An edible mushroom indoor cultivation method, system and storage medium

Abstract

The present application relates to the technical field of edible mushroom cultivation, and relates to an indoor cultivation method, system and storage medium for edible mushrooms. The method comprises: setting different monitoring frequencies according to different growth periods during the fruiting stage, collecting cap diameter, cap height and cap expansion degree morphological feature data of the fruiting body at the current monitoring time point; calculating the growth rate by comparing the morphological feature data of adjacent time points; preliminarily judging whether the growth is stagnant according to the growth rate, if it is stagnant, further judging whether it is mature according to the maturity standard and triggering the harvesting reminder, if it is not stagnant, predicting the time length to harvesting, if the growth is stagnant but not mature, continuing to monitor and identifying the abnormal growth through a secondary verification mechanism. The present application realizes accurate judgment of the harvesting time and early warning of abnormal growth through multi-dimensional morphological feature dynamic monitoring and intelligent analysis, effectively reduces the management difficulty of family users, and improves the yield and quality of mushrooms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of edible mushroom cultivation, and relates to an edible mushroom indoor cultivation method, system and storage medium. BACKGROUND

[0002] In the process of edible mushroom indoor cultivation, fine management in the fruiting stage has a decisive influence on the final yield and quality. In particular, the timing of harvesting directly relates to the commodity nature and edible value of the fruiting body. If harvested too early, the fruiting body has not fully developed, the cap has not unfolded, the flesh is thin, the yield is low, and the flavor and nutrition are not optimal. If harvested too late, the cap is completely flat or even upturned, the gill is softened, and spores are ejected in large quantities, resulting in decreased quality, poor taste, and easy rotting of the mushroom body. At the same time, spore release consumes nutrients in the bag and inhibits subsequent fruiting, causing overall yield reduction.

[0003] At present, family users or small-scale cultivation sites generally lack professional experience in fruiting period management and are difficult to accurately determine the growth stage and maturity of the fruiting body through visual observation, often relying on subjective experience or fixed time harvesting, which is prone to improper harvesting timing and cannot guarantee the quality and yield of the mushroom, restricting the promotion and benefit improvement of the family mushroom house. SUMMARY

[0004] In view of this, in order to solve the problems raised in the background art, an edible mushroom indoor cultivation method, system and storage medium are proposed.

[0005] The technical solution adopted by the present application to solve its technical problems is as follows: In a first aspect, the present application provides an edible mushroom indoor cultivation method, comprising the following steps: S1: in the fruiting stage, collecting morphological feature data of the fruiting body at the current monitoring time point, the morphological feature data including cap diameter, cap height and cap unfolding degree.

[0006] S2: comparing the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point, and analyzing to obtain the growth rate of the fruiting body at the current monitoring time point.

[0007] S3: preliminarily judging whether the growth of the fruiting body is stagnant according to the growth rate at the current monitoring time point, if not, proceeding to step S4, if stagnant, judging whether the fruiting body is mature according to the preset maturity standard, if mature, triggering a harvesting reminder, if not mature, proceeding to step S5.

[0008] S4: predicting the time length to harvesting of the fruiting body according to the growth rate trend curve of the fruiting body with time and the growth rate at the current monitoring time point.

[0009] S5: Continue to monitor the growth rate of the sub-entity at the next monitoring time point, verify whether the sub-entity has growth abnormalities, if there are abnormalities, issue a warning signal, if no abnormalities are found, return to step S3.

[0010] In a second aspect, the present application also provides an edible mushroom indoor cultivation system, comprising: a morphological feature acquisition module: at the fruiting stage, the morphological feature data of the sub-entity is collected at the current monitoring time point, and the morphological feature data includes cap diameter, cap height and cap expansion degree.

[0011] A growth rate analysis module: the morphological feature data at the current monitoring time point is compared with the morphological feature data at the adjacent last monitoring time point, and the growth rate of the sub-entity at the current monitoring time point is analyzed.

[0012] A maturity judgment module: according to the growth rate at the current monitoring time point, it is preliminarily judged whether the growth of the sub-entity is stagnant, if the growth is not stagnant, the harvesting time prediction module is executed, if the growth is stagnant, it is judged whether the sub-entity is mature according to the preset maturity standard, if it is mature, the harvesting reminder is triggered, if it is not mature, the abnormal monitoring warning module is executed.

[0013] A harvesting time prediction module: according to the growth rate trend curve of the sub-entity with time and the growth rate at the current monitoring time point, the time length from the sub-entity to the harvesting is predicted.

[0014] An abnormal monitoring warning module: continue to monitor the growth rate of the sub-entity at the next monitoring time point, verify whether the sub-entity has growth abnormalities, if there are abnormalities, issue a warning signal, if no abnormalities are found, return to the maturity judgment module.

[0015] In a third aspect, the present application also provides a storage medium, the storage medium stores one or more programs, the one or more programs can be executed by one or more processors to implement the steps in the edible mushroom indoor cultivation method of the present application.

[0016] Compared with the prior art, the beneficial effects of the present application are as follows: 1. The present application sets different monitoring frequencies according to different growth and development periods at the fruiting stage, strengthens monitoring in the rapid growth period, and reduces the frequency in the slow period, which not only saves resources, but also ensures the continuity and accuracy of key data.

[0017] 2. The present application dynamically judges whether the sub-entity enters the mature period by real-time monitoring of multi-dimensional morphological features such as cap diameter, cap height and cap expansion degree, combining with growth rate analysis, avoiding early or late harvesting, and protecting mushroom quality.

[0018] 3. The application introduces a growth stagnation judgment and secondary verification mechanism, effectively distinguishing between normal growth fluctuations and abnormal growth stagnation, and timely discovering problems such as pests and diseases or environmental stress, facilitating early intervention.

[0019] 4. The application is based on the historical growth rate trend curve combined with the current growth rate to predict the time to harvest, providing forward-looking guidance for cultivation management and improving the predictability and scientificity of management. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0021] Figure 1 The method flowchart of the application.

[0022] Figure 2 The system module connection diagram of the application.

[0023] Figure 3 The overall work flowchart of the application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the application will be described clearly and completely in the following with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0025] Please refer to Figure 1 and Figure 3 The first aspect of the application provides an indoor cultivation method of edible fungi, comprising the following steps: S1: in the fruiting stage, collecting morphological feature data of the fruiting body at the current monitoring time point, the morphological feature data including cap diameter, cap height and cap expansion degree.

[0026] Exemplarily, the specific analysis process of the step S1 is: S11, dividing the monitoring period: according to the growth and development law of the fruiting body, the fruiting stage is divided into multiple continuous 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 period according to the equal time interval principle, and all monitoring time points of the fruiting stage are determined.

[0028] S12, image acquisition and preprocessing: at the current monitoring time point, the images of the fruiting bodies are acquired from multiple angles by the miniature image sensor, 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, the vertical distance from the highest point of the cap to the cultivation substrate is measured, and the distance is determined as the cap height.

[0031] S15, cap expansion degree calculation: based on the side-view image of the fruiting body, the curvature of each point on the edge contour line of the cap is calculated, and the maximum curvature point is selected, and the tangent line of the edge contour line is drawn through the maximum curvature point, and the included angle between the tangent line and the cap stem center line is calculated, and the included angle is determined as the cap expansion degree.

[0032] It should be noted that according to the growth characteristics of different growth and development periods, the monitoring frequency is set for each growth and development period. In the key period of rapid growth such as the rapid expansion period of the cap, a higher monitoring frequency is set, that is, the monitoring time interval is shortened, so as to densely capture the rapidly changing morphological characteristics; in the period of slow growth or relative stability, a lower monitoring frequency is set, that is, the monitoring time interval is extended, so as to optimize the monitoring resources while ensuring the effectiveness of the data.

[0033] It should be noted that the present application dynamically sets the monitoring frequency based on the growth and development period, and balances the monitoring efficiency and data accuracy. Unnecessary frequent monitoring in the slow growth period is avoided, and resources are saved; at the same time, sufficient dense data points can be obtained in the key growth period, so as to more accurately depict the growth rate change trend, and provide a reliable data basis for accurately predicting the time to harvest and timely identifying growth abnormalities.

[0034] In one specific embodiment, the fruiting stage is divided into primordium period, rapid growth period and mature period, wherein the monitoring frequency of the rapid growth period is higher than that of the primordium period and the mature period, such as monitoring once every 12 hours in the rapid growth period, and monitoring once a day in the primordium period and the mature period, so as to make the workload moderate and 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, denoising, binarization, morphological operation, etc., so as to improve the quality of the acquired image, and then improve the accuracy of the morphological feature data extracted based on the image.

[0036] It should be noted that when the fruiting body is in the involuted immature state, the edge of the cap is inwardly tightened and wrapped around the stipe, and the opening degree is low, at this time the opening degree of the cap is an acute angle; when the fruiting body is in the best harvesting period, the edge of the cap stops involuting, and the opening degree is moderate, at this time the opening degree of the cap is approximately a right angle; when the fruiting body is in the over-ripe state, the edge of the cap is turned outward, and the opening degree is high, at this time the opening degree of the cap is an obtuse angle.

[0037] In the present embodiment, the present application sets different monitoring frequencies according to different growth periods of the fruiting stage, strengthens monitoring in the rapid growth period, and reduces the frequency in the slow period, which not only saves resources, but also ensures the continuity and accuracy of key data.

[0038] Exemplarily, the specific analysis process of the step S13 is: S131: extracting the edge contour line of the cap from the overhead image of the fruiting body.

[0039] S132: calculating the curvature of each point on the edge contour line, if the curvatures of each point are equal, determining that the area surrounded by the edge contour line is a standard circle, and directly determining the diameter of the standard circle as the cap diameter, otherwise, performing step S133.

[0040] S133: identifying the curvature mutation points on the edge contour line, and dividing the area surrounded by the edge contour line into a plurality of sector areas based on the curvature mutation points, calculating the area of each sector area by using the sector area formula, and accumulating the areas of all sector areas to obtain the total area of the area surrounded by the edge contour line.

[0041] S134: calculating a circle equal to the total area obtained in step S133, taking the circle as an equivalent circle, and finally determining the diameter of the equivalent circle as the cap diameter.

[0042] It should be noted that the present application selects the equivalent diameter based on the area to determine the cap diameter, which has clear physical meaning, stable measurement and high correlation with biomass. The cap shape is often irregular and not a standard circle, and if the diameter in a single direction is directly measured, significant errors will occur due to different angle selection. The area-based method avoids the directional problem, and its core idea is to calculate the diameter of an ideal circle equal to the actual projection area of the cap, so as to comprehensively reflect the overall size of the cap by a robust scalar value. This method makes the result not affected by the shape, orientation or slight irregularity of the cap, and can more objectively and consistently represent the growth condition of the cap, especially suitable for monitoring the change of growth rate in time series.

[0043] S2: comparing the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point, and analyzing to obtain the growth rate of the fruiting body at the current monitoring time point.

[0044] Illustratively, the specific analysis process of step S2 is: retrieving the morphological feature data of the subentity at the previous monitoring time point adjacent to the current monitoring time point from the preset mushrooming monitoring data set.

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

[0046] The relative increments are subjected to weighted fusion analysis to obtain the growth rate of the subentity at the current monitoring time point.

[0047] It should be noted that the weights of the relative increments of the cap diameter, cap height and cap expansion degree can be preset according to cultivation experience or determined based on historical test data. Specifically, historical data of the correlation between each morphological feature and maturity of different varieties of edible mushrooms in the mushrooming stage are collected, the correlation coefficients of the cap diameter, cap height and cap expansion degree with the maturity process of the subentity are calculated, the contribution of each feature in the growth rate evaluation is evaluated by regression analysis, and finally the contribution is converted into the corresponding weight through normalization processing, and the sum of the weights is 1.

[0048] It should be noted that the cap diameter, cap height and cap expansion degree can comprehensively reflect the key growth dimensions such as swelling, elongation and morphological expansion of the subentity in three-dimensional space, and are direct indicators for measuring the biomass accumulation and development stage. Based on the cap diameter, cap height and cap expansion degree, the growth rate is calculated, the multi-dimensional morphological change is quantified into a unified growth rate index, thereby realizing objective and accurate tracking of the growth process of the subentity, and providing quantitative decision basis for subsequent judgment of growth stagnation, prediction of time to harvest and identification of growth abnormalities.

[0049] S3: According to the growth rate at the current monitoring time point, it is preliminarily judged whether the growth of the subentity is stagnant, if the growth is not stagnant, then step S4 is entered, if the growth is stagnant, then it is judged according to the preset maturity standard whether the subentity is mature, if it is mature, then the harvesting reminder is triggered, if it is not mature, then step S5 is entered.

[0050] Illustratively, the specific analysis process of step S3 is: S31: determining the lower limit value of the growth rate according to the growth rate of the subentity at the time of historical harvesting.

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

[0052] If the current growth rate is greater than the lower limit value, it is determined that the growth of the subentity is not stagnant, and step S4 is entered.

[0053] If the current growth rate is less than or equal to the lower limit value, it is determined that the fruiting body growth is stagnant, and step S33 is performed.

[0054] S33: Based on the morphological feature data of the fruiting body at the historical harvesting time, the numerical value ranges of the cap diameter, cap height and cap expansion degree at the harvesting time are respectively counted, and the numerical value ranges are established as the maturity standard.

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

[0056] If all of them are within the mature range, it is determined that the fruiting body is mature, and a harvesting reminder is triggered.

[0057] If at least one of them is not within the corresponding mature range, it is determined that the fruiting body is not mature, and step S5 is entered.

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

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

[0060] It should be noted that a single growth rate indicator can only reflect the change of growth momentum, but cannot absolutely represent the development stage; and although the morphological feature numerical value range can directly define the mature state, if only relying on static size for judgment, it cannot distinguish between slow growth and true maturity. Based on the double judgment mechanism of growth rate and morphological feature numerical value range, the fruiting body that may enter the growth stagnation period is first screened out by the growth rate, and then the morphological features thereof are accurately reviewed, thereby effectively avoiding the problems of misjudging the short-term growth retardation as maturity due to environmental fluctuations, or prematurely harvesting due to only the size meeting the standard but still in the rapid growth period, and significantly improving the accuracy and reliability of the maturity judgment.

[0061] In this embodiment, the present application dynamically judges whether the fruiting body enters the mature period by real-time monitoring of multiple morphological features such as cap diameter, cap height and cap expansion degree, combined with growth rate analysis, to avoid premature or late harvesting and ensure mushroom quality.

[0062] S4: According to the growth rate trend curve of the fruiting body with time and the growth rate at the current monitoring time point, the time to harvesting of the fruiting body is predicted.

[0063] Exemplarily, the specific analysis process of step S4 is: S41: Based on the historical mushroom monitoring data, a trend curve of the growth rate of the fruiting body with time is drawn.

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

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

[0066] If the number of intersection points is one, the time point corresponding to the intersection point is recorded as the current time node, and step S46 is executed.

[0067] If the number of intersection points is two, step S44 is executed.

[0068] S44: From the mushroom monitoring data set, retrieve the morphological feature data of the sub-entity at the current monitoring time point and each monitoring time point before that, calculate the corresponding growth rate respectively, and draw a trend curve segment of the growth rate of the sub-entity with time based on these data.

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

[0070] If there is no peak, the intersection point appearing earlier in step S43 is determined as the true intersection point.

[0071] If there is a peak, the intersection point appearing later in step S43 is determined as the true intersection point.

[0072] The time point corresponding to the true intersection point is recorded as the current time node.

[0073] S46: Calculate the interval length between the current time node and the predicted harvesting time node, and determine the interval length as the sub-entity's harvesting time length.

[0074] It should be noted that during the mushroom stage, the growth and development of the sub-entity follows the rule of first accelerated growth to the rate peak, then decelerated growth until maturity stagnation, so its growth rate trend curve with time presents a bell-shaped curve characteristic.

[0075] It should be noted that the growth rate of the sub-entity with time presents a regular bell-shaped change trend, and the position of the current growth rate on the historical trend curve can objectively reflect its current growth stage. By dynamically comparing real-time monitoring data with historical growth model, the limitations of single growth rate index can be effectively overcome, even if the growth curve has multiple solutions, the real growth node can be accurately determined with the help of local trend analysis, so as to realize scientific and accurate prediction of harvesting time, and provide reliable decision support for intelligent cultivation management.

[0076] It should be noted that the harvesting time length of the sub-entity is dynamically updated according to the real-time monitoring result of the growth rate.

[0077] In the embodiment, the application predicts the time to harvest based on the historical growth rate trend curve combined with the current growth rate, provides forward-looking guidance for cultivation management, and improves the predictability and scientificity of management.

[0078] Illustratively, the specific analysis process of step S41 is as follows: according to the historical fruiting monitoring data, the growth rate of each fruiting body at different monitoring time points in the fruiting stage is obtained.

[0079] A coordinate system is established with the monitoring time points as the abscissa and the growth rate as the ordinate, and the different monitoring time points and their corresponding growth rate values are marked as data points in the coordinate system.

[0080] Based on all the data points, a trend curve reflecting the change rule of the growth rate of the fruiting body with time is drawn by using a mathematical model fitting method.

[0081] It should be noted that the growth rate monitoring data of historical multiple batches of fruiting bodies in the fruiting stage jointly follow the inherent growth and development rule, and this universal growth pattern can be extracted through data fitting. By integrating discrete and single-point historical growth data into continuous and intuitive trend curves, a quantitative relationship model between growth rate and time is established, which provides a reliable benchmark reference for accurately judging the current growth stage and predicting the subsequent development process, effectively improving the predictability and scientificity of cultivation management.

[0082] S5: Continue to monitor the growth rate of the fruiting body at the next monitoring time point, verify whether the fruiting body has growth abnormalities, if there are abnormalities, issue a warning signal, and if no abnormalities are found, return to step S3.

[0083] Illustratively, the specific analysis process of step S5 is as follows: continue to monitor the growth rate of the fruiting body at the next monitoring time point.

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

[0085] If the growth rate is less than or equal to the lower limit value, it is determined that the fruiting body has growth abnormalities, and a warning signal is issued.

[0086] If the growth rate is greater than the lower limit value, it is determined that the fruiting body grows normally, and returns to step S3.

[0087] It should be noted that when the fruit body growth stagnates but does not reach the maturity standard, there may be potential risks such as environmental stress, pests and diseases, or physiological obstacles. By introducing the growth rate of the next monitoring time point for secondary verification, it can effectively distinguish whether it is normal growth fluctuation before maturity or growth stagnation caused by abnormal factors. This mechanism avoids false positives caused by single judgment and significantly improves the reliability of abnormal diagnosis, providing a key decision window for timely intervention and reducing cultivation losses.

[0088] In this embodiment, the present application introduces a growth stagnation judgment and secondary verification mechanism, effectively distinguishes between normal growth fluctuations and abnormal growth stagnation, and timely discovers problems such as pests, diseases, or environmental stress, facilitating early intervention.

[0089] In this embodiment, the present application converts complex morphological judgment into intuitive growth rate and maturity indicators through automatic image acquisition and data processing, enabling even non-professional home users to achieve precise management.

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

[0091] Referring to Figure 2 The second aspect of the present application also provides an edible mushroom indoor cultivation system, which includes a morphological feature acquisition module, a growth rate analysis module, a maturity judgment module, a harvesting time prediction module, and an abnormal monitoring and early warning module.

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

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

[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, and analyze the growth rate of the fruit body at the current monitoring time point.

[0095] The maturity judgment module is used to preliminarily judge whether the fruit body growth has stagnated according to the growth rate at the current monitoring time point. If the growth has not stagnated, the harvesting time prediction module is executed, if the growth has stagnated, the maturity of the fruit body is judged according to the preset maturity standard, if the fruit body is mature, the harvesting reminder is triggered, if the fruit body 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 cultivating edible mushrooms in a room, characterized by, The method comprises the following steps: S1: in the fruiting stage, collecting morphological feature data of the fruiting body at the current monitoring time point, the morphological feature data comprising cap diameter, cap height and cap expansion degree; S2: comparing the morphological feature data at the current monitoring time point with the morphological feature data at the adjacent previous monitoring time point, and analyzing to obtain the growth rate of the fruiting body at the current monitoring time point; S3: preliminarily judging whether the growth of the fruiting body is stagnant according to the growth rate at the current monitoring time point, if the growth is not stagnant, turning to step S4, if the growth is stagnant, judging whether the fruiting body is mature according to the preset maturity standard, if the fruiting body is mature, triggering the harvesting reminder, if the fruiting body is not mature, turning to step S5; S4: predicting the time length from the current monitoring time point to the harvesting of the fruiting body according to the growth rate trend curve of the fruiting body with time and the growth rate at the current monitoring time point; S5: continuously monitoring the growth rate of the fruiting body at the next monitoring time point, verifying whether the fruiting body has growth abnormality, if the fruiting body has growth abnormality, issuing a warning signal, if no abnormality is found, returning to step S3; The specific analysis process of step S1 is as follows: S11, dividing monitoring period: according to the growth and development law of the fruiting body, dividing the fruiting stage into multiple continuous growth and development periods, and setting corresponding monitoring frequency for each growth and development period; based on the monitoring frequency, setting multiple monitoring time points in each growth and development period according to the equal time interval principle, and determining all monitoring time points of the fruiting stage; S12, image acquisition and preprocessing: at the current monitoring time point, acquiring images of the fruiting body from multiple angles through a miniature image sensor, and preprocessing the acquired images; S13, cap diameter analysis: based on the overhead image of the fruiting body, extracting the edge contour line of the cap, and determining the cap diameter by analyzing the edge contour line; S14, cap height extraction: based on the side image of the fruiting body, measuring the vertical distance from the highest point of the cap to the cultivation substrate, and determining the distance as the cap height; S15, cap expansion degree calculation: based on the side image of the fruiting body, calculating the curvature of each point on the edge contour line of the cap, and screening out the maximum curvature point, drawing a tangent line of the edge contour line through the maximum curvature point, calculating the included angle between the tangent line and the center line of the stipe, and determining the included angle as the cap expansion degree. The specific analysis process of step S4 is: S41: based on historical fruiting monitoring data, a trend curve of the growth rate of the fruiting body changing with time is drawn; S42: on the trend curve, the time point corresponding to the growth rate at the time of historical harvesting of the fruiting body is identified, and the time point is recorded as the predicted harvesting time node; S43: a horizontal line is drawn through the growth rate value at the current monitoring time point, the intersection of the horizontal line and the trend curve is obtained, and the number of intersection points is counted; if the number of intersection points is one, the time point corresponding to the intersection point is recorded as the current time node, and step S46 is executed; if the number of intersection points is two, step S44 is executed; S44: from the fruiting monitoring data set, the morphological feature data of the fruiting body at the current monitoring time point and each previous monitoring time point is called, the corresponding growth rate is calculated respectively, and a trend curve segment of the growth rate of the fruiting body changing with time is drawn based on the data; S45: it is judged whether the trend curve segment has a wave crest; if there is no wave crest, the intersection point appearing earlier in step S43 is determined as the true intersection point; if there is a wave crest, the intersection point appearing later in step S43 is determined as the true intersection point; the time point corresponding to the true intersection point is recorded as the current time node; S46: the interval length between the current time node and the predicted harvesting time node is calculated, and the interval length is determined as the time length to harvesting of the fruiting body.

2. The method for cultivating edible mushrooms in a room according to claim 1, wherein: The specific analysis process of step S13 is: S131: the edge contour line of the cap is extracted from the overhead image of the fruiting body; S132: the curvature of each point on the edge contour line is calculated, if the curvatures of each point are equal, it is determined that the area surrounded by the edge contour line is a standard circle, and the diameter of the standard circle is directly determined as the cap diameter, otherwise, step S133 is executed; S133: the curvature mutation point on the edge contour line is identified, and the area surrounded by the edge contour line is divided into multiple sector areas based on the curvature mutation point, the area of each sector area is calculated respectively by using the sector area formula, and the areas of all sector areas are accumulated to obtain the total area of the area surrounded by the edge contour line; S134: a circle equal to the total area obtained in step S133 is calculated, the circle is recorded as an equivalent circle, and the diameter of the equivalent circle is finally determined as the cap diameter.

3. The method for cultivating edible mushrooms in a room according to claim 1, wherein: The specific analysis process of step S2 is: morphological feature data of the fruiting body at the previous monitoring time point adjacent to the current monitoring time point is called from the preset fruiting monitoring data set; the morphological feature data at the current monitoring time point is compared with the morphological feature data at the adjacent previous monitoring time point one by one, and the relative increments of the cap diameter, the cap height and the cap expansion degree are calculated; the relative increments are analyzed by weighted fusion to obtain the growth rate of the fruiting body at the current monitoring time point.

4. The method for cultivating edible mushrooms in a room according to claim 1, wherein: The specific analysis process of step S3 is: S31: the lower limit value of the growth rate is determined according to the growth rate at the time of historical harvesting of the fruiting body; S32: the growth rate at the current monitoring time point is compared with the lower limit value of the growth rate; If the current growth rate is greater than the lower limit value, it is determined that the sub-entity growth is not stagnant, and step S4 is entered; If the current growth rate is less than or equal to the lower limit value, it is determined that the sub-entity growth is stagnant, and step S33 is executed; S33: Based on the morphological feature data of the sub-entity at the historical harvesting time, the numerical range of the cap diameter, cap height and cap expansion degree at the harvesting time is respectively counted, and the numerical range is established as the maturity standard; S34: Determine whether the cap diameter, cap height and cap expansion degree collected at the current monitoring time point are all within their respective mature ranges; If all are within, it is determined that the sub-entity is mature, and a harvesting reminder is triggered; If at least one of them is not within the corresponding mature range, it is determined that the sub-entity is not mature, and step S5 is entered.

5. The method of claim 4, wherein the method is characterized by: The specific analysis process of step S41 is: According to the historical mushroom monitoring data, the growth rate of each sub-entity at different monitoring time points in the mushroom stage is obtained; A coordinate system is established with the monitoring time point as the horizontal coordinate and the growth rate as the vertical coordinate, and the different monitoring time points and their corresponding growth rate values are marked as data points in the coordinate system; Based on all the data points, a trend curve reflecting the change rule of the growth rate of the sub-entity with time is drawn by using a mathematical model fitting method.

6. The method for cultivating edible mushrooms in a room according to claim 3, wherein: The specific analysis process of step S5 is: Continue to monitor the growth rate of the sub-entity at the next monitoring time point; Compare the growth rate at the next monitoring time point with the lower limit value of the growth rate; If the growth rate is less than or equal to the lower limit value, it is determined that the sub-entity grows abnormally, and a warning signal is issued; If the growth rate is greater than the lower limit value, it is determined that the sub-entity grows normally, and step S3 is returned.

7. A mushroom indoor cultivation system for performing the steps of a mushroom indoor cultivation method according to any one of claims 1-6, characterized in that, It includes: Morphological feature acquisition module: In the mushroom stage, the morphological feature data of the sub-entity is collected at the current monitoring time point, and the morphological feature data includes cap diameter, cap height and cap expansion degree; Growth rate analysis module: The morphological feature data at the current monitoring time point is compared with the morphological feature data at the adjacent last monitoring time point, and the growth rate of the sub-entity at the current monitoring time point is analyzed; Maturity judgment module: According to the growth rate at the current monitoring time point, it is preliminarily judged whether the sub-entity growth is stagnant, if the growth is not stagnant, the harvesting time prediction module is executed, if the growth is stagnant, whether the sub-entity is mature is judged according to the preset maturity standard, if it is mature, a harvesting reminder is triggered, if it is not mature, the abnormal monitoring warning module is executed; Harvesting time prediction module: According to the growth rate trend curve of the sub-entity with time and the growth rate at the current monitoring time point, the time length of the sub-entity from the harvesting time is predicted; Abnormal monitoring warning module: Continue to monitor the growth rate of the sub-entity at the next monitoring time point, verify whether the sub-entity has growth abnormality, if there is abnormality, a warning signal is issued, if no abnormality is found, return to the maturity judgment module.

8. A storage medium, characterized by 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 of edible fungi as claimed in any one of claims 1-6.

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