Indoor cultivation method and system for edible mushrooms and storage medium

Abstract

The invention relates to the technical field of edible mushroom cultivation, in particular to an indoor cultivation method and system for edible mushrooms and a storage medium. The method comprises the steps that differential monitoring frequencies are set according to different growth and development stages in the fruiting stage, and pileus diameter, pileus height and pileus expansion degree morphological characteristic data of sporocarps at the current monitoring time point are collected; the growth rate is calculated by comparing the morphological characteristic data of the adjacent time points; preliminarily judging whether the growth is stagnated according to the growth rate, if so, further judging whether the growth is mature according to a maturity standard, and triggering a harvesting prompt; if not, predicting the harvesting time length; and if the growth is stagnated but is immature, continuously monitoring and identifying growth abnormality through a secondary verification mechanism. Through dynamic monitoring and intelligent analysis of multi-dimensional morphological characteristics, accurate judgment of harvesting opportunity and early warning of abnormal growth are realized, the management difficulty of household users is effectively reduced, and the yield and quality of mushrooms are improved.

Description

Technical Field

[0001] This invention relates to the field of edible fungi cultivation technology, and specifically to a method, system, and storage medium for indoor cultivation of edible fungi. Background Technology

[0002] In indoor cultivation of edible fungi, meticulous management during the fruiting stage has a decisive impact on the final yield and quality. In particular, the timing of harvesting directly affects the marketability and edible value of the fruiting bodies. If harvested too early, the fruiting bodies are not fully developed, the caps are not fully opened, the flesh is thin, the yield is low, and the flavor and nutrition are not optimal. If harvested too late, the caps are completely flat or even upturned, the gills soften, and a large number of spores are released, leading to a decline in mushroom quality, a worse taste, and increased susceptibility to decay. Simultaneously, spore release consumes nutrients in the substrate bag, inhibiting subsequent fruiting and resulting in an overall reduction in yield.

[0003] Currently, family users or small cultivation farms generally lack professional experience in managing the fruiting period. They find it difficult to accurately judge the growth stage and maturity of the fruiting bodies by visual observation and often rely on subjective experience or fixed harvesting times, which can easily lead to problems with improper harvesting timing. This makes it impossible to guarantee the quality and yield of mushrooms, thus restricting the promotion and efficiency improvement of family mushroom houses. Summary of the Invention

[0004] In view of this, in order to solve the problems mentioned in the background art, a method, system and storage medium for indoor cultivation of edible fungi are proposed.

[0005] The technical solution adopted by the present invention to solve its technical problem is as follows: Firstly, the present invention provides a method for indoor cultivation of edible fungi, including the following steps: S1: During the fruiting stage, morphological characteristic data of the fruiting bodies are collected at the current monitoring time point, wherein the morphological characteristic data includes cap diameter, cap height and cap expansion.

[0006] S2: Compare the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point to analyze and obtain the growth rate of the sub-entity at the current monitoring time point.

[0007] S3: Based on the growth rate at the current monitoring time point, make a preliminary judgment on whether the growth of the fruiting body has stopped. If the growth has not stopped, proceed to step S4. If the growth has stopped, determine whether the fruiting body is mature according to the preset maturity standard. If it is mature, trigger a harvest reminder. If it is not mature, proceed to step S5.

[0008] S4: Based on the growth rate trend curve of the fruiting body over time and the growth rate at the current monitoring time point, predict the time remaining until harvest of the fruiting body.

[0009] S5: Continue monitoring the growth rate of the sub-entity at the next monitoring time point, and verify whether there is any abnormal growth in the sub-entity. If there is an abnormality, issue an early warning signal. If no abnormality is found, return to step S3.

[0010] Secondly, the present invention also provides an indoor cultivation system for edible fungi, comprising: a morphological feature acquisition module: during the fruiting stage, morphological feature data of the fruiting bodies are acquired at the current monitoring time point, wherein the morphological feature data includes cap diameter, cap height and cap expansion.

[0011] Growth rate analysis module: compares the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point, and analyzes to obtain the growth rate of the sub-entity at the current monitoring time point.

[0012] Maturity Judgment Module: Based on the growth rate at the current monitoring time point, it initially determines whether the fruiting body growth has stopped. If growth has not stopped, the harvest time prediction module is executed. If growth has stopped, the maturity standard is used to determine whether the fruiting body is mature. If mature, a harvest reminder is triggered. If immature, the abnormal monitoring and early warning module is executed.

[0013] Harvest time prediction module: Based on the growth rate trend curve of the fruiting body over time and the growth rate at the current monitoring time point, predict the time remaining until harvest of the fruiting body.

[0014] Anomaly monitoring and early warning module: Continue to monitor the growth rate of the sub-entity at the next monitoring time point, verify whether there is any growth anomaly in the sub-entity, and issue an early warning signal if an anomaly is found; otherwise, return to the maturity judgment module.

[0015] Thirdly, the present invention also provides a storage medium storing one or more programs, which can be executed by one or more processors to implement the steps in the indoor cultivation method for edible fungi described in the present invention.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention sets differentiated monitoring frequencies according to different growth and development stages of the fruiting stage, strengthens monitoring during the rapid growth period and reduces the frequency during the slow growth period, which saves resources and ensures the continuity and accuracy of key data.

[0017] 2. This invention dynamically determines whether the fruiting bodies have entered the mature stage by real-time monitoring of multi-dimensional morphological characteristics such as cap diameter, cap height, and cap expansion, combined with growth rate analysis, thus avoiding harvesting too early or too late and ensuring the quality of mushrooms.

[0018] 3. This invention introduces a growth arrest judgment and secondary verification mechanism, which effectively distinguishes between normal growth fluctuations and abnormal growth arrest, and timely detects problems such as pests, diseases or environmental stress, facilitating early intervention.

[0019] 4. This invention provides forward-looking guidance for cultivation management based on the historical growth rate trend curve over time and the prediction of the time until harvest based on the current growth rate, thereby improving the predictability and scientific nature of management. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0022] Figure 2 This is a system module connection diagram of the present invention.

[0023] Figure 3 This is a flowchart illustrating the overall workflow of the present invention. Detailed Implementation

[0024] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figure 1 and Figure 3 As shown, the first aspect of the present invention provides a method for indoor cultivation of edible fungi, comprising the following steps: S1: during the fruiting stage, morphological characteristic data of the fruiting bodies are collected at the current monitoring time point, wherein the morphological characteristic data includes cap diameter, cap height and cap expansion.

[0026] For example, the specific analysis process of step S1 is as follows: S11, dividing the monitoring period: based on the growth and development law of the fruiting body, the fruiting stage is divided into multiple consecutive growth and development periods, and a corresponding monitoring frequency is set for each growth and development period.

[0027] Based on the monitoring frequency, multiple monitoring time points are set in each growth and development stage according to the principle of equal time intervals to determine all monitoring time points in the fruiting stage.

[0028] S12. Image Acquisition and Preprocessing: At the current monitoring time point, images of the sub-entities are acquired from multiple angles using miniature image sensors, and the acquired images are preprocessed.

[0029] S13. Cap diameter analysis: Based on the overhead image of the fruiting body, the edge contour line of the cap is extracted, and the cap diameter is determined by analyzing the edge contour line.

[0030] S14. Cap height extraction: Based on the side-view image of the fruiting body, measure the vertical distance from the highest point of the cap to the cultivation substrate, and determine this distance as the cap height.

[0031] S15. Calculation of cap spread: Based on the side image of the fruiting body, calculate the curvature of each point on the edge contour line of the cap, and select the point with the maximum curvature. Draw a tangent line to the edge contour line through the point with the maximum curvature, calculate the angle between the tangent line and the center line of the stipe, and determine the cap spread as the cap spread.

[0032] It should be noted that monitoring frequencies are set for each growth and development stage based on its growth characteristics. During critical periods of rapid growth, such as the rapid expansion of the cap, a higher monitoring frequency is set, i.e., the monitoring interval is shortened, to intensively capture rapidly changing morphological characteristics. During periods of slower or relatively stable growth, a lower monitoring frequency is set, i.e., the monitoring interval is extended, thereby optimizing monitoring resources while ensuring data validity.

[0033] It should be noted that this invention dynamically sets the monitoring frequency based on the growth and development stages, achieving a balance between monitoring efficiency and data accuracy. This avoids unnecessary frequent monitoring during slow growth periods, saving resources; at the same time, it ensures that sufficiently dense data points are obtained during critical growth periods, thereby more accurately depicting the growth rate trend and providing a reliable data foundation for accurately predicting the time until harvest and timely identifying growth anomalies.

[0034] In one specific embodiment, the fruiting stage is divided into the primordia stage, the rapid growth stage, and the maturity stage. The monitoring frequency of the rapid growth stage is higher than that of the primordia stage and the maturity stage. For example, the rapid growth stage is monitored once every 12 hours, while the primordia stage and the maturity stage are monitored once a day. This makes the workload moderate and can capture the inflection point and important nodes of the growth rate.

[0035] It should be noted that the image preprocessing includes, but is not limited to, color space conversion, contrast enhancement, noise reduction, binarization, morphological operations, etc., in order to improve the quality of the acquired images and thus improve the accuracy of morphological feature data extraction based on the images.

[0036] It should be noted that when the fruiting body is in an incurved and immature state, the edge of the cap tightens inward and wraps around the stem, with a low degree of expansion, and the cap expansion angle is acute. When the fruiting body is at its optimal harvesting period, the edge of the cap stops incurling, and the expansion angle is moderate, with the cap expansion angle being approximately right. When the fruiting body is overripe, the edge of the cap turns outward, and the expansion angle is high, with the cap expansion angle being obtuse.

[0037] In this embodiment, the present invention sets differentiated monitoring frequencies according to different growth and development stages of the fruiting process, strengthens monitoring during the rapid growth period and reduces the frequency during the slow growth period, which saves resources and ensures the continuity and accuracy of key data.

[0038] For example, the specific analysis process of step S13 is as follows: S131: Extract the edge contour line of the cap from the overhead image of the fruiting body.

[0039] S132: Calculate the curvature of each point on the edge contour line. If the curvature of each point is equal, the area enclosed by the edge contour line is determined to be a standard circle, and the diameter of the standard circle is directly determined as the diameter of the cap. Otherwise, proceed to step S133.

[0040] S133: Identify the curvature abrupt change points on the edge contour line, and divide the area enclosed by the edge contour line into multiple sector regions based on the curvature abrupt change points. Calculate the area of ​​each sector region using the sector area formula, and sum the areas of all sector regions to obtain the total area of ​​the area enclosed by the edge contour line.

[0041] S134: Calculate a circle with the same total area as obtained in step S133, denote this circle as the equivalent circle, and finally determine the diameter of the equivalent circle as the cap diameter.

[0042] It should be noted that this invention uses an area-based equivalent diameter to determine the cap diameter, which has a clear physical meaning, stable measurement, and a high correlation with biomass. Cap shapes are often irregular and not perfectly circular; directly measuring the diameter in a single direction can introduce significant errors due to different angle selections. The area-based method avoids this directional problem. Its core idea is to calculate the diameter of an ideal circle equal to the actual projected area of ​​the cap, thus providing a robust scalar value that comprehensively reflects the overall size of the cap. This method makes the results unaffected by cap shape, orientation, or slight irregularities, providing a more objective and consistent characterization of cap growth, and is particularly suitable for monitoring changes in growth rate over time series.

[0043] S2: Compare the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point to analyze and obtain the growth rate of the sub-entity at the current monitoring time point.

[0044] For example, the specific analysis process of step S2 is as follows: retrieve the morphological feature data of the sub-entity at the previous monitoring time point adjacent to the current monitoring time point from the preset mushroom monitoring dataset.

[0045] The morphological feature data at the current monitoring time point are compared one by one with the morphological feature data at the adjacent previous monitoring time point to calculate the relative increments of cap diameter, cap height and cap expansion.

[0046] The growth rate of the sub-entity at the current monitoring time point is obtained by performing a weighted fusion analysis on the relative increments.

[0047] It should be noted that the weights of the relative increments of cap diameter, cap height, and cap expansion can be preset based on cultivation experience or determined based on historical experimental data. Specifically, this includes: collecting historical data on the correlation between various morphological characteristics and maturity of different edible fungi varieties during the fruiting stage; calculating the correlation coefficients between cap diameter, cap height, and cap expansion and the fruiting body maturation process; using regression analysis to evaluate the contribution of each characteristic to the growth rate assessment; and finally, after normalization, converting the contribution into corresponding weights, with the sum of all weights being 1.

[0048] It should be noted that cap diameter, cap height, and cap expansion comprehensively reflect key growth dimensions of the fruiting body in three-dimensional space, such as expansion, elongation, and morphological development. These are direct indicators for measuring its biomass accumulation and developmental stage. Calculating the growth rate based on cap diameter, cap height, and cap expansion quantifies multi-dimensional morphological changes into a unified growth rate index, thereby enabling objective and accurate tracking of the fruiting body's growth process. This provides a quantitative basis for subsequent judgments of growth stagnation, prediction of time to harvest, and identification of growth anomalies.

[0049] S3: Based on the growth rate at the current monitoring time point, make a preliminary judgment on whether the growth of the fruiting body has stopped. If the growth has not stopped, proceed to step S4. If the growth has stopped, determine whether the fruiting body is mature according to the preset maturity standard. If it is mature, trigger a harvest reminder. If it is not mature, proceed to step S5.

[0050] For example, the specific analysis process of step S3 is as follows: S31: Determine the lower limit of the growth rate based on the growth rate of the fruiting body at the time of historical harvesting.

[0051] S32: Compare the growth rate at the current monitoring time point with the lower limit of the growth rate.

[0052] If the current growth rate is greater than the lower limit, it is determined that the growth of the sub-entity has not stopped, and the process proceeds to step S4.

[0053] If the current growth rate is less than or equal to the lower limit value, the growth of the sub-entity is determined to be stagnant, and step S33 is executed.

[0054] S33: Based on the morphological characteristics data of the fruiting bodies at the time of historical harvest, the numerical ranges of cap diameter, cap height and cap expansion at the time of harvest are statistically analyzed, and these numerical ranges are established as the maturity standard.

[0055] S34: Determine whether the cap diameter, cap height, and cap expansion of the mushroom collected at the current monitoring time point are all within their respective maturity ranges.

[0056] If both conditions are met, the fruiting body is determined to be mature, and a harvesting reminder is triggered.

[0057] If at least one of them is not within the corresponding maturity range, the sub-entity is determined to be immature, and the process proceeds to step S5.

[0058] It should be noted that the lower limit of the growth rate is a value close to zero.

[0059] In another specific embodiment, the minimum or average growth rate at the time of historical harvesting of the fruiting body is determined as the lower limit of the growth rate characterizing growth stagnation.

[0060] It should be noted that a single growth rate indicator can only reflect changes in growth momentum and cannot absolutely characterize the developmental stage; while the numerical range of morphological characteristics can directly define maturity, relying solely on static size judgment cannot distinguish between slow growth and true maturity. This invention, based on a dual judgment mechanism of growth rate and morphological characteristic numerical range, first uses growth rate to initially screen out fruiting bodies that may be entering a growth stagnation period, and then precisely verifies their morphological characteristics. This effectively avoids misjudging maturity due to temporary growth retardation caused by environmental fluctuations, or premature harvesting of fruiting bodies that only meet size standards but are still in a rapid growth phase, significantly improving the accuracy and reliability of maturity judgment.

[0061] In this embodiment, the present invention dynamically determines whether the fruiting body has entered the mature stage by real-time monitoring of multi-dimensional morphological characteristics such as cap diameter, cap height and cap expansion, combined with growth rate analysis, so as to avoid harvesting too early or too late and ensure the quality of mushrooms.

[0062] S4: Based on the growth rate trend curve of the fruiting body over time and the growth rate at the current monitoring time point, predict the time remaining until harvest of the fruiting body.

[0063] For example, the specific analysis process of step S4 is as follows: S41: Based on historical mushroom monitoring data, draw a trend curve of the fruiting body growth rate changing over time.

[0064] S42: On the trend curve, identify the time point corresponding to the growth rate of the sub-entity at the historical harvest time, and record this time point as the predicted harvest time node.

[0065] S43: Draw a horizontal line through the growth rate value at the current monitoring time point, obtain the intersection points of the horizontal line and the trend curve, and count the number of intersection points.

[0066] If there is only one intersection point, then the time point corresponding to that intersection point is recorded as the current time node, and step S46 is executed.

[0067] If there are two intersection points, then proceed to step S44.

[0068] S44: From the mushroom growth monitoring dataset, retrieve the morphological feature data of the sub-entities at the current monitoring time point and previous monitoring time points, calculate their corresponding growth rates, and plot the trend curve segment of the sub-entity growth rate over time based on these data.

[0069] S45: Determine whether the trend curve segment has a peak.

[0070] If no peak exists, the intersection point that appears earlier in step S43 is determined to be a true intersection point.

[0071] If a peak exists, the intersection point that appears later in step S43 is determined to be a true intersection point.

[0072] Record the time point corresponding to the true intersection point as the current time node.

[0073] S46: Calculate the interval between the current time node and the predicted harvest time node, and determine the interval as the time until harvest for the sub-entity.

[0074] It should be noted that during the fruiting stage, the growth and development of the fruiting body follows the pattern of first accelerating growth to the peak rate, then slowing down until maturity and stagnation. Therefore, the trend curve of its growth rate over time exhibits a bell-shaped curve characteristic.

[0075] It should be noted that the growth rate of fruiting bodies exhibits a regular bell-shaped trend over time. The position of the current growth rate on the historical trend curve objectively reflects its current growth stage. By dynamically comparing real-time monitoring data with historical growth models, the limitations of a single growth rate indicator can be effectively overcome. Even if the growth curve has multiple interpretations, local trend analysis can accurately determine the true growth node, thereby enabling scientific and accurate prediction of harvest time and providing reliable decision support for intelligent cultivation management.

[0076] It should be noted that the time remaining until harvest for the sub-entities is dynamically updated based on the real-time monitoring results of the growth rate.

[0077] In this embodiment, the present invention provides forward-looking guidance for cultivation management based on the historical growth rate trend curve over time and the current growth rate to predict the time until harvest, thereby improving the predictability and scientific nature of management.

[0078] For example, the specific analysis process of step S41 is as follows: based on historical fruiting monitoring data, obtain the growth rate of each fruiting body at different monitoring time points during the fruiting stage.

[0079] A coordinate system is established with the monitoring time point as the horizontal axis and the growth rate as the vertical axis. The different monitoring time points and their corresponding growth rate values ​​are marked as data points in the coordinate system.

[0080] Based on all data points, a trend curve reflecting the change of the growth rate of the fruiting body over time was plotted using a mathematical model fitting method.

[0081] It should be noted that historical monitoring data on the growth rate of multiple batches of fruiting bodies during the fruiting stage all follow their inherent growth and development patterns. This universal growth pattern can be extracted through data fitting. By integrating discrete, single-point historical growth data into continuous and intuitive trend curves, a quantitative relationship model between growth rate and time is established. This provides a reliable benchmark for accurately determining the current growth stage and predicting subsequent development processes, effectively improving the predictability and scientific nature of cultivation management.

[0082] S5: Continue monitoring the growth rate of the sub-entity at the next monitoring time point, and verify whether there is any abnormal growth in the sub-entity. If there is an abnormality, issue an early warning signal. If no abnormality is found, return to step S3.

[0083] For example, the specific analysis process of step S5 is as follows: continue to monitor the growth rate of the sub-entity at the next monitoring time point.

[0084] The growth rate at the next monitoring time point is compared with the lower limit of the growth rate.

[0085] If the growth rate is less than or equal to the lower limit, the growth of the fruiting body is determined to be abnormal, and an early warning signal is issued.

[0086] If the growth rate is greater than the lower limit, the growth of the fruiting body is determined to be normal, and the process returns to step S3.

[0087] It should be noted that when fruiting body growth stagnates but fails to reach maturity, there may be potential risks such as environmental stress, pests and diseases, or physiological disorders. By introducing the growth rate at the next monitoring time point for secondary verification, it is possible to effectively distinguish between normal growth fluctuations before maturity and growth stagnation caused by abnormal factors. This mechanism avoids false alarms that may be caused by a single judgment, significantly improves the reliability of abnormal diagnosis, and provides a key decision-making window for timely intervention and reduction of cultivation losses.

[0088] In this embodiment, the present invention introduces a growth arrest judgment and secondary verification mechanism to effectively distinguish between normal growth fluctuations and abnormal growth arrest, and to promptly detect problems such as pests, diseases or environmental stress, so as to facilitate early intervention.

[0089] In this embodiment, the present invention transforms complex morphological judgments into intuitive growth rate and maturity indicators through automated image acquisition and data processing, enabling even home users without professional experience to achieve precise management.

[0090] In this embodiment, the present invention avoids yield loss and quality decline caused by improper harvesting by timely harvesting and abnormal early warning, thereby improving the fruiting efficiency and commercial value of each batch of mushrooms.

[0091] See Figure 2 As shown, a second aspect of the present invention also provides an indoor cultivation system for edible fungi, including a morphological feature acquisition module, a growth rate analysis module, a maturity judgment module, a harvest time prediction module, and an anomaly monitoring and early warning module.

[0092] The growth rate analysis module is connected to the morphological feature acquisition module and the maturity judgment module, respectively. The maturity judgment module is connected to the harvest time prediction module and the abnormal monitoring and early warning module, respectively.

[0093] The morphological feature acquisition module is used to collect morphological feature data of the fruiting bodies at the current monitoring time point during the fruiting stage. The morphological feature data includes cap diameter, cap height, and cap expansion.

[0094] The growth rate analysis module is used to compare the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point to analyze and obtain the growth rate of the sub-entity at the current monitoring time point.

[0095] The maturity judgment module is used to preliminarily determine whether the growth of the fruiting body has stopped based on the growth rate at the current monitoring time point. If the growth has not stopped, the harvest time prediction module is executed. If the growth has stopped, the maturity of the fruiting body is determined according to the preset maturity standard. If it is mature, a harvest reminder is triggered. If it is not mature, the abnormal monitoring and early warning module is executed.

[0096] The harvesting time prediction module is used to predict the time remaining until harvest of the fruiting body based on the trend curve of the fruiting body growth rate over time and the growth rate at the current monitoring time point.

[0097] The anomaly monitoring and early warning module is used to continue monitoring the growth rate of the sub-entity at the next monitoring time point, verify whether there is any growth anomaly in the sub-entity, and issue an early warning signal if an anomaly is found; otherwise, it returns to the maturity judgment module.

[0098] Thirdly, the present invention also provides a storage medium storing one or more programs, which can be executed by one or more processors to implement the steps in the indoor cultivation method for edible fungi described in the present invention.

[0099] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0101] Those skilled in the art will recognize that the modules 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. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for indoor cultivation of edible fungi, characterized in that, include: S1: During the fruiting stage, collect morphological feature data of the fruiting bodies at the current monitoring time point. The morphological feature data includes cap diameter, cap height and cap expansion. S2: Compare the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point, and analyze to obtain the growth rate of the sub-entity at the current monitoring time point; S3: Based on the growth rate at the current monitoring time point, make a preliminary judgment on whether the growth of the fruiting body has stopped. If the growth has not stopped, proceed to step S4. If the growth has stopped, determine whether the fruiting body is mature according to the preset maturity standard. If it is mature, trigger a harvest reminder. If it is not mature, proceed to step S5. S4: Based on the growth rate of the fruiting body over time and the growth rate at the current monitoring time point, predict the time remaining until harvest for the fruiting body. S5: Continue monitoring the growth rate of the sub-entity at the next monitoring time point, and verify whether there is any abnormal growth in the sub-entity. If there is an abnormality, issue an early warning signal. If no abnormality is found, return to step S3.

2. The method for indoor cultivation of edible fungi according to claim 1, characterized in that: The specific analysis process of step S1 is as follows: S11. Divide the monitoring cycle: Based on the growth and development pattern of the fruiting body, divide the fruiting stage into multiple consecutive growth and development periods, and set a corresponding monitoring frequency for each growth and development period. Based on the monitoring frequency, multiple monitoring time points are set in each growth and development stage according to the principle of equal time intervals to determine all monitoring time points in the fruiting stage. S12. Image Acquisition and Preprocessing: At the current monitoring time point, images of the sub-entities are acquired from multiple angles using miniature image sensors, and the acquired images are preprocessed. S13. Cap diameter analysis: Based on the overhead image of the fruiting body, the edge contour line of the cap is extracted, and the cap diameter is determined by analyzing the edge contour line. S14. Cap height extraction: Based on the side-view image of the fruiting body, measure the vertical distance from the highest point of the cap to the cultivation substrate, and determine this distance as the cap height; S15. Calculation of cap spread: Based on the side image of the fruiting body, calculate the curvature of each point on the edge contour line of the cap, and select the point with the maximum curvature. Draw a tangent line to the edge contour line through the point with the maximum curvature, calculate the angle between the tangent line and the center line of the stipe, and determine the cap spread as the cap spread.

3. The method for indoor cultivation of edible fungi according to claim 2, characterized in that: The specific analysis process of step S13 is as follows: S131: Extract the edge contour of the cap from an overhead image of the fruiting body; S132: Calculate the curvature of each point on the edge contour line. If the curvature of each point is equal, the area enclosed by the edge contour line is determined to be a standard circle, and the diameter of the standard circle is directly determined as the diameter of the cap. Otherwise, proceed to step S133. S133: Identify the curvature abrupt change points on the edge contour line, and divide the area enclosed by the edge contour line into multiple sector regions based on the curvature abrupt change points. Calculate the area of ​​each sector region using the sector area formula, and sum the areas of all sector regions to obtain the total area of ​​the area enclosed by the edge contour line. S134: Calculate a circle with the same total area as obtained in step S133, denote this circle as the equivalent circle, and finally determine the diameter of the equivalent circle as the cap diameter.

4. The method for indoor cultivation of edible fungi according to claim 1, characterized in that: The specific analysis process of step S2 is as follows: Retrieve morphological feature data of the sub-entity at the previous monitoring time point adjacent to the current monitoring time point from the preset mushroom growth monitoring dataset; The morphological feature data at the current monitoring time point are compared with the morphological feature data at the adjacent previous monitoring time point one by one, and the relative increments of cap diameter, cap height and cap expansion are calculated. The growth rate of the sub-entity at the current monitoring time point is obtained by performing a weighted fusion analysis on the relative increments.

5. The method for indoor cultivation of edible fungi according to claim 1, characterized in that: The specific analysis process of step S3 is as follows: S31: Determine the lower limit of the growth rate based on the growth rate of the fruiting body at the time of historical harvesting; S32: Compare the growth rate at the current monitoring time point with the lower limit of the growth rate; If the current growth rate is greater than the lower limit, it is determined that the growth of the sub-entity has not stopped, and the process proceeds to step S4. If the current growth rate is less than or equal to the lower limit value, the growth of the sub-entity is determined to be stagnant, and step S33 is executed; S33: Based on the morphological characteristics data of the fruiting bodies at the time of historical harvesting, the numerical ranges of cap diameter, cap height and cap expansion at the time of harvesting are statistically analyzed, and this numerical range is established as the maturity standard. S34: Determine whether the cap diameter, cap height, and cap expansion of the mushroom collected at the current monitoring time point are all within their respective maturity ranges. If both conditions are met, the fruiting body is determined to be mature, and a harvesting reminder is triggered. If at least one of them is not within the corresponding maturity range, the sub-entity is determined to be immature, and the process proceeds to step S5.

6. The method for indoor cultivation of edible fungi according to claim 1, characterized in that: The specific analysis process of step S4 is as follows: S41: Based on historical fruiting monitoring data, plot the trend curve of fruiting body growth rate over time; S42: On the trend curve, identify the time point corresponding to the growth rate of the sub-entity during historical harvesting, and record this time point as the predicted harvesting time node; S43: Draw a horizontal line through the growth rate value at the current monitoring time point, obtain the intersection points of the horizontal line and the trend curve, and count the number of intersection points; If there is only one intersection point, then the time point corresponding to that intersection point is recorded as the current time node, and step S46 is executed. If there are two intersection points, proceed to step S44; S44: From the mushroom growth monitoring dataset, retrieve the morphological feature data of the sub-entities at the current monitoring time point and previous monitoring time points, calculate their corresponding growth rates, and draw trend curve segments of the sub-entity growth rate over time based on these data. S45: Determine whether the trend curve segment has a peak; If no peak exists, the intersection point that appears earlier in step S43 is determined to be a true intersection point; If a peak exists, the intersection point that appears later in step S43 is determined to be a true intersection point; The time point corresponding to the true intersection is recorded as the current time node; S46: Calculate the interval between the current time node and the predicted harvest time node, and determine the interval as the time until harvest for the sub-entity.

7. The method for indoor cultivation of edible fungi according to claim 6, characterized in that: The specific analysis process of step S41 is as follows: Based on historical fruiting monitoring data, the growth rate of each fruiting body at different monitoring time points during the fruiting stage was obtained. A coordinate system is established with the monitoring time point as the horizontal axis and the growth rate as the vertical axis. The different monitoring time points and their corresponding growth rate values ​​are marked as data points in the coordinate system. Based on all data points, a trend curve reflecting the change of the growth rate of the fruiting body over time was plotted using a mathematical model fitting method.

8. The method for indoor cultivation of edible fungi according to claim 5, characterized in that: The specific analysis process of step S5 is as follows: Continue monitoring the growth rate of the sub-entities at the next monitoring time point; Compare the growth rate at the next monitoring time point with the lower limit of the growth rate; If the growth rate is less than or equal to the lower limit value, the growth of the fruiting body is determined to be abnormal, and an early warning signal is issued. If the growth rate is greater than the lower limit, the growth of the fruiting body is determined to be normal, and the process returns to step S3.

9. An indoor cultivation system for edible fungi, characterized in that, include: Morphological feature acquisition module: During the fruiting stage, morphological feature data of the fruiting bodies are collected at the current monitoring time point. The morphological feature data includes cap diameter, cap height and cap expansion. Growth rate analysis module: compares the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point, and analyzes to obtain the growth rate of the sub-entity at the current monitoring time point; Maturity Judgment Module: Based on the growth rate at the current monitoring time point, it preliminarily judges whether the growth of the fruiting body has stopped. If the growth has not stopped, the harvest time prediction module is executed. If the growth has stopped, the maturity standard is used to judge whether the fruiting body is mature. If it is mature, a harvest reminder is triggered. If it is not mature, the abnormal monitoring and early warning module is executed. Harvest time prediction module: Based on the growth rate trend curve of the fruiting body over time and the growth rate at the current monitoring time point, predict the time remaining until harvest of the fruiting body. Anomaly monitoring and early warning module: Continue to monitor the growth rate of the sub-entity at the next monitoring time point, verify whether there is any growth anomaly in the sub-entity, and issue an early warning signal if an anomaly is found; otherwise, return to the maturity judgment module.

10. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps in the indoor cultivation method for edible fungi as described in any one of claims 1-8.

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