A Macadamia Nut Maturity Assessment System

Abstract

This invention discloses a macadamia nut maturity assessment system, belonging to the field of maturity assessment technology. The system includes a monitoring trigger module, an image processing module, a spectral analysis module, and a detection adjustment module. Its key technical points are: it achieves refined assessment and dynamic adjustment of macadamia nut maturity; it analyzes the hardness of fruits that do not meet the standards, and constructs a dynamic adjustment calculation model by combining hardness values, standard values, and adjustment datasets to generate corrected assessment thresholds, effectively solving the problem of inaccurate assessment results caused by fixed thresholds in traditional assessment methods; by introducing two adjustment terms—the proportion of abnormal hardness and the average hardness deviation—it comprehensively considers the hardness of fruits that do not meet the standards, achieving refined adjustment of the assessment thresholds and improving the accuracy and flexibility of the assessment; simultaneously, by dynamically adjusting the assessment thresholds, it can more accurately reflect the actual maturity of the fruits, providing a scientific basis for agricultural production.

Description

Technical Field

[0001] This invention relates to the field of maturity assessment technology, specifically to a macadamia nut maturity assessment system. Background Technology

[0002] The maturity of macadamia nuts is primarily assessed through appearance and physicochemical indicators. Appearance-wise, mature macadamia nuts are dark brown or reddish-brown in color, with a hard shell that tilts slightly, and the nuts will fall off naturally. Physicochemically, maturity can be determined by testing the oil content of the kernel; mature kernels typically have a very high oil content, reaching around 72%. In practice, after peeling and shelling fresh nuts, the kernels are placed in water and observed for buoyancy. If the kernels float completely, the nuts are mature; if they sink, it indicates a high sugar content and incomplete oil conversion, meaning the nuts are not yet fully mature. Combining visual observation with physicochemical testing allows for a relatively accurate assessment of macadamia nut maturity, ensuring the harvest of high-quality nuts.

[0003] In the existing process of assessing nut maturity, one or more monitoring points need to be selected. The results of the monitoring points are used to reflect the entire nut planting area. Traditional agricultural monitoring suffers from large assessment errors and waste of resources due to uneven distribution of monitoring points and inaccurate monitoring timing. When assessing nut maturity, manual contact is used to determine the maturity. If the operation is not done properly, it can not only cause the spread of pests and diseases, but also make it difficult to achieve a fast and accurate assessment. The error of the assessment results is also difficult to avoid. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a macadamia nut maturity assessment system, which solves the problems mentioned in the background art.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A macadamia nut maturity assessment system, the system comprising:

[0009] The monitoring trigger module evenly allocates monitoring points according to the area of ​​the target park, and obtains the real-time density of macadamia nuts at each monitoring point. During the estimated maturity period, if the real-time density curve of any monitoring point shows a downward trend over time, the current monitoring point is monitored to obtain the time point when the real-time density drops from the maximum value to the predetermined interval calibration value, and the maturity assessment mechanism is triggered at that time point.

[0010] The image processing module executes a maturity assessment mechanism, randomly acquires image data of M fallen macadamia nuts at the corresponding monitoring point, uses edge detection and texture analysis technology to obtain a comparison dataset for each macadamia nut, builds a rule engine, compares the data in the comparison dataset with the corresponding threshold, and calculates the maturity level value at the corresponding monitoring point based on the comparison results.

[0011] The spectral analysis module constructs a weighted calculation model based on the maturity level value and the spectral analysis results, generates a maturity estimate for the target park, and compares the maturity estimate with the preset evaluation threshold to determine whether the maturity of macadamia nuts in the target park meets the standard.

[0012] If the fuzzy criteria are met, output the result.

[0013] If the target is not met, an analysis and adjustment strategy will be triggered.

[0014] The detection and adjustment module executes the analysis and adjustment strategy, extracts the number of macadamia nuts in the target park whose maturity has not met the standard and the quantity S of the nuts. It performs hardness analysis and measurement on the nuts that have not met the standard to obtain the hardness value of each nut, and compares each hardness value with the standard value to obtain the percentage of nuts with a hardness value exceeding the standard value. Combining the collected adjustment dataset, it constructs a dynamic adjustment calculation model to generate a corrected evaluation threshold. The corrected evaluation threshold is then compared with the maturity estimate for the second time, and the comparison results determine whether the maturity of macadamia nuts in the target park meets the standard.

[0015] Furthermore, the ratio between monitoring points and the area occupied is 1:30. The number of monitoring points is set as follows: if the area occupied is less than 30 square meters, a monitoring point is placed in the center of the target park; if the area occupied is not an integer multiple of 30 square meters, the number of monitoring points is the current area occupied, which is the number of points divided by 30 and rounded up.

[0016] Furthermore, the preset interval calibration value is represented as: the maximum real-time density - 0.3% of the maximum real-time density, and 0.3% of the maximum real-time density represents the preset interval value.

[0017] Furthermore, the comparison dataset includes the number of cracks and texture contrast; the number of cracks is identified by the Canny edge detection algorithm, and the texture contrast is calculated by GLCM.

[0018] Furthermore, the data in the comparison dataset is compared with the corresponding threshold. The comparison results are as follows: for each fallen macadamia nut, if the number of cracks exceeds the corresponding threshold and the texture contrast is lower than the corresponding threshold, the maturity level of the corresponding macadamia nut is marked as level two; if the number of cracks is lower than the corresponding threshold and the texture contrast exceeds the corresponding threshold, the maturity level of the corresponding macadamia nut is marked as level one.

[0019] Furthermore, the process of deriving the maturity level value for the corresponding monitoring point is as follows:

[0020] Calculate the maturity level of each of the M macadamia nuts;

[0021] The average value is calculated and obtained as the maturity level value for the corresponding monitoring point.

[0022] The value of M is at least 3 and is a positive integer.

[0023] Furthermore, the spectral analysis process is as follows: the fatty acid content of M fallen macadamia nuts is analyzed using a near-infrared spectrometer, so the fatty acid content of each macadamia nut is the result of the spectral analysis.

[0024] Furthermore, the process of constructing the weighted calculation model is as follows:

[0025] The fatty acid content of M macadamia nuts was depolarized.

[0026] The average fatty acid content is obtained by calculating the mean.

[0027] The average fatty acid content and maturity grade values ​​were dimensionless and then weighted for calculation. The formula is as follows:

[0028] ;

[0029] In the formula, Gy represents the maturity estimate, F1 and F2 are both weighting coefficients, and F1 + F2 = 1. The value represents the average fatty acid content, and Cd represents the maturity level.

[0030] Furthermore, the maturity assessment is compared with the preset assessment threshold. The comparison process is as follows:

[0031] If the maturity assessment exceeds the evaluation threshold, it is determined that the fuzzy standard has been met; otherwise, it is determined that the fuzzy standard has not been met.

[0032] Furthermore, the original assessment threshold T and the average firmness value of the non-ambiguous qualified fruit were also adjusted. Standard value ;

[0033] The formula used to construct the dynamically adjusted calculation model is as follows:

[0034] ;

[0035] In the formula, T1 represents the corrected evaluation threshold, α and γ are adjustment coefficients, and Q represents the number of fruits with a firmness value exceeding the standard value; the values ​​of α and γ are both between 0 and 1.

[0036] A method for assessing the maturity of macadamia nuts includes the following steps:

[0037] S1. Based on the area of ​​the target park, monitor points are evenly distributed to obtain the real-time density of macadamia nuts at each monitor point. During the estimated maturity period, if the real-time density curve of any monitor point shows a downward trend over time, the current monitor point is monitored to obtain the time point when the real-time density drops from the maximum value to the predetermined interval calibration value, and the maturity assessment mechanism is triggered at that time point.

[0038] S2. Execute the maturity assessment mechanism, randomly obtain image data of M fallen macadamia nuts at the corresponding monitoring point, use edge detection and texture analysis technology to obtain the comparison dataset of each macadamia nut, build a rule engine, compare the data in the comparison dataset with the corresponding definition threshold, and calculate the maturity level value at the corresponding monitoring point based on the comparison results.

[0039] S3. Based on the maturity level value and the spectral analysis results, construct a weighted calculation model to generate the maturity estimate of the target park, and compare the maturity estimate with the preset evaluation threshold to determine whether the maturity of macadamia nuts in the target park meets the standard.

[0040] If the fuzzy criteria are met, output the result.

[0041] If the target is not met, an analysis and adjustment strategy will be triggered.

[0042] S4. Implement the analysis and adjustment strategy, extract the number of macadamia nuts in the target park whose maturity has not met the standard and the number of nuts S. Perform hardness analysis and measurement on the nuts that have not met the standard to obtain the hardness value of each nut. Compare each hardness value with the standard value to obtain the percentage of nuts with hardness values ​​exceeding the standard value. Combine the collected adjustment dataset to construct a dynamic adjustment calculation model and generate a corrected evaluation threshold. Compare the corrected evaluation threshold with the maturity estimate for the second time. Based on the comparison results, determine whether the maturity of macadamia nuts in the target park meets the standard.

[0043] (III) Beneficial Effects

[0044] This invention provides a system for assessing the maturity of macadamia nuts, which has the following advantages:

[0045] (1) This scheme ensures comprehensive and efficient monitoring coverage by setting a reasonable ratio of monitoring points to land area; at the same time, it uses real-time density change curves to capture subtle changes in the early stage of fruit ripening in a timely manner. When the density drops to the preset threshold, the ripeness assessment mechanism is automatically triggered, which improves the accuracy and timeliness of the assessment. This method not only optimizes the use of monitoring resources, but also reduces improper harvesting time caused by misjudging fruit ripeness, thereby ensuring the yield and quality of macadamia nuts and improving the level of intelligent agricultural production and economic benefits.

[0046] (2) This solution achieves accurate assessment and intelligent management of macadamia nut maturity, accurately identifies the cracks and texture features of nuts, and determines the maturity level by combining a rule engine, effectively solving the problems of strong subjectivity and low accuracy of traditional assessment methods; at the same time, it also uses near-infrared spectroscopy to detect fatty acid content, and constructs a weighted calculation model based on maturity level values ​​to scientifically assess the overall maturity of the park, and achieves a preliminary fuzzy judgment of maturity by comparing with preset thresholds; this solution integrates image recognition and spectral analysis technology, which not only improves the accuracy and objectivity of the assessment, but also enhances the adaptability and robustness of the model;

[0047] (3) This scheme realizes the refined assessment and dynamic adjustment of the maturity of macadamia nuts. It analyzes the hardness of fruits that do not meet the standards, and constructs a dynamic adjustment calculation model by combining hardness value, standard value and adjustment dataset to generate the corrected assessment threshold. This effectively solves the problem of inaccurate assessment results caused by fixed thresholds in traditional assessment methods.

[0048] By introducing two adjustment items—the proportion of abnormal hardness and the average hardness deviation—the hardness of fruits that do not meet the standards is comprehensively considered, enabling fine-tuning of the assessment threshold and improving the accuracy and flexibility of the assessment. Simultaneously, this technical solution triggers the adjustment mechanism only when the fruit is initially determined to be not fully compliant, ensuring the stability and efficiency of the assessment process. Dynamically adjusting the assessment threshold more accurately reflects the actual ripeness of the fruit, providing a scientific basis for agricultural production, helping to optimize harvesting decisions, and to some extent improving yield and quality, further promoting the development of smart agriculture. Attached Figure Description

[0049] Figure 1 This is a modular schematic diagram of a macadamia nut maturity assessment system according to the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1:

[0052] Please see Figure 1 This embodiment provides a system for assessing the maturity of macadamia nuts;

[0053] Traditionally, the maturity assessment of macadamia nuts has relied mainly on manual visual inspection, tactile assessment of shell hardness, and experience. These methods are subjective, inefficient, and difficult to standardize, especially in large-scale plantations where rapid and accurate assessment is challenging. Considering specific scenarios such as nighttime assessment, rapid screening in large-scale plantations, and the need to minimize human contact to reduce the spread of pests and diseases, this system design will combine modern sensing and image recognition technologies to achieve an intelligent and automated maturity assessment system.

[0054] The evaluation system comprises various functional modules, namely, a monitoring and triggering module, an image processing module, a spectral analysis module, and a detection adjustment module; the system functional modules are described below:

[0055] The monitoring trigger module evenly distributes monitoring points according to the area of ​​the target park, with a ratio of 1:30 between the monitoring points and the area. It acquires the real-time density of macadamia nuts at each monitoring point. During the estimated maturity period, if the real-time density curve of any monitoring point shows a downward trend over time, it monitors the moment when the real-time density drops from the maximum value to the predetermined interval calibration value at the current monitoring point and triggers the maturity assessment mechanism at that moment.

[0056] The required number of monitoring points is set as follows:

[0057] If the area occupied is less than 30 square meters, a monitoring point will be placed in the center of the target park; if the area occupied is not a multiple of 30, the number of monitoring points will be the integer value of the current area occupied as a multiple of 30. For example, if the area occupied is 70, then the area occupied is 2.3 times 30, and the number of monitoring points will be 3 after rounding up.

[0058] The uniform distribution of monitoring points is illustrated with an example:

[0059] If the target park has 4 monitoring points, the target park will be divided into four equal parts, and a high-definition camera will be placed at the center of each part. The high-definition camera will be used to monitor one or more fruit trees.

[0060] Estimated maturity period:

[0061] This indicates the historical maturity period of macadamia nuts in the target area and climate. This period is derived from the earliest and latest maturity times obtained from historical data to determine the estimated maturity period {earliest time, latest time}; for example {July 10, August 10}. This example is for reference and understanding purposes only.

[0062] Real-time density of macadamia nuts:

[0063] This indicates the density of fruits (i.e., the real-time density of macadamia nuts) within a fixed frame area captured by the high-definition camera at the corresponding monitoring point. This real-time density increases continuously as the fruit tree grows. When it increases to a certain value, it means that the fruits have taken shape and the number no longer increases. After that, some fruits will fall from the tree after they mature. At this time, the density of fruits within the fixed frame area decreases over time, indicating that the fruits have matured and fallen. However, not all fallen fruits fall because they are mature. Therefore, it is necessary to set a value before it can be determined whether the ripening evaluation mechanism needs to be triggered.

[0064] Curve graph:

[0065] In the graph, the X-axis represents time, and the Y-axis represents real-time density. Therefore, real-time density changes over time. When it reaches its highest point, it indicates that the real-time density has reached its maximum value. At this point, some mature fruits fall, which affects the real-time density. As more fruits fall, the curve of real-time density decreases over time. The preset interval calibration value represents: the maximum real-time density minus 0.3% of the maximum real-time density. Therefore, 0.3% of the maximum real-time density represents the preset interval value. The time point corresponding to the preset interval calibration value also indicates that the number of macadamia nuts falling at the monitoring point has reached a certain level.

[0066] By adopting the above technical solution, monitoring points can be scientifically configured according to the target park area, and key nodes in the fruit ripening process can be accurately identified by monitoring the density changes of macadamia nuts in real time.

[0067] This technology effectively solves the problems of large assessment errors and resource waste caused by uneven distribution of monitoring points and inaccurate monitoring timing in traditional agricultural monitoring. By setting a reasonable ratio of monitoring points to land area, it ensures comprehensive and efficient monitoring coverage. At the same time, by using real-time density change curves, it can capture subtle changes in the early stage of fruit ripening in a timely manner. When the density drops to a preset threshold, it automatically triggers the ripeness assessment mechanism, which improves the accuracy and timeliness of the assessment.

[0068] This method not only optimizes the use of monitoring resources but also reduces improper harvesting timing caused by misjudging fruit ripeness, thereby ensuring the yield and quality of macadamia nuts and improving the level of intelligence and economic benefits of agricultural production. In addition, this technical solution has strong adaptability and scalability and can be extended to the ripeness monitoring of other fruit trees, providing strong support for the development of smart agriculture.

[0069] The image processing module executes a ripeness assessment mechanism, randomly acquires image data of M fallen macadamia nuts at the corresponding monitoring point, uses edge detection and texture analysis technology to obtain a comparison dataset for each macadamia nut, builds a rule engine, compares the data in the comparison dataset with the corresponding threshold, and calculates the ripeness level value at the corresponding monitoring point based on the comparison results; the value of M is at least 3, and usually 10.

[0070] The comparison dataset includes the number of cracks and texture contrast.

[0071] The Canny edge detection algorithm identifies the number of cracks, and the texture contrast is calculated using the GLCM; Gray-level Co-occurrence Matrix (GLCM): used to describe the spatial dependency of gray levels in an image;

[0072] The comparison results are as follows:

[0073] For each fallen macadamia nut, if the number of cracks exceeds the corresponding threshold and the texture contrast is lower than the corresponding threshold, the maturity level of the corresponding macadamia nut is marked as level two; if the number of cracks is lower than the corresponding threshold and the texture contrast exceeds the corresponding threshold, the maturity level of the corresponding macadamia nut is marked as level one.

[0074] Level 2 indicates that the macadamia nuts are at a high level of maturity;

[0075] Grade 1 indicates that the macadamia nuts are less mature;

[0076] It should be noted that if a fallen macadamia nut is detected to be neither Grade 1 nor Grade 2, it will be removed. However, this situation usually does not occur. If it does, the value of M will be the value of the corresponding number of macadamia nuts to be removed. For ease of representation and calculation, we will continue to use M.

[0077] The process of calculating the maturity level value at the corresponding monitoring point is as follows:

[0078] The maturity level of each of the M macadamia nuts is calculated, and then the average value is taken as the maturity level value at the corresponding monitoring point. For example, if M=3, the first nut is level 2, the second nut is level 2, and the third nut is level 1, then the maturity level value at the corresponding monitoring point is 2.5.

[0079] Spectral analysis module:

[0080] Based on maturity level values ​​and spectral analysis results, a weighted calculation model is constructed to generate maturity estimates for the target park. The maturity estimates are then compared with preset assessment thresholds to determine whether the macadamia nut maturity in the target park meets the fuzzy standard. If it meets the fuzzy standard, the result is output; if it does not meet the fuzzy standard, an analysis and adjustment strategy is triggered.

[0081] The process of spectral analysis is as follows:

[0082] The fatty acid content of M fallen macadamia nuts was analyzed by near-infrared spectroscopy, so the fatty acid content of each macadamia nut is the result of the spectral analysis.

[0083] The process of constructing the weighted calculation model is as follows:

[0084] The fatty acid content of M macadamia nuts was depolarized and then averaged to obtain the average fatty acid content. The average fatty acid content and maturity grade values ​​were then dimensionless and weighted for calculation. The formula is as follows:

[0085] ;

[0086] In the formula, Gy represents the maturity estimate, F1 and F2 are both weighting coefficients, and F1 + F2 = 1. The value represents the average fatty acid content, and Cd represents the maturity grade value.

[0087] The weighting coefficients are determined using the coefficient of variation method, which assigns weights to each indicator based on the degree of variation between the current value and the target value. If the numerical difference of an indicator is large, it can clearly distinguish each evaluated object, indicating that the indicator has rich discriminative information and should therefore be given a larger weight. Conversely, if the numerical difference of each evaluated object on a certain indicator is small, then the indicator's ability to distinguish each evaluated object is weak, and therefore it should be given a smaller weight. This method directly utilizes the information contained in each indicator to calculate the weight of the indicator, thus possessing objectivity.

[0088] The maturity valuation preset is compared with the assessment threshold. The comparison process is as follows:

[0089] If the maturity assessment exceeds the evaluation threshold, it is determined that the fuzzy standard has been met; otherwise, it is determined that the fuzzy standard has not been met.

[0090] By adopting the above technical solution, the maturity of macadamia nuts can be accurately assessed and intelligently managed. The image processing module accurately identifies the cracks and texture features of the nuts, and the maturity level is determined by the rule engine, which effectively solves the problems of strong subjectivity and low accuracy of traditional assessment methods.

[0091] Meanwhile, the spectral analysis module uses near-infrared spectroscopy to detect fatty acid content, and combines it with maturity level values ​​to construct a weighted calculation model to scientifically assess the overall maturity of the park. By comparing it with a preset threshold, it achieves a fuzzy judgment of maturity. This solution integrates image recognition and spectral analysis technologies, which not only improves the accuracy and objectivity of the assessment, but also enhances the adaptability and robustness of the model by dynamically determining the weights through the coefficient of variation method.

[0092] Furthermore, this technical solution ensures the reliability of the assessment results, provides a scientific basis for agricultural production, helps optimize harvesting timing, improves yield and quality, promotes the development of smart agriculture, and solves problems such as resource waste and low efficiency caused by inaccurate maturity assessment in agricultural production.

[0093] The detection and adjustment module executes the analysis and adjustment strategy, extracts the number of macadamia nuts in the target park whose maturity has not met the standard and the quantity S of the nuts. It performs hardness analysis and measurement on the nuts that have not met the standard to obtain the hardness value of each nut, and compares each hardness value with the standard value to obtain the proportion of nuts with hardness values ​​exceeding the standard value, i.e., Q / S, where Q represents the number of nuts with hardness values ​​exceeding the standard value. Combining the collected adjustment dataset, a dynamic adjustment calculation model is constructed to generate a corrected evaluation threshold. The corrected evaluation threshold is compared with the maturity estimate for the second time, and the comparison results determine whether the maturity of macadamia nuts in the target park meets the standard.

[0094] The adjustments to the dataset include:

[0095] The original assessment threshold T, and the average firmness value of the non-ambiguous qualified fruit. Standard value ;

[0096] The formula used to construct the dynamically adjusted calculation model is as follows:

[0097] ;

[0098] In the formula, T1 represents the corrected evaluation threshold, and α and γ are adjustment coefficients used to control the degree to which Q / S and hardness deviation reduce the original evaluation threshold T; the values ​​of α and γ are between 0 and 1.

[0099] It should be noted that the proportion of abnormal hardness affects the assessment threshold: α×Q / S represents the impact of the proportion of unqualified fruits with hardness values ​​exceeding the standard value on the assessment threshold; when this proportion increases, it indicates that the hardness of many unqualified fruits is actually close to or exceeds the standard, therefore the assessment threshold should be lowered; the average hardness deviation also affects the assessment threshold. This indicates the impact of the relative deviation between the average hardness value of the non-compliant fruit and the standard hardness value on the evaluation threshold. When the average hardness value is higher than the standard value, this deviation is positive, indicating that the hardness of the non-compliant fruit is generally too high, and therefore the evaluation threshold should be lowered.

[0100] Comprehensive adjustment: By introducing two adjustment terms (proportion of abnormal hardness and average hardness deviation), the formula can more comprehensively reflect the hardness of fruits that do not meet the standards and make more precise adjustments to the evaluation threshold. The above formula model is still triggered when it is initially judged as not meeting the standards. Only when the estimated maturity of the fruit does not exceed the original evaluation threshold T will we further consider the hardness of the fruit and adjust the evaluation threshold according to the above formula.

[0101] By adopting the above technical solution, a refined assessment and dynamic adjustment of macadamia nut maturity is achieved. Through the detection and adjustment module, the hardness of fruits that do not meet the standards is analyzed. Combining the hardness value, standard value and adjustment dataset, a dynamic adjustment calculation model is constructed to generate a corrected assessment threshold, which effectively solves the problem of inaccurate assessment results caused by fixed thresholds in traditional assessment methods.

[0102] This scheme introduces two adjustment items: the proportion of abnormal hardness and the average hardness deviation. By comprehensively considering the hardness of fruits that do not meet the standards, it achieves fine adjustment of the assessment threshold, improving the accuracy and flexibility of the assessment. Simultaneously, the adjustment mechanism is triggered only when the fruit is initially determined to be not fully compliant, ensuring the stability and efficiency of the assessment process. By dynamically adjusting the assessment threshold, the actual ripeness of the fruit can be more accurately reflected, providing a scientific basis for agricultural production. This helps optimize harvesting decisions, improve yield and quality, further promotes the development of smart agriculture, and solves the problems of inaccurate ripeness assessment and resource waste caused by fixed assessment thresholds in agricultural production.

[0103] Example 2:

[0104] Based on Example 1, this example also provides a method for assessing the maturity of macadamia nuts, including the following steps:

[0105] S1. Based on the area of ​​the target park, monitor points are evenly distributed to obtain the real-time density of macadamia nuts at each monitor point. During the estimated maturity period, if the real-time density curve of any monitor point shows a downward trend over time, the current monitor point is monitored to obtain the time point when the real-time density drops from the maximum value to the predetermined interval calibration value, and the maturity assessment mechanism is triggered at that time point.

[0106] S2. Execute the maturity assessment mechanism, randomly obtain image data of M fallen macadamia nuts at the corresponding monitoring point, use edge detection and texture analysis technology to obtain the comparison dataset of each macadamia nut, build a rule engine, compare the data in the comparison dataset with the corresponding definition threshold, and calculate the maturity level value at the corresponding monitoring point based on the comparison results.

[0107] S3. Based on the maturity level value and the spectral analysis results, construct a weighted calculation model to generate the maturity estimate of the target park, and compare the maturity estimate with the preset evaluation threshold to determine whether the maturity of macadamia nuts in the target park meets the standard.

[0108] If the fuzzy criteria are met, output the result.

[0109] If the target is not met, an analysis and adjustment strategy will be triggered.

[0110] S4. Implement the analysis and adjustment strategy, extract the number of macadamia nuts in the target park whose maturity has not met the standard and the number of nuts S. Perform hardness analysis and measurement on the nuts that have not met the standard to obtain the hardness value of each nut. Compare each hardness value with the standard value to obtain the percentage of nuts with hardness values ​​exceeding the standard value. Combine the collected adjustment dataset to construct a dynamic adjustment calculation model and generate a corrected evaluation threshold. Compare the corrected evaluation threshold with the maturity estimate for the second time. Based on the comparison results, determine whether the maturity of macadamia nuts in the target park meets the standard.

[0111] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0113] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A system for assessing the maturity of macadamia nuts, characterized in that, The system includes: The monitoring trigger module evenly allocates monitoring points according to the area of ​​the target park, and obtains the real-time density of macadamia nuts at each monitoring point. During the estimated maturity period, if the real-time density curve of any monitoring point shows a downward trend over time, the current monitoring point is monitored to obtain the time point when the real-time density drops from the maximum value to the predetermined interval calibration value, and the maturity assessment mechanism is triggered at that time point. The image processing module executes a maturity assessment mechanism, randomly acquires image data of M fallen macadamia nuts at the corresponding monitoring point, uses edge detection and texture analysis technology to obtain a comparison dataset for each macadamia nut, builds a rule engine, compares the data in the comparison dataset with the corresponding threshold, and calculates the maturity level value at the corresponding monitoring point based on the comparison results. The spectral analysis module constructs a weighted calculation model based on the maturity level value and the spectral analysis results, generates a maturity estimate for the target park, and compares the maturity estimate with the preset evaluation threshold to determine whether the maturity of macadamia nuts in the target park meets the standard. If the fuzzy criteria are met, output the result. If the target is not met, an analysis and adjustment strategy will be triggered. The detection and adjustment module executes the analysis and adjustment strategy, extracts the macadamia nuts in the target park whose maturity has not met the standard and the number of nuts S, performs hardness analysis and measurement on the nuts that have not met the standard, obtains the hardness value of each nut, compares each hardness value with the standard value, obtains the percentage of nuts with hardness values ​​exceeding the standard value, and combines the collected adjustment dataset to construct a dynamic adjustment calculation model, generates a corrected evaluation threshold, compares the corrected evaluation threshold with the maturity estimate a second time, and determines whether the maturity of macadamia nuts in the target park meets the standard based on the comparison results. The data in the comparison dataset is compared with the corresponding threshold. The comparison results are as follows: for each fallen macadamia nut, if the number of cracks exceeds the corresponding threshold and the texture contrast is lower than the corresponding threshold, the maturity level of the corresponding macadamia nut is marked as level two; if the number of cracks is lower than the corresponding threshold and the texture contrast exceeds the corresponding threshold, the maturity level of the corresponding macadamia nut is marked as level one. The process of obtaining the maturity level value for the corresponding monitoring point is as follows: Calculate the maturity level of each of the M macadamia nuts; The average value is calculated and obtained as the maturity level value for the corresponding monitoring point. Where M is at least 3 and is a positive integer; The process of spectral analysis is as follows: the fatty acid content of M fallen macadamia nuts is analyzed by a near-infrared spectrometer, so the fatty acid content of each macadamia nut is the result of spectral analysis. The process of constructing the weighted calculation model is as follows: The fatty acid content of M macadamia nuts was depolarized. The average fatty acid content is obtained by calculating the mean. The average fatty acid content and maturity grade values ​​were dimensionless and then weighted for calculation. The formula is as follows: ； In the formula, Gy represents the maturity estimate, F1 and F2 are both weighting coefficients, and F1 + F2 = 1. The value represents the average fatty acid content, and Cd represents the maturity grade value. The dataset adjustments included: the original evaluation threshold T, and the average firmness value of the unambiguous, qualified fruits. Standard value ; The formula used to construct the dynamically adjusted calculation model is as follows: ； In the formula, T1 represents the corrected evaluation threshold, α and γ are adjustment coefficients, and Q represents the number of fruits with a firmness value exceeding the standard value; the values ​​of α and γ are both between 0 and 1.

2. The macadamia nut maturity assessment system according to claim 1, characterized in that: The ratio between monitoring points and the area occupied is 1:

30. The number of monitoring points is set as follows: if the area occupied is less than 30 square meters, a monitoring point is placed in the center of the target park; if the area occupied is not an integer multiple of 30 square meters, the number of monitoring points is the current area occupied divided by 30 and rounded up.

3. The macadamia nut maturity assessment system according to claim 1, characterized in that: The preset interval calibration value is represented as: the maximum real-time density value minus 0.3% of the maximum real-time density value, and 0.3% of the maximum real-time density value represents the preset interval value.

4. The macadamia nut maturity assessment system according to claim 1, characterized in that: The comparison dataset includes the number of cracks and texture contrast; the number of cracks is identified by the Canny edge detection algorithm, and the texture contrast is calculated by GLCM.

5. The macadamia nut maturity assessment system according to claim 1, characterized in that: The maturity estimate is compared with the preset assessment threshold. The comparison process is as follows: If the maturity assessment exceeds the evaluation threshold, it is determined that the fuzzy standard has been met; otherwise, it is determined that the fuzzy standard has not been met.

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