, moving, by the control unit, the fruit to a normal region corresponding to the grade of the fruit among a plurality of normal regions.

7 . The method of claim 6 , wherein the determining that the fruit is in the defective state or the normal state includes:

when at least one of the defect feature certainties is greater than or equal to the reference defect feature certainty, determining that the fruit is in the defective state; and

when all of the defect feature certainties are less than the reference defect feature certainty, determining that the fruit is in the normal state.

8 . The method of claim 6 , wherein the defective state includes at least one of a wound, a bruise, a spoilage, or pest infestation of a fruit.

9 . The method of claim 6 , further comprising:

generating, by the control unit, identification result data for the fruits; and

transmitting, by the control unit, the identification result data to a mobile terminal of a user through a communication unit.

10 . The fruit identification device of claim 1 ,

wherein the artificial-intelligence model comprises a convolutional neural network including a plurality of convolution layers, pooling layers, and fully connected layers configured to output the defect probability values for the localized image fragments.

11 . The fruit identification device of claim 1 ,

wherein determining that the fruit is in the defective state comprises determining that at least one of the defect feature certainties is equal to or greater than 90 percent (90%).

12 . The fruit identification device of claim 1 ,

wherein the control unit is further configured to generate a visual projection of defect regions onto corresponding localized image fragments for display.