Olive fruit maturity detection method and system

Abstract

The invention discloses an olive fruit maturity detection method and system, and the method comprises the steps: firstly constructing a first standard model and a second standard model, then obtaining a comparison image and an actual hardness calculation parameter of a to-be-detected olive fruit, and calculating a color degree score of the olive fruit according to the comparison image and the first standard model, calculating a hardness score according to the actual hardness calculation parameter and a second standard model, and judging the maturity of the olive fruits according to the color score and the hardness score; olea europaea fruits correspond to different color changes and different hardness changes in different ripening stages, the surface layer changes of the fruits are reflected through the color changes, the internal changes of the fruits are reflected through the hardness of the fruits, the fruits are evaluated at the same time from the inside and the outside, and the evaluation accuracy is improved. Misjudgment caused by premature color change or abnormal change of the fruits can be effectively avoided, and the accuracy of maturity detection is improved; the determination of the fruit maturity can be standardized, and the dependence of the determination of the fruit maturity on manpower is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of fruit detection, and in particular to a method and system for detecting the maturity of olive fruit. Background Art

[0002] Determining olive ripeness is crucial for harvest timing and oil quality. Premium extra virgin olive oil is typically harvested within the appropriate ripeness period and pressed within six hours of harvest. Olive ripeness directly impacts the acidity, polyphenol content, and flavor of extra virgin olive oil and related products. Unripe or overripe olives significantly reduce their natural nutritional content and impact product quality.

[0003] The maturity of olives is generally determined visually by color changes and / or surface gloss. The industry generally uses a four-level maturity scale (green-ripe, color-changing, fully ripe, and overripe), with olives corresponding to different color changes at different stages of maturity. Specifically, when unripe, olives are typically bright green or dark green, then turn yellow-green, and finally purple-black or dark black when fully ripe.

[0004] However, during olive cultivation, the maturity of olive fruit varies significantly from plant to plant. For example, different varieties, varying light intensities, and varying irrigation conditions all significantly impact the maturity of olive fruit within the same plot or region. Consequently, some olive fruits may appear to have completed color change or have changed color early, but are actually not yet ripe. Meanwhile, some olive fruits may appear to have not yet completed color change or matured, but are actually approaching maturity. Therefore, simply judging the ripeness of olive fruit based on its color change is not a scientific indicator.

[0005] In addition to changes in fruit color, changes in fruit hardness also directly affect fruit maturity. As the fruit continues to mature, its hardness will continue to decrease. However, the color of olives from light to dark under different varieties, different light, and different water irrigation conditions does not have a direct positive correlation with hardness.

[0006] Currently, the existing technology generally relies on manual determination of fruit ripeness to schedule harvesting. However, manual determination of fruit ripeness is limited to observing the fruit's skin color and is also subject to the influence of worker experience. While there are existing methods for machine-based olive ripeness testing, these methods only use color as a single indicator, resulting in low accuracy. Summary of the Invention

[0007] The main purpose of this application is to provide an olive fruit maturity detection method and system, aiming to solve the defect of low detection accuracy in the existing technology.

[0008] This application achieves the above objectives through the following technical solutions: A method for detecting the maturity of olive fruit comprises the following steps: Constructing a first standard model and a second standard model for judging the maturity of olive fruits; Obtaining a comparison image and actual hardness calculation parameters of the olive fruit to be tested; Calculating the color score of the olive fruit to be tested based on the comparison image and the first standard model; Calculating the hardness score of the olive fruit to be tested according to the actual hardness calculation parameter and the second standard model; The maturity of the olives to be tested is determined according to the color score and the hardness score.

[0009] Optionally, constructing a first standard model for judging olive fruit maturity comprises the following steps: Obtain a number of ripe olive fruits as standard fruits; Obtain standard images of each standard fruit respectively; generating a standard hue value and a standard saturation value according to each of the standard images; Generate a color difference value calculation formula based on the standard hue value and the standard saturation value; A color score calculation formula is generated according to the color difference value, and the color score calculation formula is output as a first standard model.

[0010] Optionally, the color difference value calculation formula is: ; The expression of the first standard model is: ; Where H0 represents the standard hue value, S0 represents the standard saturation value, E max Indicates the theoretical maximum value of color difference, H indicates the actual hue value, and S indicates the actual saturation value; Optionally, constructing a second standard model for judging olive fruit maturity comprises the following steps: Obtain a number of ripe olive fruits as standard fruits; The standard hardness value, standard density value and standard sound velocity value of each standard fruit are tested respectively; According to each standard hardness value, standard density value and standard sound velocity value, the calculation formula of the actual hardness value is generated by least square fitting; A hardness score calculation formula is generated according to the actual hardness value, and the hardness score calculation formula is output as a second standard model.

[0011] Optionally, the actual hardness value calculation formula is: , where k, m and b are all constants, ρ represents the actual density of the olive fruit to be tested, V s Represents the actual sound speed value; the expression of the second standard model is: , where ΔQ max Indicates the maximum change in hardness value, and its calculation expression is ΔQ max =Q1-Q0, Q represents the actual hardness value of the olive fruit to be tested, Q0 represents the ideal hardness value of the mature fruit, and Q1 represents the maximum hardness value of the olive fruit.

[0012] Optionally, calculating the color score of the olive fruit to be tested based on the comparison image and the first standard model comprises the following steps: Acquire a comparison image, divide the comparison image into a plurality of standard squares, and output the standard squares completely filled with the olive fruit image as unit comparison images; Obtaining the actual hue value and saturation value of each unit comparison image respectively; generating a weight coefficient for each of the unit comparison images according to the classification condition, the unit actual hue value, and the unit actual saturation value; Calculating the actual hue value and the actual saturation value according to the actual hue value of each unit, the actual saturation value of each unit and the weight coefficient of the comparison image of each unit; A colorimetry score is calculated according to the actual hue value, the actual saturation value, and a first standard model.

[0013] Optionally, the actual hue value is calculated as ; The calculation formula of the actual saturation value is ; Where n represents the number of unit comparison images, i represents the number of each unit comparison image, W i Indicates the weight coefficient of each unit comparison image, its value; Δs i Indicates the actual hue value of the unit, Δh i Indicates the actual saturation value of the unit, p i Represents the area of ​​the cell alignment image.

[0014] Optionally, calculating the hardness score of the olive fruit to be tested according to the actual hardness calculation parameter and the second standard model comprises the following steps: Acquiring actual hardness calculation parameters, wherein the actual hardness calculation parameters include an actual density value and an actual sound velocity value; Calculate the actual hardness value according to the actual density value, actual sound velocity value and hardness value calculation formula; Retrieving the second standard calculation model; A hardness score is calculated according to the actual hardness value and the second standard calculation model.

[0015] Optionally, the maturity calculation formula is Q=αA+βB, where α and β both represent weight values, and α+β=1.

[0016] Accordingly, the present application also discloses a detection system based on the above detection method, comprising: A standard model generation module, used to construct a first standard model and a second standard model for judging the maturity of olive fruits; A parameter acquisition module is used to obtain a comparison image and actual hardness calculation parameters of the olive fruit to be tested; A first calculation module is used to calculate the color score of the olive fruit to be tested based on the comparison image and the first standard model; A second calculation module is used to calculate the hardness score of the olive fruit to be tested according to the actual hardness calculation parameter and the second standard model; The determination module is used to determine the maturity of the olives to be tested based on the color score and the hardness score.

[0017] Compared with the prior art, this application has the following beneficial effects: This application first constructs a first standard model and a second standard model, then obtains a comparison image and actual hardness calculation parameters of the olive fruit to be tested, calculates the color score of the olive fruit based on the comparison image and the first standard model, and then calculates the hardness score based on the actual hardness calculation parameters and the second standard model, and comprehensively determines the maturity of the olive fruit based on the color score and the hardness score.

[0018] Olive fruit has different color changes and hardness changes at different stages of maturity. Specifically, when it is immature, its color is generally bright green or dark green, then turns yellow-green, and becomes purple-black or dark black when fully mature. Correspondingly, in terms of hardness, as the fruit continues to mature, its hardness will continue to decrease. When the hardness reaches 3kg / cm 2 The present application determines the color and hardness values ​​of the standard by using mature standard fruits, and then establishes the first standard model and the second standard model based on these values. This can standardize the determination of fruit maturity, reduce the dependence of fruit maturity determination on manual labor, and also effectively reduce the interference of human subjective factors in the determination process, thereby more objectively and scientifically evaluating the maturity of the fruit, which is conducive to improving the accuracy of the evaluation.

[0019] Compared with the method of judging maturity simply by color, this application combines hardness judgment on the basis of color judgment. The change of fruit hardness is directly related to the density change inside the fruit. That is, this application reflects the surface changes of the fruit through color changes, and reflects the internal changes of the fruit through fruit hardness. The maturity of the fruit is evaluated from both internal and external aspects. Therefore, it can effectively avoid misjudgment caused by premature color change or abnormal changes in the fruit color, and improve the accuracy of fruit maturity detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of a method for detecting the maturity of olive fruit provided in Example 1 of the present application; Figure 2 This is the calculation principle diagram of the color difference value; Figure 3 This is the calculation principle diagram of the actual hue value; Figure 4 A schematic diagram of the structure of the detection system provided in Example 2 of the present application; The purpose, features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0023] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0024] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A or scheme B or schemes in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0025] Example 1:

[0026] Reference Figures 1 to 3 This embodiment, as an optional embodiment of the present application, discloses a method for detecting the maturity of olive fruit, comprising the following steps: S1. Constructing a first standard model and a second standard model for judging the maturity of olive fruits; S101. Obtain a number of mature olive fruits as standard fruits; A number of olive fruits are manually selected from the olive fruits as standard fruits. It should be noted that all the standard fruits are of the same variety of olive fruits, and the first standard model and the second standard model are established for different types of olive fruits respectively. S102, respectively obtaining standard images of each standard fruit; The screened standard fruits are placed on the testing table respectively, and the standard images of the standard fruits are obtained by the industrial camera respectively; Since the olive fruit is cylindrical in shape, two to three industrial cameras can be used to capture the fruit. A stitching line is drawn on the fruit and the images captured by the multiple industrial cameras are stitched together using the stitching line as a joint to obtain a standard image of the entire fruit surface. Secondly, the shooting area can be continuously switched by rotating the olive fruit at a specified angle, and then a standard image of the entire fruit surface can be obtained by image stitching. The rotation angle is determined by the technical parameters of the industrial camera. S103, generating a standard hue value and a standard saturation value according to each of the standard images; Retrieving each standard image, manually calibrating the area that best reflects the maturity of the fruit on the standard image based on experience, and after the calibration is completed, automatically extracting the hue value and saturation value of each calibrated area by computer; The selected hue values ​​and saturation values ​​are then combined to determine the standard hue value and saturation value by calculating the average value or manually calibrating.

[0027] S104, generating a color difference value calculation formula according to the standard hue value and the standard saturation value; The color difference value calculation formula is expressed as: , where H0 represents the standard hue value, S0 represents the standard saturation value, H represents the actual hue value, and S represents the actual saturation value; When the fruit is ripe, the olive fruit is purple-black or dark black. Figure 2 , which is represented by oblique cross-hatching in the figure, where the points marked by H0 and S0 represent the points closest to the origin in the qualified area; The point marked with the actual hue value H and the actual saturation S represents the actual position of the olive fruit, and the distance between it and the point (H0, S0) is the color difference value; The larger the color difference value, the farther the actual point is from the ripening zone, that is, the worse the maturity. Conversely, it means the maturity is higher. When the color difference value is 0, it means the fruit is ripe. S105: generating a color score calculation formula according to the color difference value, and outputting the color score calculation formula as a first standard model; The expression of the first standard model is: ; Among them E max Indicates the theoretical maximum value of color difference, E max According to the actual situation, the difference between the color value of the unripe fruit and the color value of the fully ripened fruit is pre-set as E. max , E max In essence, it represents the range of color difference values; As the maturity increases, the color difference value is closer to 0, and the color score approaches 100, otherwise it approaches 0. The above establishes a positive correlation between the color score and maturity, which objectively reflects the maturity of the fruit.

[0028] S106, respectively detecting the standard hardness value, standard density value and standard sound velocity value of each standard fruit; First, measure the weight of the standard fruit, then test its volume using the displacement method, and calculate the standard density value according to the density calculation formula; After the standard density value is calculated, the standard fruit is placed on the testing table and the hardness of the standard fruit is measured using the destructive puncture method. While the puncture method is being measured, an ultrasonic transmitter is used to irradiate the standard fruit and the speed of the ultrasonic shear wave is measured simultaneously. Repeat the above steps to obtain several sets of parameter values ​​(Q', ρ', V s')1, (Q', ρ', V s ')2, (Q', ρ', V s ')3,...,(Q',ρ',V s ') e , where e represents the number; S107, generating a calculation formula for the actual hardness value by fitting the standard hardness values, standard density values, and standard sound velocity values ​​using the least squares method; The actual hardness value calculation formula is: , where k, m and b are all constants, ρ represents the actual density of the olive fruit to be tested, V s Indicates the actual sound speed value; Substitute several sets of standard hardness values, standard density values ​​and standard sound velocity values, and use the least squares method to fit the various constants in the actual hardness calculation formula. That is, determine the constants k, m and b respectively to obtain the actual hardness value calculation formula; The propagation speed of ultrasound within the flesh is directly related to the density of the fruit, and density is positively correlated with the hardness of the fruit. This relationship can be used to link the propagation speed of ultrasound to the hardness of the fruit. However, fruit is different from materials such as concrete. Factors such as the arrangement of flesh cells within the fruit and the inconsistent cell wall degradation rate accompanying flesh softening will lead to a nonlinear mapping relationship between fruit hardness and its density. This application characterizes this nonlinear mapping through a power law relationship, which can more accurately reflect the changing relationship between fruit hardness, density, and actual sound velocity values, thereby improving the accuracy of fruit maturity judgment. S108. Generate a hardness score calculation formula according to the actual hardness value, and output the hardness score calculation formula as a second standard model.

[0029] The expression of the second standard model is: , where ΔQ max Indicates the maximum change in hardness value, and its calculation expression is ΔQ max =Q1-Q0, Q represents the actual hardness value of the olive fruit to be tested, Q0 represents the ideal hardness value of the mature fruit, and Q1 represents the maximum hardness value of the olive fruit.

[0030] The construction principle of the second standard model is the same as that of the first standard model, that is, the closer the actual hardness value is to the ideal hardness value, the closer its hardness score is to 100, and vice versa. It should be noted that Q0 represents the ideal hardness value of a mature fruit, which can directly use the standard hardness value in step S106 or can be tested or set separately; Q1 represents the maximum hardness value of an olive fruit, that is, an immature or early-fruiting olive fruit is manually selected for actual measurement, and the measurement method is preferably the destructive puncture method; S2. Obtaining a comparison image and actual hardness calculation parameters of the olive fruit to be tested; Take photos of the olive fruits to be tested to obtain comparison images; At the same time, the actual density of the olive fruit is measured by the water displacement method, and the actual sound velocity of the olive fruit to be tested is measured by the ultrasonic transmitter. The actual sound velocity and actual density are the actual hardness calculation parameters; S3. Calculating the color score of the olive fruit to be tested based on the comparison image and the first standard model; S31, obtaining a comparison image, dividing the comparison image into a number of standard squares, and outputting the standard squares completely filled with olive fruit images as unit comparison images; Reference Figure 3 , retrieve the comparison image obtained by shooting and stitching, and cut the comparison image into several standard squares; It should be noted that the size of each standard square is determined according to actual conditions, and its minimum area is one pixel unit; Since the outline of the olive is a curve, after the cutting is completed, there is a deletion area between the background area and the olive fruit image. The standard squares in the deletion area contain both the background image and the olive fruit image. The standard squares in the deletion area are removed, and the remaining standard squares completely filled with the olive fruit image are output as the unit comparison image; Calculating the area within the curve region will take up a lot of computing power, and the above-mentioned area is relatively small. Taking it out will not affect the final judgment result, but can also effectively improve the judgment efficiency; S32, respectively obtaining the unit actual hue value and the unit actual saturation value of each unit comparison image; The actual tone values ​​Δs1, Δs2, ..., Δs of each unit comparison image are read by a computer respectively. i ; At the same time, the actual saturation values ​​of the units Δh1, Δh2, ..., Δh are obtained respectively. i ; S33, generating a weight coefficient for each unit comparison image according to the classification condition, the actual hue value of the unit, and the actual saturation value of the unit; The classification conditions refer to the conditions for classifying each unit comparison unit; During the growth of olive fruits, some areas receive sufficient light, which is the illuminated side; some areas receive insufficient light, which is the backlit side; and the remaining areas are transitional areas. Different lighting conditions will result in significant differences in hue and saturation values ​​across different regions. For example, even in the same mature state, the hue and saturation values ​​in the backlit area will be significantly smaller. However, in the generation of standard hue and saturation values, the parameters of the illuminated area are generally used as the standard. Therefore, by classifying and assigning different weight coefficients for adjustment, we can avoid calculation errors caused by differences in hue and saturation values ​​and improve judgment accuracy. It should be noted that there are three weight parameters mentioned above, namely the weight parameter of the illuminated area, the weight parameter of the backlight area and the weight parameter of the transition area. The above three parameters are set manually according to actual conditions; for example, the weight parameter of the illuminated area is set to 1, the weight parameter of the backlight area is set to 1.2, and the weight parameter of the transition area is set to 1.1.

[0031] The classification conditions are determined manually according to different olive fruit varieties; For example, the classification conditions are set as follows: in the illuminated area, the hue value is 20-60, and the saturation value is not less than 40; in the backlight area, the hue value is 90-180 and the saturation value is not greater than 25; the remaining standard squares are grouped into the transition area; S34 , respectively calculating the actual hue value and the actual saturation value according to the actual hue value of each unit, the actual saturation value of each unit, and the weight coefficient of the comparison image of each unit.

[0032] After the classification is completed, the calculation parameters of each region can be obtained, namely (W1, Δs1, p1), (W2, Δs2, p2), ..., (W i , Δs i 、p i ) and (W1, Δh1, p1), (W2, Δh2, p2),..., (W i , Δh i 、p i ); It should be noted that W1, W2, ..., W i You can only choose from the weight parameters of the illuminated area, the backlight area, and the transition area. If the unit comparison image with number 1 is determined to be the backlight area, its weight coefficient will be changed to the weight coefficient of the backlight area. The calculation formula of the actual hue value is: ; The calculation formula of the actual saturation value is ; Where n represents the number of unit comparison images, i represents the number of each unit comparison image, W i Represents the weight coefficient of each unit comparison image, and its value; Δsi represents the actual hue value of the unit, Δhi represents the actual saturation value of the unit, and pi represents the area of ​​the unit comparison image; Put (W1, Δs1, p1), (W2, Δs2, p2),..., (Wi , Δs i 、p i ) are substituted into The actual hue value can be calculated. Similarly, (W1, Δh1, p1), (W2, Δh2, p2), ..., (W i , Δh i 、p i ) are substituted into The actual saturation value can be calculated.

[0033] In the above calculation method, not only are the hue and saturation values ​​under different lighting conditions adjusted through different weight coefficients to ensure the accuracy of the prediction results, but the different colors of the entire fruit surface are also comprehensively calculated through the partition and classification calculation method, thereby accurately reflecting the correct hue and saturation values ​​of the olive fruit surface and improving the accuracy of color prediction.

[0034] S35 , calculating a color score according to the actual hue value, the actual saturation value, and the first standard model.

[0035] Get the actual hue value and actual saturation value calculated in step S34, and then calculate the color difference value according to the formula Calculate the color difference value of the olive fruit to be tested; Then substitute the calculated color difference value into the first standard model The color score can be calculated; It should be noted that when calculating the color difference value, when the fruit is over-ripe, the hue value and saturation value will exceed the standard hue value and standard saturation value, because there is a comparison step before calculating the color difference value; That is, the actual hue value H is compared with the standard hue value H0, and the actual saturation value S is compared with the standard saturation value S0. If H0 < H and S0 < S are satisfied, the hue difference value is directly determined to be 0 and the color score is 100 points; S4. Calculating a hardness score of the olive fruit to be tested according to the actual hardness calculation parameter and the second standard model; S41, obtaining actual hardness calculation parameters, wherein the actual hardness calculation parameters include actual density value and actual sound velocity value; Retrieve the actual density value and actual sound velocity value measured in step S2; S42, calculating the actual hardness value according to the actual density value, the actual sound velocity value and the hardness value calculation formula; Retrieve the actual hardness calculation formula obtained by fitting calculation in step S107 , the actual density value and the actual sound velocity value are substituted into the above formula to calculate the actual hardness value of the olive fruit to be tested; S43, calling the second standard calculation model; S44. Calculate a hardness score according to the actual hardness value and the second standard calculation model.

[0036] The expression of the second standard calculation model is , the actual hardness value calculated in step S42 is substituted to obtain the hardness score; S5, judging the maturity of the olive to be tested according to the color score and the hardness score The maturity calculation formula is Q = αA + βB, where α and β represent weight values, and α + β = 1. α and β can be flexibly adjusted according to actual conditions. Substituting the calculation results of steps S35 and S44 into the above formula can obtain the final maturity parameter, and comparing it with the pre-set parameters can determine the maturity of the olive fruit to be tested; For example, 120≤Q<140 is the first level of maturity, 140≤Q<160 is the second level of maturity, 160≤Q<180 is the third level of maturity, and 180≤Q is the fourth level of maturity; By comparing the above parameters, the judgment results can be quickly output.

[0037] Example 2:

[0038] Reference Figure 4 This embodiment, as another feasible implementation of the present application, discloses a detection system, including a standard model generation module, a parameter acquisition module, and a first calculation module. The standard model generation module and the parameter acquisition module are independent of each other and are respectively communicatively connected to the first calculation module. The detection system also includes a second calculation module, which is independent of the first calculation module. The standard model generation module and the parameter acquisition module are respectively communicatively connected to the second calculation module. The detection system also includes a determination module, which is communicatively connected to the first calculation module and the second calculation module respectively to determine the maturity of the olives to be detected based on the color score and the hardness score.

[0039] Olive fruit has different color changes and hardness changes at different stages of maturity. Specifically, when it is immature, its color is generally bright green or dark green, then turns yellow-green, and becomes purple-black or dark black when fully mature. Correspondingly, in terms of hardness, as the fruit continues to mature, its hardness will continue to decrease. When the hardness reaches 3kg / cm 2The present application determines the standard color and hardness values ​​of mature standard fruits, and then establishes the first standard model and the second standard model based on these values. This can standardize the determination of fruit maturity, reduce the dependence of fruit maturity determination on manual labor, and effectively reduce the interference of human subjective factors in the determination process, thereby more objectively evaluating the maturity of the fruit, which is conducive to improving the accuracy of the evaluation.

[0040] Secondly, compared with the method of judging maturity simply by color, this application combines hardness judgment on the basis of color judgment. The change of fruit hardness is directly related to the density change inside the fruit. That is, this application reflects the surface changes of the fruit through color changes, and reflects the internal changes of the fruit through fruit hardness. The fruit is evaluated from both internal and external aspects at the same time. Therefore, it can effectively avoid misjudgment caused by premature color change or abnormal changes in fruit color, and improve the accuracy of fruit maturity detection.

[0041] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for detecting the maturity of olive fruit, characterized in that: The following steps are involved: Constructing a first standard model and a second standard model for judging the maturity of olive fruits; Obtaining a comparison image and actual hardness calculation parameters of the olive fruit to be tested; Calculating the color score of the olive fruit to be tested based on the comparison image and the first standard model; Calculating the hardness score of the olive fruit to be tested according to the actual hardness calculation parameter and the second standard model; The maturity of the olives to be tested is determined according to the color score and the hardness score.

2. The olive fruit maturity detection method according to claim 1, wherein The method of constructing a first standard model for judging the maturity of olive fruits comprises the following steps: Obtain a number of ripe olive fruits as standard fruits; Obtain standard images of each standard fruit respectively; generating a standard hue value and a standard saturation value according to each of the standard images; Generate a color difference value calculation formula based on the standard hue value and the standard saturation value; A color score calculation formula is generated according to the color difference value, and the color score calculation formula is output as a first standard model.

3. The olive fruit maturity detection method according to claim 2, wherein The color difference value calculation formula is expressed as: ; The expression of the first standard model is: ; Where H0 represents the standard hue value, S0 represents the standard saturation value, E max Indicates the theoretical maximum value of color difference, H indicates the actual hue value, and S indicates the actual saturation value.

4. The olive fruit maturity detection method according to claim 1, wherein The second standard model for judging the maturity of olive fruits is constructed, comprising the following steps: Obtain a number of ripe olive fruits as standard fruits; The standard hardness value, standard density value and standard sound velocity value of each standard fruit are tested respectively; According to each standard hardness value, standard density value and standard sound velocity value, the calculation formula of the actual hardness value is generated by least square fitting; A hardness score calculation formula is generated according to the actual hardness value, and the hardness score calculation formula is output as a second standard model.

5. The olive fruit maturity detection method according to claim 4, wherein The actual hardness value calculation formula is expressed as: , where k, m and b are all constants, ρ represents the actual density of the olive fruit to be tested, V s Represents the actual sound speed value; the expression of the second standard model is: , where ΔQ max Indicates the maximum change in hardness value, and its calculation expression is ΔQ max =Q1-Q0, Q represents the actual hardness value of the olive fruit to be tested, Q0 represents the ideal hardness value of the mature fruit, and Q1 represents the maximum hardness value of the olive fruit.

6. The olive fruit maturity detection method according to claim 1, wherein Calculating the color score of the olive fruit to be tested based on the comparison image and the first standard model comprises the following steps: Acquire a comparison image, divide the comparison image into a plurality of standard squares, and output the standard squares completely filled with the olive fruit image as unit comparison images; Obtaining the actual hue value and saturation value of each unit comparison image respectively; generating a weight coefficient for each of the unit comparison images according to the classification condition, the unit actual hue value, and the unit actual saturation value; Calculating the actual hue value and the actual saturation value according to the actual hue value of each unit, the actual saturation value of each unit and the weight coefficient of the comparison image of each unit; A colorimetry score is calculated according to the actual hue value, the actual saturation value, and a first standard model.

7. The olive fruit maturity detection method according to claim 6, wherein The calculation formula of the actual hue value is: ; The calculation formula of the actual saturation value is ; Where n represents the number of unit comparison images, i represents the number of each unit comparison image, W i Indicates the weight coefficient of each unit comparison image, its value; Δs i Indicates the actual hue value of the unit, Δh i Indicates the actual saturation value of the unit, p i Represents the area of ​​the cell alignment image.

8. The olive fruit maturity detection method according to claim 1, wherein The method of calculating the hardness score of the olive fruit to be tested according to the actual hardness calculation parameter and the second standard model comprises the following steps: Acquiring actual hardness calculation parameters, wherein the actual hardness calculation parameters include an actual density value and an actual sound velocity value; Calculate the actual hardness value according to the actual density value, actual sound velocity value and hardness value calculation formula; Retrieving the second standard calculation model; A hardness score is calculated according to the actual hardness value and the second standard calculation model.

9. The olive fruit maturity detection method according to claim 1, wherein The maturity calculation formula is Q=αA+βB, where α and β both represent weight values, and α+β=1.

10. A detection system based on the detection method according to any one of claims 1 to 9, characterized in that: include: A standard model generation module, used to construct a first standard model and a second standard model for judging the maturity of olive fruits; A parameter acquisition module is used to obtain a comparison image and actual hardness calculation parameters of the olive fruit to be tested; A first calculation module is used to calculate the color score of the olive fruit to be tested based on the comparison image and the first standard model; A second calculation module is used to calculate the hardness score of the olive fruit to be tested according to the actual hardness calculation parameter and the second standard model; The determination module is used to determine the maturity of the olives to be tested based on the color score and the hardness score.

Images