Melon crop germplasm resource commodity data acquisition device

Abstract

The invention relates to the field of agricultural science, in particular to a melon crop germplasm resource commodity data acquisition device which comprises a detection table, a controller, a clamping and cutting module and a scanning detection module are installed on the detection table, the clamping and cutting module comprises a shell, an insertion hole is formed in the shell, and a feeler lever and a fiber grating strain sensor are arranged in the insertion hole; the feeler lever is sleeved with a spring, a cutting knife is installed on the shell, and a piezoelectric film array is installed on the cutting knife; the scanning detection module comprises an annular track, a moving part is arranged on the annular track, a mounting frame is mounted on the moving part, a mounting plate is arranged on the mounting frame, a laser range finder and a multispectral camera are mounted on the mounting plate, a telescopic part is mounted on the mounting plate, a probe is mounted on the telescopic part, and a vibrator and a capacitive moisture detection ring are mounted in the probe; and all the electrical elements are in signal connection with the controller. The method is used for comprehensively, accurately and quickly collecting commodity data of melon crops so as to meet the requirements of germplasm resource research and breeding practice.

Description

Technical Field

[0001] The present invention relates to the field of agricultural science, and particularly to a device for collecting commercial data of germplasm resources of melon crops. Background Art

[0002] In the research of germplasm resources and breeding practice of melon crops, especially watermelon and melon, the quantitative characterization of commercial data plays a crucial role, which is directly related to the efficiency of variety selection and the potential of commercial promotion. The commercial index system is a complex and multi-dimensional system, which not only covers morphological parameters such as single fruit weight, longitudinal diameter, transverse diameter and fruit shape index, but also involves physical properties including fruit shell thickness, flesh firmness and water gradient distribution, as well as biochemical qualities such as soluble solid content and cellulose content, etc.

[0003] However, the traditional data collection process faces many challenges. First of all, methods such as manual caliper measurement, cross-section cutting observation and fixed-point detection with hand-held instruments have poor repeatability of measurement calibration. Research data shows that the coefficient of variation of manual repeated measurement of fruit diameter is as high as 5.8%, and the standard deviation of error in determining fruit shell thickness also reaches 0.3 mm, which obviously cannot meet the requirements of high-precision data collection.

[0004] Secondly, the low efficiency of destructive testing is also a major drawback of traditional methods. In order to obtain comprehensive commercial data, a single sample often needs to undergo a series of cumbersome operations such as weighing, cutting and multi-point sampling, which is not only time-consuming and laborious, but also difficult to meet the requirements of high-throughput detection in the germplasm resource library.

[0005] In addition, the problem of spatio-temporal mismatch of multi-source data cannot be ignored. The collection links of morphological parameters and internal quality data are often separated, which not only increases the difficulty of data integration, but also reduces the reliability of phenotype-genotype association analysis, thus affecting the accuracy and efficiency of breeding decisions.

[0006] With the rapid development of technology, especially the ever-changing sensor technology and automation technology, new opportunities have been brought for the collection of commercial data of germplasm resources of melon crops. However, although there have been many advanced detection devices and technical means on the market, there is still a lack of a comprehensive device that can collect commercial data of germplasm resources of melon crops (especially watermelon and melon) comprehensively, accurately and quickly. Therefore, the research and development of an efficient and intelligent data collection device has far-reaching significance and urgent needs for promoting the research and utilization of germplasm resources of melon crops and improving the breeding level and market competitiveness of melon crops in China. Summary of the Invention

[0007] To solve the above problems, the present invention provides a device for collecting commercial data of melon crop germplasm resources, which is used to comprehensively, accurately and quickly collect commercial data of melon crops, including multi-dimensional characteristics such as morphological parameters, physical properties and biochemical qualities, so as to meet the needs of germplasm resource research and breeding practice.

[0008] To achieve the above object, the technical solution of the present invention is as follows: A device for collecting commercial data of melon crop germplasm resources, including a detection table, on which a controller, a clamping and cutting module for fixing and cutting melon crop samples, and a scanning and detection module for detecting commercial data of melon crop sample germplasm resources are installed. The clamping and cutting module includes several outer shells, in which fiber Bragg grating strain sensors are installed, and cutting components are installed on the outer shells. The cutting components include cutting knives, and piezoelectric film arrays are installed on the cutting edges of the cutting knives;

[0009] The scanning and detection module includes an annular track, which is fixedly connected to the outer edge of the outer top wall of the detection table. A moving member is slidably fitted on the annular track. An installation frame is fixedly connected to the moving member. One end of the installation frame away from the moving member is fixedly connected to an installation plate. A laser rangefinder and a multispectral camera are installed on the installation plate. A telescopic member is installed on the installation plate. A probe is installed at one end of the telescopic member away from the installation plate. The probe is of a hollow structure, and a vibrator and a capacitive moisture detection ring are installed inside the probe;

[0010] The fiber Bragg grating strain sensor, the piezoelectric film array, the moving member, the laser rangefinder, the multispectral camera, the telescopic member, the vibrator and the capacitive moisture detection ring are all signal-connected to the controller; the controller calculates the diameter of the melon crop sample according to the spring deformation data monitored by the two fiber Bragg grating strain sensors in real time; at the same time, the controller generates a three-dimensional fruit model according to the real-time monitoring data of the fiber Bragg grating strain sensor and the laser rangefinder;

[0011] When the piezoelectric film array monitors that the cutting component cuts the melon crop sample, the controller triggers the moving member, and the moving member moves on the annular track and synchronously activates the multispectral camera to continuously capture the cross-sectional images of the cut;

[0012] When the laser rangefinder measures that the sudden change in the peel thickness exceeds the set threshold, the controller controls the telescopic member to push the probe forward until the capacitive moisture detection ring contacts the pulp surface and then stops, so as to establish a dynamic compensation relationship between the piercing depth and the peel thickness;

[0013] The vibrator starts to perform sweep-frequency vibration after the probe pierces to the set depth, and the vibration decay time t and the determination of the meat texture thickness satisfy:

[0014] When t < 1ms, it is determined as fine meat texture, when 1ms ≤ t < 3ms, it is medium meat texture, and when t ≥ 3ms, it is thick meat texture.

[0015] Further, it also includes a headspace detection module. The headspace detection module includes a liftable and sealable cover. The inner sidewall of the liftable and sealable cover is provided with a spiral flow guide groove. The end of the spiral flow guide groove is connected to a hose, and the hose is connected to a probe to form a closed air flow circuit. A gas pump is provided at the top of the liftable and sealable cover, and an annular electromagnet is provided at the bottom edge of the liftable and sealable cover. An iron ring is embedded in the inner top wall of the detection table.

[0016] When the sealable cover descends to the surface of the detection table, the electromagnet adsorbs with the iron ring embedded in the detection table to form a sealed chamber.

[0017] After the probe completes puncture, the controller starts the gas pump to make the volatile substances form a closed air flow circuit along the spiral flow guide groove - probe channel.

[0018] Further, the outer shell is symmetrically installed on the top of the detection table along the central axis of the detection table. A plurality of jacks are provided in the outer shell. A touch rod is slidably fitted in each jack. A spring is sleeved on each touch rod. One end of the spring is fixedly connected to the outer sidewall of the touch rod, and the other end of the spring is fixedly connected to the bottom of the jack. The fiber Bragg grating strain sensor is installed on the sidewall of the jack.

[0019] The cutting assembly includes a handle. An installation cavity is provided in the sidewall of the outer shell. The handle is hinged with a cross bar. The cross bar is located in the installation cavity. One end of the cross bar away from the handle is hinged with a special-shaped rod. Crank structures are hinged on both the special-shaped rod and the handle. The crank structure includes a connecting rod. One end of the connecting rod away from the special-shaped rod and the handle is hinged with a push block. The side of the push block away from the connecting rod is fixedly connected to a cutting knife.

[0020] Further, the cutting knife is a triangular cutting knife. The surface of the triangular cutting knife is provided with alternately hydrophobic and hydrophilic stripes. The stripe spacing decreases along the axial direction of the knife body. The stripe spacing decreases in an arithmetic progression from the knife handle to the knife tip, and the common difference Δd = 0.15mm.

[0021] The stripes divide the knife body into a hydrophobic area and a hydrophilic area. The contact angle of the hydrophobic area is ≥150°, and the contact angle of the hydrophilic area is ≤20°. A super-hydrophilic coating is provided within a range of 5mm from the knife tip, and the contact angle is ≤5°.

[0022] Further, the puncturing end of the probe is provided with a hierarchical puncturing structure. The hierarchical puncturing structure is composed of concentric needle tubes with diameters of 0.1mm, 0.5mm, and 1mm. Each needle tube is independently connected to a pressure sensor, and the pressure sensor is signal-connected to the controller.

[0023] When the probe contacts the pulp, the controller determines the crispness of the pulp according to the ratio of the pressure change rates of each needle tube:

[0024] If (P1 / P0) > 2.5 and (P2 / P1) < 1.2, it is determined as crisp pulp;

[0025] If (P1 / P0) < 1.8 and (P2 / P1) > 1.5, it is determined as soft pulp;

[0026] Among them, P0, P1, and P2 are the initial puncture pressures of 0.1 mm, 0.5 mm, and 1 mm syringe needles respectively.

[0027] Furthermore, when the controller performs fruit shell texture analysis:

[0028] The image data collected by the multispectral camera is fused with the height data of the laser rangefinder to generate a texture depth distribution map;

[0029] Define the texture significance index where Δh is the height difference between adjacent points, Δc is the color difference, and A is the area of the analysis region;

[0030] When S > 8, it is determined as a deep texture variety, 3 ≤ S ≤ 8 is medium texture, and S < 3 is a shallow texture variety.

[0031] Furthermore, when the vibrator of the probe performs sweep frequency vibration:

[0032] The initial frequency f0 = 100 Hz, and it increases to 2000 Hz at a rate of Δf = 50 Hz / ms;

[0033] The vibration attenuation curve is fitted as A(t) = A0·e^(-βt)·sin(ωt + φ), where A0 is the initial amplitude, β is the damping coefficient, ω is the angular frequency, φ is the phase angle, t is time, and the β value is linearly positively correlated with the density of meat fibers.

[0034] Furthermore, it also includes an early warning module. The early warning module includes an indicator light and a buzzer, and both the indicator light and the buzzer are signal-connected to the controller;

[0035] When the controller detects that one or more commercial data of the melon crop sample exceed the preset range, the controller triggers the early warning module, the indicator light flashes and emits light of a specific color, and the buzzer emits an alarm sound.

[0036] Furthermore, it also includes a data storage module. The data storage module is used to store the commercial data of the melon crop sample collected by the fiber Bragg grating strain sensor, piezoelectric film array, laser rangefinder, multispectral camera, telescopic member, vibrator, and capacitive moisture detection ring, as well as the analysis results of the controller.

[0037] Furthermore, it also includes a user interface. The user interface is signal-connected to the controller. The user interface is used to display the commercial data and analysis results of the melon crop sample. At the same time, the user interface is used to provide operation options through a touch screen for the operator to input instructions or adjust parameters.

[0038] Adopting the above solution has the following beneficial effects:

[0039] 1. Through the mechanical linkage design of the clamping-cutting linkage module and the rotary scanning-puncturing composite mechanism, this solution synchronously triggers multiple detections such as shell thickness measurement, texture scanning, flesh puncturing, and juice collection during a single cutting action. Compared with the prior art where step-by-step operations lead to data asynchrony in time and space, the data acquisition time is shortened from 10 - 15 minutes in the traditional method to within 90 seconds, and the time difference between the acquisitions of various parameters is ≤50 ms, ensuring the consistency of data in time and space and solving the problem of parameter correlation distortion caused by traditional modular detection.

[0040] 2. This solution combines non-contact laser contour scanning and minimally invasive hierarchical puncturing technology. Compared with the prior art where contact detection damages the integrity of the sample. The laser rangefinder (accuracy 0.05 mm) obtains the three-dimensional model of the fruit shell through rotary scanning, avoiding the surface indentation caused by traditional caliper measurement; the 0.1 / 0.5 / 1 mm three-level puncture needle tube reduces the flesh damage area by 82% (compared with the traditional 3 mm probe); the vibration attenuation analysis method can evaluate the flesh characteristics with only a 0.5 mm puncture depth, and the sample utilization rate is increased to 97%.

[0041] 3. This solution realizes objective quantification through multi-physical field coupling analysis. Compared with the prior art where the evaluation of flesh quality depends on subjective experience. The cutting resistance spectrum analysis (1 - 10 kHz) extracts the crispness characteristic frequency and establishes a crispness index model (R 2 = 0.92); the vibration attenuation damping coefficient β is linearly correlated with the fiber density; the hierarchical puncture pressure ratios (P1 / P0, P2 / P1) construct a fuzzy logic classifier for crispy / soft flesh, with an accuracy rate of 93.7%.

[0042] 4. Through the closed-loop design of the headspace detection module and the puncture probe, this solution improves the enrichment efficiency of volatile substances by 3 times with a spiral diversion groove (lift angle 30° - 45°) compared with the prior art where the flavor detection device is separated. Compared with the prior art where sugar content detection requires juice extraction, this solution innovatively designs the hydrophilic-hydrophobic stripes (contact angle gradient 5° - 150°) on the blade body of the triangular cutting knife to achieve directional diversion of juice, and the collection efficiency reaches 95%.

[0043] 5. This solution innovatively introduces a multi-level linkage warning mechanism. Compared with the prior art where there is a lack of abnormal warning, the combination coding of the three-color indicator light + buzzer can distinguish various types of abnormalities.

[0044] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is an axonometric view of an embodiment of the device for collecting commercial data of germplasm resources of melon crops according to the present invention;

[0046] Figure 2 This is a side view of an embodiment of the device for collecting commercial data of melon crop germplasm resources according to the present invention;

[0047] Figure 3 is Figure 2 a sectional view taken along the A-A direction in

[0048] Figure 4 This is an installation schematic diagram of the cutting assembly in an embodiment of the device for collecting commercial data of melon crop germplasm resources according to the present invention;

[0049] Figure 5 This is an axonometric view of the liftable seal cover in an embodiment of the device for collecting commercial data of melon crop germplasm resources according to the present invention;

[0050] Figure 6 This is a framework diagram of an embodiment of the device for collecting commercial data of melon crop germplasm resources according to the present invention.

[0051] The reference numerals in the accompanying drawings of the specification include: 1, detection table; 2, housing; 201, installation cavity; 3, jack; 4, contact rod; 5, spring; 6, cutting knife; 7, annular track; 8, moving member; 9, mounting bracket; 10, mounting plate; 11, laser rangefinder; 12, multispectral camera; 13, probe; 14, liftable seal cover; 15, handle; 16, cross bar; 17, special-shaped rod; 18, connecting rod; 19, pushing block. Detailed Embodiments

[0052] Next, the technical solutions of the present invention will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0053] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0054] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] The following is a further detailed description through specific embodiments:

[0056] Embodiment 1:

[0057] As shown in Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 6 : A commercial data acquisition device for melon crop germplasm resources, including a detection table 1, on which a controller, a clamping and cutting module for fixing and cutting melon crop samples (in this embodiment, watermelon / melon samples are selected as melon crop samples), and a scanning and detection module for detecting the commercial data of melon crop sample germplasm resources are installed. The clamping and cutting module includes two outer shells 2, which are symmetrically installed on the top of the detection table 1 along the center axis of the detection table 1. A number of jacks 3 are provided inside the outer shell 2, and touch rods 4 are slidably fitted in the jacks 3. Springs 5 are sleeved on the touch rods 4. One end of the spring 5 is fixedly connected to the outer side wall of the touch rod 4, and the other end of the spring 5 is fixedly connected to the bottom of the jack 3. A fiber Bragg grating strain sensor (connected to an 8-channel FBG using a wavelength demodulation module (WDM), with a central wavelength of 1525 - 1565 nm, a strain sensitivity of 1.2 pm / με, and transmitting the wavelength offset to the controller through an RS485 bus, with a sampling frequency of 500 Hz) is installed on the side wall of the jack 3. A cutting assembly is installed on the outer shell 2. The cutting assembly includes a cutting knife 6, and a piezoelectric film array is installed on the cutting edge of the cutting knife 6 (configured with a charge amplifier (gain 100 mV / pC) connected to the PVDF piezoelectric film, setting a band-pass filter (1 - 10 kHz) to extract the cutting dynamic signal, and an AD sampling rate of 1 MHz). The cutting assembly includes a handle 15. An installation cavity 201 is provided in the side wall of the outer shell 2. The handle 15 is hinged with a cross bar 16, and the cross bar 16 is located in the installation cavity 201. One end of the cross bar 16 away from the handle 15 is hinged with a special-shaped rod 17. Crank structures are hinged on both the special-shaped rod 17 and the handle 15. The crank structure includes a connecting rod 18. Push blocks 19 are hinged at one end of the connecting rod 18 away from the special-shaped rod 17 and the handle 15. One side of the push block 19 away from the connecting rod 18 is fixedly connected to the cutting knife 6.

[0058] The scanning detection module includes a circular track 7, which is fixedly connected to the outer edge of the outer top wall of the detection table 1. A moving member 8 is slidably fitted on the circular track 7 (in this embodiment, the moving member 8 adopts a maglev guide rail module, which consists of a linear motor stator winding (three-phase ironless design) embedded in the circular track 7 and a permanent magnet array at the bottom of the moving member 8 to form a driving unit. The Hall sensor array (with a spacing of 5 mm) real-time feeds back the position information to achieve nanometer-level positioning (repeated positioning accuracy ±2 μm)). An installation frame 9 is fixedly connected to the moving member 8 (in this embodiment, the installation frame 9 is an installation frame with adjustable height). One end of the installation frame 9 far from the moving member 8 is fixedly connected to an installation plate 10. A laser rangefinder 11 (in this embodiment, the laser rangefinder 11 adopts a triangulation laser sensor (KEYENCE LJ-V7000), which emits a 650 nm laser beam to the incision surface, and a CMOS linear array (2048 pixels) receives the displacement of the reflected light spot, and calculates the distance in combination with the calibration curve) and a multispectral camera 12 (configuration of the multispectral camera 12: adopts JAI CM-140GE (400 - 1000 nm, 12-channel spectrum)) are installed on the installation plate 10. An expansion member (in this embodiment, the expansion member is selected as an electric telescopic rod) is installed on the installation plate 10. One end of the expansion member far from the installation plate 10 is installed with a probe 13. The probe 13 is of a hollow structure, and a vibrator (driving a piezoelectric ceramic by a class D power amplifier, with an adjustable output voltage of 0 - 200 Vpp, and the sweep signal is generated by a DDS chip AD9834, with a frequency resolution of 0.1 Hz) and a capacitive moisture detection ring (in this embodiment, the capacitive moisture detection ring adopts a coaxial structure design of annular electrodes, with an inner diameter of 1.5 mm (excitation electrode) and an outer diameter of 3 mm (inductive electrode). Apply a 1 MHz sine signal, measure the real part ΔR and the imaginary part ΔX of the complex impedance, and calculate the moisture content through the non-linear mapping relationship between the R / X ratio and the moisture content)) are installed inside the probe 13. The puncture end of the probe 13 is provided with a hierarchical puncture structure, which consists of concentric needle tubes with diameters of 0.1 mm, 0.5 mm, and 1 mm. Each needle tube is independently connected to a pressure sensor (in this embodiment, the pressure sensor is selected as a MEMS piezoresistive sensor (measurement range 0 - 50 N, overload capacity 150%)). The signal output by the Wheatstone bridge is amplified by an instrumentation amplifier (AD8421, gain 1000) and then by a 24-bit Σ-Δ ADC (ADS1256). The pressure sensor is signal-connected to the controller; when the probe 13 contacts the pulp, the controller determines the crispness of the pulp according to the ratio of the pressure change rates of each needle tube: if (P1 / P0) > 2.5 and (P2 / P1) < 1.2, it is determined as crisp flesh; if (P1 / P0) < 1.8 and (P2 / P1) > 1.5, it is determined as soft flesh; where P0, P1, and P2 are the initial puncture pressures of the 0.1 mm, 0.5 mm, and 1 mm needle tubes respectively.

[0059] The fiber grating strain sensor, the piezoelectric film array, the moving member 8, the laser rangefinder 11, the multispectral camera 12, the telescopic member, the vibrator, and the capacitive moisture detection ring are all signal-connected to the controller; the controller calculates the diameter of the melon crop sample according to the deformation data of the spring 5 monitored by the fiber grating strain sensors on both sides in real time; at the same time, the controller converts and generates a three-dimensional fruit model according to the real-time monitoring data of the fiber grating strain sensor and the laser rangefinder 11; when the piezoelectric film array monitors that the cutting assembly cuts the melon crop sample, the controller triggers the moving member 8, and the moving member 8 moves on the circular track 7 and synchronously activates the multispectral camera 12 to continuously capture the cross-sectional images of the cut; when the laser rangefinder 11 measures that the sudden change in the pericarp thickness exceeds the set threshold, the controller controls the telescopic member to push the probe 13 until the capacitive moisture detection ring contacts the pulp surface and then stops, so as to establish a dynamic compensation relationship between the piercing depth and the pericarp thickness.

[0060] When the vibrator of the probe 13 performs swept-frequency vibration: the initial frequency f0 = 100 Hz, and it increases at a rate of Δf = 50 Hz / ms to 2000 Hz; the vibration attenuation curve is fitted as A(t) = A0·e^(-βt)·sin(ωt + φ), where A0 is the initial amplitude, β is the damping coefficient, ω is the angular frequency, φ is the phase angle, t is the time, and the β value is linearly positively correlated with the density of the meat fibers. The vibrator starts the swept-frequency vibration after the probe 13 pierces to the set depth, and the vibration attenuation time t and the determination of the thickness of the meat satisfy: when t < 1 ms, it is determined as fine meat, when 1 ms ≤ t < 3 ms, it is medium meat, and when t ≥ 3 ms, it is coarse meat.

[0061] When the controller performs fruit shell pattern analysis: it fuses the image data collected by the multispectral camera 12 with the height data of the laser rangefinder 11 to generate a pattern depth distribution map; defines the pattern significance index where Δh is the height difference between adjacent points, Δc is the color difference, and A is the analysis area; when S > 8, it is determined as a deep pattern variety, when 3 ≤ S ≤ 8, it is a medium pattern, and when S < 3, it is a shallow pattern variety.

[0062] It also includes a user interface, which is signal-connected to the controller. The user interface is used to display the commercial data and analysis results of the melon crop sample, and at the same time, the user interface is used to provide operation options on the touch screen for the operator to input commands or adjust parameters.

[0063] The specific implementation process is as follows: Put the watermelon / melon sample down from the top of the outer shell 2, and the watermelon / melon sample gradually falls into the inner part of the outer shell 2. The touch rod 4 installed on the inner side wall of the outer shell 2 gradually contacts the surface of the watermelon / melon sample fruit body, and the touch rod 4 squeezes the spring 5. The compression amount of the spring 5 is monitored in real time by the fiber grating strain sensor, and the controller automatically calculates the diameter of the watermelon / melon sample:

[0064]

[0065] wherein, ΔL i is the displacement of each touch rod 4.

[0066] The controller determines the current state of the fruit in real time according to the calculated diameter: Fruit shape index = longitudinal diameter / transverse diameter, where the watermelon index: oblate, fruit shape index < 1; round, fruit shape index = 1; high round, fruit shape index 1 - 1.1; short oval, fruit shape index 1.1 - 1.2; oval, fruit shape index 1.2 - 1.4; long oval, fruit shape index above 1.4. Melon index: round, oblate fruit shape index < 1, spherical fruit shape index = 1, high round fruit shape index 1 - 1.1; oval, short oval fruit shape index 1.1 - 1.3, long oval fruit shape index > 1.3; oval, egg-shaped fruit shape index 1.1 - 1.3, long egg-shaped > 1.3; spindle-shaped, both ends pointed fruit shape index 1.5 - 2.0; pear-shaped, large at the top and small at the fruit stalk end fruit shape index 1.2 - 1.5; cylindrical, cylindrical fruit shape index < 1.5, long cylindrical fruit shape index > 1.5; rod-shaped, slender fruit shape index > 2.

[0067] After the watermelon / melon sample to be detected is placed in position, the monitoring personnel press the handle 15. The other end of the handle 15 drives the cross bar 16, and the cross bar 16 drives the special-shaped rod 17. The handle 15 and the special-shaped rod 17 respectively drive the crank structures on both sides. The connecting rod 18 in the crank structure drives the push block 19 to achieve bidirectional feeding, so that the push block 19 pushes the cutting knife 6, and the cutting knife 6 cuts the watermelon / melon sample. During cutting, the piezoelectric film array collects the cutting force spectrum (1 - 10 kHz) in real time. When the cutting knife 6 first drops by 30%, the scanning detection module is started through the controller. The controller starts the moving part 8, and the moving part 8 tracks the cut with the angular velocity ω = 0.2Drad / s along the circular track 7. According to the calculated diameter of the watermelon / melon sample, the height of the mounting plate 10 is adjusted by using the mounting frame 9. The laser rangefinder 11 scans the edge of the cut (line scanning speed 50 mm / s, point distance 0.1 mm). During the movement of the moving part 8 along the circular track 7, the multi-spectral camera 12 takes a cross-sectional image (exposure time 10 ms, 12-channel synchronous acquisition), and transmits the shooting result to the controller. The controller generates a high-resolution cross-sectional pseudo-color image by fusing the multi-spectral image (400 - 1000 nm), extracts the boundary between the pulp and the peel through an edge detection algorithm (such as the Canny operator), generates a sectional view and marks the key areas (such as the peel, pulp, and seed cavity).

[0068] Edge sugar detection: A circular area is delimited at a distance of 5 - 10 mm from the peel, and the average spectral reflectance of this area is extracted (focusing on analyzing the 530 nm and 850 nm bands); Core sugar detection: A circular area is delimited around the seed cavity, and the spectral reflectance of the central area is extracted.

[0069] Calculate the sugar content by near infrared spectroscopy model (PLS regression algorithm):

[0070] Brix = a·R 530 +b·R 850 +c

[0071] Among them, R 530 and R 850 are the reflectances of 530nm and 850nm bands respectively, and a, b, c are model coefficients (determined by laboratory calibration).

[0072] At the same time, the flesh color (creamy white, white, pink, dark pink, red, dark red, orange-red, light yellow, yellow or orange-yellow) of the watermelon / melon samples was judged by the pseudo-color image of the cross section.

[0073] When the laser rangefinder 11 detects a sudden change in the peel thickness (Δh>2mm), the telescopic member pushes the probe 13, and the puncture speed v=2mm / s. The puncture depth is dynamically adjusted, Δd=0.05D+0.3mm.

[0074] After the graded puncture structure contacts the pulp, the pressure sensor records the P0, P1 and P2 pressure curves, and the vibrator performs sweep frequency vibration (100→2000Hz, lasting 40ms). At the same time, the capacitive moisture detection ring measures the dielectric constant ε (accuracy ±0.5%), and the moisture content W (%) is calculated by the formula W=0.85ε-12.6.

[0075] Crispness determination: Fuzzy logic classification is performed based on the pressure ratios (P1 / P0) and (P2 / P1); if (P1 / P0)>2.5 and (P2 / P1)<1.2, the meat is determined to be crispy; if (P1 / P0)<1.8 and (P2 / P1)>1.5, the meat is determined to be soft.

[0076] Meat quality classification: Fit the vibration attenuation curve and calculate the β value. β>120 is fine meat quality, 80≤β≤120 is medium meat quality, and β<80 is coarse meat quality.

[0077] 3D modeling: The laser ranging data is integrated with the displacement of the feeler rod 4 to generate a 3D mesh model of the fruit (STL format).

[0078] Calculation of texture significance index S value: When S>8, it is judged as a deep-grain variety, 3≤S≤8 is a medium-grain variety, and S<3 is a shallow-grain variety.

[0079] The user interface displays: pseudo-color cross-sectional images (color scale indicates sugar content distribution); radar map of flesh texture (three-dimensional indicators of crispness, fiber density, and water content); and variety classification recommendations (based on the SVM algorithm).

[0080] Embodiment 2:

[0081] As shown in the Figure 5 accompanying drawings, the difference from Example 1 is that it further includes a headspace detection module. The headspace detection module includes a liftable and sealable cover 14, and the inner side wall of the liftable and sealable cover 14 is provided with a spiral diversion groove. Design parameters of the spiral diversion groove: groove depth h = 0.2H + 0.5 mm, where H is the probe insertion depth, and the surface roughness Ra of the polytetrafluoroethylene coating ≤ 0.8 μm. The end of the spiral diversion groove is connected to a hose, and the hose is connected to the probe 13 to form a closed air flow circuit; a micro air pump is provided at the top of the liftable and sealable cover 14, and an annular electromagnet is provided at the bottom edge of the liftable and sealable cover 14, and an iron ring is embedded in the inner top wall of the detection table 1.

[0082] When the sealable cover descends to the surface of the detection table 1, the electromagnet adsorbs with the iron ring embedded in the detection table 1 to form a sealed chamber; when the probe 13 completes the puncture, the controller starts the micro air pump, so that the volatile substances form a closed air flow circuit along the spiral diversion groove - probe 13 channel.

[0083] The specific implementation process is as follows: During the detection process of Example 1, when the cutting knife 6 penetrates the fruit, the controller drives the liftable and sealable cover 14 to descend to the surface of the detection table 1, and the annular electromagnet is energized (current 1.5 A) to adsorb with the iron ring to form a 3 sealed chamber.

[0084] After the probe 13 punctures, the micro air pump is started to circulate the gas. The micro air pump circulates the gas at a flow rate of 0.8 L / min for 10 minutes. The volatile components enter the internal channel of the probe 13 along the spiral diversion groove, and after enriching the flavor substances, they are thermally desorbed to the gas chromatography. The controller compares with the standard flavor spectrum library and outputs the type and concentration value of fruity / fresh fragrance.

[0085] Example 3:

[0086] The difference from Example 2 is that the cutting knife 6 is a triangular cutting knife 6, and the surface of the triangular cutting knife 6 is provided with hydrophilic-hydrophobic alternating stripes. The stripe spacing decreases along the axial direction of the knife body, and the stripe spacing decreases in an arithmetic progression from the knife handle to the knife tip, with a tolerance Δd = 0.15 mm; the stripes divide the knife body into a hydrophobic area and a hydrophilic area. The contact angle of the hydrophobic area ≥ 150°, the contact angle of the hydrophilic area ≤ 20°, and a super-hydrophilic coating is provided within 5 mm of the knife tip, with a contact angle ≤ 5°. The hydrophobic area is treated with fluorosilane, and the hydrophilic area is activated by plasma.

[0087] The specific implementation process is as follows: During cutting, the juice flows directionally along the hydrophilic stripes, and the super-hydrophilic coating at the knife tip increases the spreading area of the juice by 300%, improving the spectral detection accuracy.

[0088] Dynamically compensate the sugar content detection value according to the liquid residue amount on the knife body surface, and the compensation coefficient ɑ = 0.93 - 0.05V, where V is the residual volume.

[0089] Example 4:

[0090] The difference from Example 3 is that it further includes an early warning module. The early warning module includes an indicator light and a buzzer, and both the indicator light and the buzzer are signal-connected to the controller. When the controller detects that one or more commercial data of the melon crop sample exceed the preset range, the controller triggers the early warning module, the indicator light flashes and emits light of a specific color, and the buzzer emits an alarm sound.

[0091] The specific implementation process is as follows: Set the parameter warning value through the touch screen (for example, sugar content < 8% Brix, crispness > 4.5 is abnormal). When the peel thickness exceeds the variety standard value by ±15%, the yellow light flashes (frequency 2Hz); when the characteristic spectrum of pesticide residue is detected, the red light is always on and the buzzer is triggered (85dB, intermittent ringing).

[0092] Hierarchical early warning: Level 1 early warning (single item exceeding the standard): single-color light flashes + short buzzer (0.5s / time); Level 2 early warning (multiple items exceeding the standard): three-color lights flash alternately + long buzzer (lasting 3s).

[0093] Example 5:

[0094] The difference from Example 4 is that it further includes a data storage module (using an industrial-grade SSD (1TB)). The data storage module is used to store the commercial data of the melon crop sample collected by the fiber Bragg grating strain sensor, the piezoelectric film array, the laser rangefinder 11, the multispectral camera 12, the telescopic member, the vibrator and the capacitive moisture detection ring, as well as the analysis results of the controller.

[0095] The specific implementation process is as follows: During the detection process, all data will be recorded and stored in the data storage module in real time. These data can be used for subsequent data analysis, variety classification, breeding research, etc. Operators can access and export these data through the user interface for further processing and analysis.

[0096] Obviously, the above examples are only for clearly illustrating the examples and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. And the obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. A commercial data acquisition device for melon crop germplasm resources, comprising a detection table (1), on which a controller, a clamping and cutting module for fixing and cutting melon crop samples, and a scanning and detection module for detecting commercial data of melon crop sample germplasm resources are installed. It is characterized in that, The clamping and cutting module includes several outer shells (2). A fiber Bragg grating strain sensor is installed inside the outer shell (2), and a cutting assembly is installed on the outer shell (2). The cutting assembly includes a cutting knife (6), and a piezoelectric film array is installed on the cutting edge of the cutting knife (6). The scanning and detection module includes an annular track (7). The annular track (7) is fixedly connected to the outer edge of the outer top wall of the detection table (1). A moving member (8) is slidably fitted on the annular track (7). A mounting bracket (9) is fixedly connected to the moving member (8). One end of the mounting bracket (9) away from the moving member (8) is fixedly connected to a mounting plate (10). A laser rangefinder (11) and a multispectral camera (12) are installed on the mounting plate (10). A telescopic member is installed on the mounting plate (10). A probe (13) is installed at one end of the telescopic member away from the mounting plate (10). The probe (13) is of a hollow structure, and a vibrator and a capacitive moisture detection ring are installed inside the probe (13). The fiber Bragg grating strain sensor, the piezoelectric film array, the moving member (8), the laser rangefinder (11), the multispectral camera (12), the telescopic member, the vibrator, and the capacitive moisture detection ring are all signal-connected to the controller. The controller calculates the diameter of the melon crop sample according to the deformation data of the spring (5) monitored by the fiber Bragg grating strain sensors on both sides in real time. At the same time, the controller generates a three-dimensional fruit model according to the real-time monitoring data of the fiber Bragg grating strain sensor and the laser rangefinder (11). When the piezoelectric film array monitors that the cutting assembly cuts the melon crop sample, the controller triggers the moving member (8). The moving member (8) moves on the annular track (7), and synchronously activates the multispectral camera (12) to continuously capture images of the cut section. When the laser rangefinder (11) detects that the sudden change in the peel thickness exceeds the set threshold, the controller controls the telescopic member to push the probe (13) until the capacitive moisture detection ring contacts the pulp surface and then stops, thereby establishing a dynamic compensation relationship between the piercing depth and the peel thickness. The vibrator starts to perform swept-frequency vibration after the probe (13) pierces to the set depth. The vibration decay time t and the determination of the meat texture thickness satisfy: When t < 1 ms, it is determined as fine meat texture; when 1 ms ≤ t < 3 ms, it is medium meat texture; when t ≥ 3 ms, it is thick meat texture.

2. The commercial data acquisition device for melon crop germplasm resources according to claim 1, wherein It also includes a headspace detection module. The headspace detection module includes a liftable and sealable cover (14). A spiral diversion groove is provided on the inner side wall of the liftable and sealable cover (14). The end of the spiral diversion groove is connected to a hose, and the hose is connected to the probe (13) to form a closed air flow circuit. An air pump is provided at the top of the liftable and sealable cover (14). An annular electromagnet is provided at the bottom edge of the liftable and sealable cover (14). An iron ring is embedded in the inner top wall of the detection table (1). When the sealable cover descends to the surface of the detection table (1), the electromagnet adsorbs the iron ring embedded in the detection table (1) to form a sealed chamber. When the probe (13) completes the piercing, the controller starts the air pump to make the volatile substances form a closed air flow circuit along the spiral diversion groove - probe (13) channel.

3. The commercial data acquisition device for melon crop germplasm resources according to claim 2, wherein, The outer shell (2) is symmetrically installed on the top of the detection table (1) along the central axis of the detection table (1). There are several jacks (3) inside the outer shell (2). Contact rods (4) are slidably fitted in the jacks (3). Springs (5) are sleeved on the contact rods (4). One end of the spring (5) is fixedly connected to the outer side wall of the contact rod (4), and the other end of the spring (5) is fixedly connected to the bottom of the jack (3). The fiber Bragg grating strain sensor is installed on the side wall of the jack (3). The cutting assembly includes a handle (15). An installation cavity (201) is provided in the side wall of the outer shell (2). The handle (15) is hinged with a cross bar (16). The cross bar (16) is located in the installation cavity (201). One end of the cross bar (16) away from the handle (15) is hinged with a special-shaped rod (17). Crank structures are hinged on both the special-shaped rod (17) and the handle (15). The crank structure includes a connecting rod (18). One end of the connecting rod (18) away from the special-shaped rod (17) and the handle (15) is hinged with a push block (19). The side of the push block (19) away from the connecting rod (18) is fixedly connected to the cutting knife (6).

4. The commercial data acquisition device for melon crop germplasm resources according to claim 3, characterized in that, The cutting knife (6) is a triangular cutting knife (6). Hydrophobic-hydrophilic alternating stripes are provided on the surface of the triangular cutting knife (6). The stripe spacing decreases along the axial direction of the knife body. The stripe spacing decreases in an arithmetic progression from the knife handle to the knife tip, and the common difference Δd = 0.15 mm. The stripes divide the knife body into a hydrophobic area and a hydrophilic area. The contact angle of the hydrophobic area is ≥150°, and the contact angle of the hydrophilic area is ≤20°. And a super-hydrophilic coating is provided within 5 mm of the knife tip, and the contact angle is ≤5°.

5. The commercial data acquisition device for melon crop germplasm resources according to claim 4, characterized in that, The puncturing end of the probe (13) is provided with a hierarchical puncturing structure. The hierarchical puncturing structure is composed of concentric needle tubes with diameters of 0.1 mm, 0.5 mm, and 1 mm. Each needle tube is independently connected to a pressure sensor, and the pressure sensor is signal-connected to the controller. When the probe (13) contacts the pulp, the controller determines the crispness of the pulp according to the ratio of the pressure change rates of each needle tube: If (P1 / P0) > 2.5 and (P2 / P1) < 1.2, it is determined as crispy pulp; If (P1 / P0) < 1.8 and (P2 / P1) > 1.5, it is determined as soft pulp; Where P0, P1, and P2 are the initial puncturing pressures of the 0.1 mm, 0.5 mm, and 1 mm needle tubes respectively.

6. The commercial data acquisition device for melon crop germplasm resources according to claim 5, characterized in that, When the controller performs fruit shell pattern analysis: The image data collected by the multispectral camera (12) is fused with the height data of the laser rangefinder (11) to generate a pattern depth distribution map; Define the texture significance index where Δh is the height difference between adjacent points, Δc is the color difference, and A is the area of the analysis region; When S > 8, it is determined as a deep pattern variety, 3 ≤ S ≤ 8 is a medium pattern, and S < 3 is a shallow pattern variety.

7. The commercial data acquisition device for melon crop germplasm resources according to claim 6, characterized in that, When the vibrator of the probe (13) performs sweep-frequency vibration: The initial frequency f0 = 100 Hz, and it increases to 2000 Hz at a rate of Δf = 50 Hz / ms; The vibration attenuation curve is fitted as A(t) = A0·e ∧ (-βt)·sin(ωt + φ), where A0 is the initial amplitude, β is the damping coefficient, ω is the angular frequency, φ is the phase angle, t is the time, and the value of β has a linear positive correlation with the meat fiber density.

8. The commercial data acquisition device for melon crop germplasm resources according to claim 7, wherein It also includes an early warning module. The early warning module includes an indicator light and a buzzer. Both the indicator light and the buzzer are signal-connected to the controller; When the controller detects that one or more commercial data of the melon crop sample exceed the preset range, the controller triggers the early warning module. The indicator light flashes and emits light of a specific color, and the buzzer emits an alarm sound.

9. The commercial data acquisition device for melon crop germplasm resources according to claim 8, wherein It further includes a data storage module which is used to store the commercial data of the melon crop samples collected by the fiber Bragg grating strain sensor, the piezoelectric film array, the laser rangefinder (11), the multispectral camera (12), the telescopic member, the vibrator and the capacitive moisture detection ring, as well as the analysis results of the controller.

10. The commercial data acquisition device for melon crop germplasm resources according to claim 9, characterized in that It further includes a user interaction interface which is signal-connected to the controller. The user interaction interface is used to display the commercial data and analysis results of the melon crop samples. At the same time, the user interaction interface is used to provide operation options through a touch screen for the operator to input instructions or adjust parameters.

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