JPS6111679B2 - - Google Patents

Description

[Detailed description of the invention] As automation and labor saving are progressing in various industrial fields, the grade (mainly the quality of shape and color) and class (size ), a large amount of manpower and time is currently being spent on screening.

The present invention relates to a fruit and vegetable sorting device that automatically determines the grade of fruits and vegetables.

Conventionally, this type of fruit and vegetable sorting apparatus has been of the gravimetric type, mechanical type, and light beam type. Gravimetric methods measure the weight of fruits and vegetables regardless of their appearance, such as their shape or color.
This is a convenient sorting method when trading based on weight. However, even if the weight is the same, the size is not necessarily the same, and items that have an unsightly shape or appearance may be mixed in, making it difficult to carry out the packaging process smoothly or reducing the transaction price. There were problems, such as a decline in performance. In addition, the mechanical type
For example, this method sorts fruits and vegetables based on the spacing between the rollers that convey them, and is suitable for rough sorting, but it is not sufficient for sufficient selection because it simply sorts fruits and vegetables based on a single criterion, the maximum diameter. It is difficult to say that it is a fruitful device, and its results do not necessarily match the actual appearance. Another drawback is that the quality deteriorates due to "damage" that occurs when passing between the rollers.

On the other hand, the light beam type is almost the same as the mechanical type except that it uses light, but it has the advantage that it does not suffer from "damage" due to penetration, unlike the mechanical type. However, there still remains the drawback that the screening results do not match the actual appearance. Furthermore, among these light beam methods, there is a method called the surface line method that measures the width and length of the target fruit or vegetable, but it has a drawback in that its measurement accuracy is difficult.

This invention was made in view of the above-mentioned current situation, and eliminates unfair manual judgment or sorting, and enables high-speed and accurate discrimination and sorting from various viewpoints to determine the appearance of the product when packed in a box. Another object of the present invention is to provide a fruit sorting device that can uniformize grain alignment, improve marketability, and save labor.

The feature of this invention is that the fruits and vegetables to be measured are photographed by a video sensor and a color video sensor, and the image signals obtained by the video sensors are subjected to arithmetic processing, thereby determining the classification of fruits and vegetables according to their size and shape. In addition, the grade of fruits and vegetables is determined based on brightness, hue, and saturation by calculating signals obtained from a color video sensor, and the fruits and vegetables are sorted by integrating the determination results. .

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing an embodiment of the present invention, FIG. 2 is a block diagram showing a color determining device, FIG. 3 is a block diagram showing a shape determining device, and FIG. 4 is a block diagram showing an embodiment of the present invention. FIG. 2 is an explanatory diagram for explaining a specific example.

In Figure 1, FR is the fruit or vegetable to be measured, BC is the bucket conveyor, SE ₁ is the color video sensor,
SE ₂ is an ITV (industrial television) camera, FS is a photo sensor, DE ₁ is a color judgment device, DE ₂ is a shape judgment device, SY is a general judgment device, and SR is a shift register.

The fruits and vegetables FR to be measured are placed on a bucket conveyor BC and transported, for example, in the direction of the arrow in the figure at a predetermined speed. Although only one bucket conveyor is shown in the figure, two bucket conveyors may be provided to measure two pieces at a time. Fruits and vegetables transported FR
First, the coloring state of the surface is detected by the color sensor SE ₁ , and its grade (Excellent A, Excellent B, Good C) is determined by the color determining device DE ₁ . Next, as the bucket conveyor BC progresses, the fruit and vegetable FR becomes ITV.
When the camera SE ₂ enters the field of view, a shutter (not shown) opens to take a picture of the fruits and vegetables, and the shape determining device
DE ₂ performs predetermined arithmetic processing and judgment to classify and grade fruits and vegetables (extra large LLL, extra large LL, large L, medium M, small S). In addition,
The grading carried out here is to judge Excellent A, Excellent B, or Good C depending on the degree of deformation, such as when something that should originally be round becomes an ellipse. The comprehensive judgment device SY combines these judgment results and stores the results in the shift register SR. The determination result stored in the shift register SR is provided to a sorting device (not shown) at a predetermined timing based on a signal from the photo sensor FS. The photo sensor FS outputs a judgment timing signal t to the judgment devices SE ₁ and SE ₂ in accordance with the photographing timing of the camera, and also sorts the fruits and vegetables FR that have arrived at a predetermined position on the bucket conveyor BC at a predetermined timing. Outputs a signal to discharge through the device. The data regarding fruits and vegetables sorted in this way can be displayed by producer and sorting as needed, and can be printed out as individual data and daily report data.

Next, the color determination device will be explained with reference to FIG.

The reflected light from fruits and vegetables is separated into wavelength components of predetermined colors (red, green, and blue) and taken out by a filter, and converted into electrical signals by photoelectric conversion elements, and then sent to amplifiers 1 ₁ to 1. Calculation/judgment device 2 via ₃
given to. The calculation/judgment device 2 performs the following calculations from the signal values of the three components and their mixing ratios to determine the lightness (color intensity or brightness) _Li , hue (color tone) _La , and saturation (vividness). degree) L _b is calculated.

L _i =√・100(%) L _a =175(1.02X-Y)/√ L _b =70(Y-0.847Z)/√ Here, X, Y, and Z are red, green, and blue, respectively. It is a signal component value.

These calculation results are compared with a predetermined value preset in the setting device 4, and the grade (excellent A, excellent B, good C) is determined. The matrix circuit 3 is
A predetermined output terminal is selected based on this determination result, and the determined output is provided to the comprehensive determination device shown in FIG.

Note that a commercially available color sensor can be used as such a color sensor.

Next, the shape determination device will be explained with reference to FIG.

The image signal obtained by photographing with the ITV camera undergoes noise removal in the preprocessing and binarization circuit 11, binarization by comparison with a predetermined level signal, etc., and is stored in the pixel memory 12. . Based on the data stored in the pixel memory 12, the calculation/judgment circuit 13 determines the maximum cross-sectional area S ₁ and maximum circumference L _M of the target fruit or vegetable converted into a binary image (pixel). For example, in the case of area, since the area of one pixel is known in advance, it can be determined by counting the number of significant pixels in the binarized signal, that is, the number of pixels indicating fruits and vegetables. I can do it. On the other hand, the maximum perimeter L _M can be determined by counting the number of pixels that form the boundary between the fruit and vegetable pattern and the background, since the unit length of each pixel is known in advance, or by counting the number of pixels that form the boundary between the fruit and vegetable pattern and the background, or by Simply count the number of pieces in the straight line part, and approximate the other parts with diagonal lines, that is, calculate each number by multiplying the counted number by √2, and add these numbers to easily calculate. The methods for determining the area and perimeter (boundary length) are well known, but if necessary, please refer to, for example, JP-A-54-
Please refer to Publication No. 25634.

The parameter obtained as described above, that is, the maximum cross-sectional area _S1 , is used as a parameter when determining the class (size) of the fruit or vegetable to be measured.

On the other hand, in order to determine the shape of fruits and vegetables (external appearance, such as whether or not they are distorted or symmetrical),
The roundness is determined from the maximum circumferential length L _M obtained above. This means that the value obtained by dividing the circumference L _M by Pi is the diameter (L _M /π), and from this diameter, calculate the area = (Pi) x (Diameter) ² x 1/4. Then, the virtual area S ₂ is determined, and this area value S ₂ is compared with the area value S ₁ previously determined by image processing. In other words, the virtual area S ₂ obtained from the perimeter L _M will almost match the measured cross-sectional area S ₁ if the cross section of the measured fruit or vegetable is a perfect circle, but for example, if the fruit or vegetable is elliptical or has a hollow fruit ( Items that have a cavity inside, such as peppers or tomatoes,
The effect of this cavity is that the surface becomes wrinkled and the circumference becomes substantially longer. ), it becomes larger than the measured cross-sectional area _S1 , and this is used to determine whether it is an ellipse, that is, its roundness. When the difference between these, that is, L _M ² /4π-S ₁ , is close to zero, it is determined to be a round fruit, and when this difference exceeds a predetermined value determined by experiment, it is determined to be an elliptic fruit or a hollow fruit. do.

Hereinafter, an example of a hollow fruit (such as a tomato) will be explained with reference to FIG.

In this case, in order to perform class determination based on size, the area value _S1 is calculated using the shape determining device described above, and the calculated result is compared with a predetermined setting value to determine whether the area is very large (LLL) or extra large. (LL), large (L), normal (M), and small (S).

On the other hand, to determine the grade, roundness is determined and it is determined whether the fruit is an ellipse or a hollow fruit. Furthermore, to distinguish between an elliptic fruit and a hollow fruit, the diameters R _{x and} R _y in the x-axis direction and the y-axis direction, as shown in Figure 4 a and b,
are determined respectively, and when R _x ≠ R _y , it is assumed to be an ellipse, and R
When _x = R _y, it is determined that the fruit is hollow. In addition, Figure 4a
Figure b shows an oval fruit, and figure b shows a hollow fruit.

On the other hand, the color determination device determines the grade based on color. That is, the color of tomatoes is divided into three categories: green (immature), intermediate, and red (overripe), and the tomato is graded into excellent B, excellent A, and good C, respectively, taking into account lightness or chroma information.

As described above, according to the present invention, the classification of fruits and vegetables is automatically determined at high speed and with high precision, so that manual unfairness is corrected and packaging work is reduced. This makes it easier to store products, and because the colors and shapes of the packaged products are uniform, transaction prices can be increased.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing an embodiment of the present invention, FIG. 2 is a block diagram showing a color determining device, FIG. 3 is a block diagram showing a shape determining device, and FIG. 4 is a block diagram showing an embodiment of the present invention. FIG. 2 is an explanatory diagram for explaining a specific example. Symbol explanation, SE ₁ ……Color sensor, SE ₂ ……
ITV camera, FR...Fruits and vegetables, BC...Bucket conveyor, FS...Photo switch, DE ₁ ...Color judgment device, DE ₂ ...Shape judgment device, SY...Comprehensive judgment device, SR...Shift register, 1 ...Amplifier, 2, 13... Arithmetic/judgment device, 3... Matrix, 4, 14... Setting device, 11... Preprocessing and binarization circuit, 12... Pixel memory.

Claims (1)

[Claims] 1. First and second imaging means arranged at a predetermined interval from each other to individually photograph fruits and vegetables to be measured, and binarizing the imaging signal from the first imaging means at a predetermined threshold level. a storage means for storing the binarized imaging signal, a maximum cross-sectional area _S1 and a maximum cross-sectional circumference L _M of fruits and vegetables based on the stored data; A virtual area S ₂ is determined from the diameter by dividing the circumference L _M by pi, and the difference between the virtual area S ₂ and the measured area S ₁ is calculated to determine the perfect circle of the fruit or vegetable. a first determining means that calculates a parameter representing roundness and compares the roundness and the measured area _S1 with predetermined set values, respectively, to determine the class according to the shape and size of fruits and vegetables; an extraction means for extracting individual signal components of a predetermined wavelength from the imaging signal of the second imaging means; and an extraction means for calculating the brightness, hue, and saturation of fruits and vegetables from the values of each signal component extracted by the means and the mixing ratio thereof. and a second determining means for determining the grade of fruits and vegetables based on their color by comparing the calculation results with a predetermined set value, and the fruits and vegetables are classified based on the determination results of both the first and second determining means. A fruit and vegetable sorting device characterized by sorting.