Construction method, device and storage medium of flowering period and quality prediction model of cut rose "Peach Snow Mountain"

Abstract

The present application relates to the field of computer technology, and the embodiments specifically disclose a method, device and storage medium for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain". The method comprises: collecting internal environmental data of a greenhouse where the cut rose "Peach Snow Mountain" is planted through a sensor with an Internet of Things; further subdividing and processing the internal environmental data collected by the sensor into multiple environmental factors to obtain input values; performing source-storage ratio processing on the cut rose "Peach Snow Mountain" to obtain output values; generating multiple data sets containing different input values and corresponding output values by independently and randomly sampling each output value and all the input values multiple times with replacement; obtaining input values, i.e., eigenvalues, that have a high correlation with each output value using a random forest algorithm; solving the relationship formula between each output value and its related eigenvalues through polynomial fitting, thereby obtaining the flowering period and quality prediction model.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device and storage medium for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain". Background Art

[0002] Traditionally, rose cultivation was primarily done outdoors. With the introduction of greenhouses and soilless cultivation, producers can achieve high-quality, high-yield, and environmentally friendly cut flower production through controlled environmental conditions, efficient energy use, and reusable and precisely regulated water and fertilizer irrigation. This, however, presents challenges for traditional flower growers in terms of updating their theoretical knowledge and transforming their production models. While many flower growers possess extensive experience in rose cultivation and management, this knowledge is often based on "feelings" or "estimates," lacking specific values or ranges. This knowledge, in turn, cannot be directly recognized and utilized by control systems and automated equipment under these new production models. However, the new model has been widely adopted, and cut flower production under this new production model has become an inevitable trend. Traditional flower growers must accept, adapt, and transform. As part of this new model, environmental control is a routine component of daily production, requiring adjustments based on market price fluctuations, planting plans, production density, and variety type. "Peach Snow Mountain" is one of the cut rose varieties that is most planted by producers and has the largest market circulation. Therefore, it is necessary to develop a usable computing model for "Peach Snow Mountain" that can use the real-time changing environmental data of the greenhouse to predict plant growth and development responses, flowering period and quality, and assist in environmental control settings. Summary of the Invention

[0003] The purpose of this application is to provide a method, device and storage medium for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain", so as to solve the problem in the prior art of needing to develop a usable computing model for "Peach Snow Mountain" that can use real-time greenhouse environmental data to predict plant growth and development responses, flowering period and quality, and assist in environmental control settings.

[0004] To achieve the above objectives, the present invention provides a method for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain", comprising the following steps: collecting internal environmental data of a greenhouse where the cut rose "Peach Snow Mountain" is grown using sensors with an Internet of Things;

[0005] Further subdividing and processing the internal environment data collected by the sensor into multiple environmental factors to obtain input values;

[0006] Perform source-sink ratio processing on the cut rose "Peach Snow Mountain" to obtain the output value;

[0007] Generate multiple data sets containing different input values and corresponding output values by independently and randomly sampling each of the output values and all the input values multiple times with replacement;

[0008] Use the random forest algorithm to obtain input values that have a high correlation with each output value, namely the eigenvalues;

[0009] The relationship formula between each output value and its related characteristic value is solved by polynomial fitting, thereby obtaining the flowering period and quality prediction model.

[0010] Optionally, the internal environment data acquired by the sensor is data information of the top of the vertical flower branch and the surface of the nutrient branch;

[0011] The internal environment data includes at least one of photosynthetically active radiation intensity, temperature, relative humidity and carbon dioxide concentration.

[0012] Optionally, the input value includes at least one of the daytime average temperature, nighttime average temperature, daytime average humidity, nighttime average humidity, photosynthetically active radiation intensity, carbon dioxide concentration, relative radiant product and radiant product.

[0013] Optionally, the source-sink ratio processing includes: obtaining 3 flower branches and 2 leaf area index nutrient branch and leaf curtains, 3 flower branches and 4 leaf area index nutrient branch and leaf curtains, 5 flower branches and 2 leaf area index nutrient branch and leaf curtains, and 5 flower branches and 4 leaf area index nutrient branch and leaf curtains, recording the dates of each growth period during the growth of the flower branches, including the germination period, bud period, and harvest period, measuring the appearance quality at the time of harvest, and obtaining the output value, which specifically includes: at least one of the flower branch length, flower branch thickness, flower bud height, leaf area, and stem, leaf, and flower dry weight.

[0014] Optionally, obtaining the eigenvalue using a random forest algorithm specifically includes:

[0015] The influence of the environmental factor is calculated by summing the reduction in impurity caused by the feature in the node splitting of each decision tree;

[0016] The permutation importance evaluation method was used to further verify the impact of features.

[0017] Optionally, the polynomial fitting is used to solve the relationship formula between each output value and its related characteristic value, thereby obtaining the flowering period and quality prediction model, specifically including:

[0018] Initialize the polynomial regression prediction model, assuming there is growth prediction features and merge all features into one vector, i.e. , , and at the same time set the initial parameter vector according to the number of features ,

[0019] ,

[0020] vector The initial values of all parameter values in are random values. The parameter vector is trained and adjusted according to the real data of the prediction target and feature value. The initial polynomial regression prediction model is:

[0021] ,

[0022] Where, Represents the predicted value, that is, the output value, including flower branch thickness, flower branch length, leaf area, bud height, stem weight, and flower dry weight;

[0023] represents the i-th eigenvalue, 1≤i≤n;

[0024] w i It represents the parameter corresponding to the i-th feature associated with the predicted target, and w0 is the intercept term.

[0025] Alternatively, if the degree of the fitted polynomial is too high, w can be refitted by ridge regression. i Regularization is performed, that is, w i After derivation, the result approaches 0.

[0026] Optionally, after obtaining the flowering period and quality prediction model, the method further includes:

[0027] Ensure that the cut rose "Peach Snow Mountain" in the greenhouse is planted within the same planting cycle with a spacing of 15 cm between plants, a row spacing of 15 cm, a substrate trough spacing of 80 cm, and a leaf area index of 2 or 4. Obtain greenhouse internal environmental data at regular time intervals, further segment and process them to obtain characteristic values, and substitute the processed characteristic values into the flowering period and quality prediction model to obtain daily predicted values, i.e., output values.

[0028] After obtaining the prediction results of flowering period and quality, it also includes:

[0029] Obtain characteristic values of each predicted value under corresponding source and storage conditions, and perform environmental control settings based on the characteristic values and actual production conditions.

[0030] To achieve the above-mentioned purpose, the present application also provides a device for constructing a flowering period and quality prediction model of the cut rose "Peach Snow Mountain", comprising: a memory; and

[0031] A processor connected to the memory, wherein the processor is configured to execute the steps of the method described above.

[0032] To achieve the above objectives, the present application also provides a computer storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a machine.

[0033] The embodiments of the present application have the following advantages:

[0034] The flowering period and quality prediction model constructed using this method enables repeated updates of predictions and efficient adjustment of relevant environmental factors. It also assists in developing environmental control plans based on specific market sales targets. Furthermore, by supplementing the model with common source-to-sink ratio scenarios for different varieties in production, the model can be applied in practice, combining production density and source-to-sink numbers from field surveys to achieve more realistic and accurate predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To more clearly illustrate the embodiments of this application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely illustrative, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0036] Figure 1 A flowchart of a method for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain" provided in at least one embodiment of the present application;

[0037] Figure 2 A logic block diagram of a model construction method for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain" provided in at least one embodiment of the present application;

[0038] Figure 3 A module block diagram of a device for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain" provided in at least one embodiment of the present application. DETAILED DESCRIPTION

[0039] The following specific embodiments illustrate the implementation of this application. Those familiar with the art can easily understand the other advantages and functions of this application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of this application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0040] It should be noted that, in the claims and description of this application, the steps may be executed substantially in parallel or in reverse order under appropriate circumstances, depending on the functions involved.

[0041] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0042] Currently, there are no modeling tools for rose greenhouse cultivation of cut flowers that are tailored to different varieties and take into account the interactions of multiple environmental factors. Farmers and large-scale production cooperatives or enterprises still rely on past experience to guess the possible flowering date. When encountering special weather changes, they cannot make timely adjustments to environmental control settings. Therefore, the estimated flowering period will vary greatly, and it is impossible to accurately predict the quality of cut flowers, resulting in losses in time, energy consumption and operations.

[0043] In addition to supplying fixed-order customers, supply auctions, import / export markets, and special holiday markets are also crucial. These markets often generate significant orders and revenue, but they are also volatile. Currently, producers lack effective strategies for coping with this fluctuating supply. Production plans tailored to supply and demand fluctuations often fall short of expectations due to inadequate environmental controls or untimely adjustments. Modeling can enable iterative updates of forecasts and efficient adjustments to relevant environmental impact factors. It can also assist in developing environmental control plans tailored to specific market sales targets.

[0044] In modern protected production, the rose plant is divided into two parts: the vegetative branches and the flowering branches. The vegetative branches, which do not flower but only produce leaves, continuously replenish photosynthetic assimilates for the production-generating flowering branches. Therefore, the vegetative branches are often considered the "source" of assimilates, while the flowering branches are the "sink" of assimilates. The relationship between the source and sink, or the source-sink ratio, influences the distribution of assimilates, ultimately affecting the yield and quality of the flowering branches. In daily plant management, pruning is a means of controlling this ratio, often through pruning and bud removal shortly after bud break, to maintain a stable distribution throughout the crop. Currently, algorithms for predicting flowering time and quality based on different source-sink ratio scenarios have not been developed, and surveys of source and sink numbers are not included in the development of production environmental control plans. This can lead to inaccuracies in predictions and discrepancies between planned and actual results. Incorporating common source-sink ratio scenarios for different varieties in production can enable models to be applied in practice, incorporating production density and source-sink numbers from field surveys, making predictions more realistic and accurate.

[0045] An embodiment of the present application provides a method for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain". The model algorithm also supports reverse deduction to formulate a focused environmental condition control plan. Figure 1 and Figure 2 , Figure 1This is a flowchart of a method for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain" provided in at least one embodiment of the present application. Figure 2 A logic block diagram of a model construction method for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain" provided in at least one embodiment of the present application, Figure 2 The plant data obtained in the

[0015] (including stem diameter, stem length, stem dry weight, leaf area, leaf dry weight, bud height, bud dry weight, and flowering period) is used to train a flowering period and quality prediction model. It should be understood that the method may further include additional blocks not shown and / or may omit the blocks shown, and the scope of the present application is not limited in this respect.

[0046] At step 101, data information at two important locations: the top of the vertical flower branch and the surface of the nutrient branch, is collected through sensors with the Internet of Things (for example, once every 30 minutes) in the greenhouse where the cut rose "Peach Snow Mountain" is planted.

[0047] In some embodiments, the internal environment data acquired by the sensor is data information of the top of the vertical flower branch and the surface of the nutrient branch.

[0048] In some embodiments, the internal environment data includes at least one of photosynthetically active radiation intensity (400-700 nm, μmol m-2 s-1), temperature (°C), relative humidity (%), and carbon dioxide concentration (ppm).

[0049] At step 102 , the internal environment data collected by the sensor is further subdivided and processed into multiple environmental factors to obtain input values.

[0050] In some embodiments, the input value includes at least one of daytime average temperature, nighttime average temperature, daytime average humidity, nighttime average humidity, photosynthetically active radiation intensity, carbon dioxide concentration, relative radiant product, and radiant product.

[0051] In step 103, several common source-sink ratio processings are performed on the cut rose "Peach Snow Mountain" to obtain an output value.

[0052] In some embodiments, the source-sink ratio processing includes: obtaining 3 flower branches and 2 LAI (leaf area index) nutrient branch and leaf curtains, 3 flower branches and 4 LAI (leaf area index) nutrient branch and leaf curtains, 5 flower branches and 2 LAI (leaf area index) nutrient branch and leaf curtains, and 5 flower branches and 4 LAI (leaf area index) nutrient branch and leaf curtains, recording the dates of each growth period during the growth of the flower branches, including the budding period, the budding period, and the harvesting period, and measuring the appearance quality at the time of harvest to obtain the output value, which specifically includes: at least one of the flower branch length, flower branch thickness, flower bud height, leaf area, and stem, leaf, and flower dry weight.

[0053] In step 104 , multiple data sets including different input values and corresponding output values are generated by performing independent and random multiple sampling (bootstrap sampling) on each of the output values and all the input values.

[0054] At step 105 , a random forest algorithm (see formula 1-3) is used to obtain input values, ie, feature values, that have a high correlation with each output value, ie, predicted value.

[0055] In some embodiments, obtaining the characteristic value using the random forest algorithm specifically includes:

[0056] The random forest algorithm quantifies the importance of features in two ways:

[0057] 1. Based on the contribution of split points to impurity

[0058] For classification problems, feature importance, that is, the influence of the environmental factor, is calculated by summing the reduction in impurity caused by the feature in each node split of the decision tree. Gini impurity ( The reduction formula of ) is as follows:

[0059] (1)

[0060] in, is the impurity of the parent node, and is the impurity of the left and right nodes after the split, 、 、 are the left and right child nodes and the total number of samples respectively.

[0061] feature The importance of is the sum of the impurity reduction of the split point based on this feature in all trees (see formula 2):

[0062] (2)

[0063] in, is the location of the split point, Represents all In the decision tree, A decision tree based on features The set of all nodes to be split, is the index of the decision tree, from 1 to Traverse all decision trees, is the total number of decision trees in the random forest.

[0064] 2. Permutation Importance

[0065] To further verify the impact of features, the permutation importance evaluation method is used. The core idea is to randomly disrupt the value of a single feature and observe the changes in model performance. The formula is as follows:

[0066] (3)

[0067] in, is the original performance of the model, is the performance of the model after the b-th permutation, and B is the number of permutation experiments.

[0068] At step 106 , a polynomial fitting is further performed to solve the relationship formula between each output value and its related characteristic value (see Formula 4), thereby obtaining the flowering period and quality prediction model.

[0069] Specifically include: initializing the polynomial regression prediction model, assuming that there is growth prediction features and merge all features into one vector, i.e. , , and at the same time set the initial parameter vector according to the number of features ,

[0070] ,

[0071] vector The initial values of all parameter values in are random values. The parameter vector is trained and adjusted according to the real data of the prediction target and feature value. The initial polynomial regression prediction model is:

[0072] (4)

[0073] Where, : Prediction value, that is, output value, including flower branch thickness (cm), flower branch length (cm), leaf area (cm 2 ), bud height (cm), stem weight (g), flower dry weight (g);

[0074] : the i-th eigenvalue, 1≤i≤n;

[0075] w i : The parameter corresponding to the i-th feature associated with the predicted target, w0 is the intercept term.

[0076] In some embodiments, if the degree of the fitting polynomial is too high, it is easy to overfit and produce abnormal fitting coefficients, so w is obtained by ridge regression. i Regularization is performed, that is, w iAfter derivation, the result approaches 0.

[0077] The specific approach is as follows: First, set a loss function for the algorithm and adjust the parameters according to the principle of minimizing the loss value to improve the degree of fit. The loss function is (see Formula 5); then add a penalty term to the original polynomial function to obtain the formula for ridge regression (see Formula 6)

[0078] (5)

[0079] (6)

[0080] Where, is the loss function, is the true value of growth, For about The minimum value of To calculate the scalar and The square of the difference.

[0081] Through fitting, the relationship formula between all input characteristic values (Table 1) and output prediction values (Table 2) under common source-to-repository ratio settings is obtained (see Formula 10-35).

[0082]

[0083] (7)

[0084]

[0085] (9)

[0086] Where, d represents the day d from the beginning of the experiment, RTE (T) It represents the physiological thermal effect when the average temperature is T, and the value range is 0-1; T b The lower limit temperature for development, below which development stops, for roses it is 8℃; T m The upper temperature limit of development, above which development stops, for roses it is 37℃; T ob The lower limit of the optimum temperature for growth is 22℃ for roses. ou The optimal upper temperature limit for development is 25°C for roses. The cumulative radiant heat product (TEP) is the daily sum of the relative radiant heat product (RTEP). That is, the cumulative radiant heat product on day d is the sum of the relative radiant heat product of that day and the previous day - 1.

[0087]

[0088] For all treatments:

[0089] (10)

[0090] (11)

[0091] Under the conditions of retaining 5 flowering branches and a vegetative branch and leaf curtain with a leaf area index of 2:

[0092] (12)

[0093] (13)

[0094] (14)

[0095] (15)

[0096] (16)

[0097] (17)

[0098] Under the condition of retaining 3 flowering branches and a vegetative branch and leaf curtain with a leaf area index of 2:

[0099] (18)

[0100] (19)

[0101] (20)

[0102] (twenty one)

[0103] (twenty two)

[0104] (twenty three)

[0105] Under the conditions of retaining 5 flowering branches and a vegetative branch and leaf curtain with a leaf area index of 4:

[0106] (twenty four)

[0107] (25)

[0108] (26)

[0109] (27)

[0110] (28)

[0111] (29)

[0112] Under the condition of retaining 3 flowering branches and a vegetative branch and leaf curtain with a leaf area index of 4:

[0113] (30)

[0114] (31)

[0115] (32)

[0116] (33)

[0117] (34)

[0118] (35)

[0119] In some embodiments, after obtaining the flowering period and quality prediction model, the method further includes:

[0120] Ensure that the cut rose "Peach Snow Mountain" in the greenhouse is planted within the same planting cycle with a plant spacing of 15 cm, a row spacing of 15 cm, a substrate trough spacing of 80 cm, and a leaf area index of 2 or 4. Obtain greenhouse internal environmental data at certain time intervals, and further segment and process them to obtain characteristic values. Substitute the processed characteristic values into the flowering period and quality prediction model to obtain daily prediction values, i.e., output values.

[0121] Specifically, in actual use, if the user wants to use the flowering period and quality prediction model to repeatedly update the flowering period and quality prediction, the following prerequisites and information are required before use:

[0122] (1) The environmental sensors in the planting area of this variety have a reasonable measurement range and take into account the height position of the flower branches and nutrient branches. They can collect all the internal environmental data of the greenhouse (temperature, relative humidity, photosynthetic active radiation intensity, carbon dioxide concentration) at a certain time interval of 15 minutes, 30 minutes or 1 hour, and can export the data through the client to facilitate data analysis and calculation;

[0123] (2) A five-point sampling method is used to conduct field surveys in the planting area of the variety to understand the actual field spacing between rows, the spacing between soilless cultivation substrates, the number of buds (retained buds) on each plant, and the date of germination. Ensure that within the same planting cycle, the spacing between rows is 15 cm, the spacing between rows is 15 cm, and the spacing between substrates is 80 cm. The number of buds must be consistent with the several scenarios that the model can provide for prediction: 3 (branches) or 5 (branches). The nutritional branches and leaves of the investigated plants need to be ( )Down( ) Photosynthetically active radiation intensity measurement, , and are brought into the Lambert-Beer formula (see formula 34) to calculate the leaf area index. Using the model for prediction requires ensuring that the leaf area index of the vegetative branches and foliage curtain is consistent with the scenario for which the model can provide predictions: If the number of flower branches or the leaf area index of vegetative branches and leaves do not meet the applicable conditions of the model (the source-sink conditions do not meet), pruning is required to control them;

[0124] (36)

[0125] (3) Starting from the germination day, the internal environment data is exported every day, and the internal environment data from 0:00 to 24:00 on the same day are further subdivided and processed according to the corresponding source library conditions and the characteristic value requirements involved in the content to be predicted, such as calculating the daytime average value and the nighttime maximum value;

[0126] Users can substitute the processed feature values into the flowering period and quality prediction model under the corresponding source and database conditions to obtain daily predicted values, i.e., output values. Furthermore, users can also use the daily input values to input the flowering period and quality prediction model to obtain the corresponding output values, thereby updating the output values obtained the previous day.

[0127] In some embodiments, after obtaining the prediction results of flowering period and quality, the method further includes:

[0128] Obtain characteristic values of each predicted value under corresponding source and storage conditions, and perform environmental control settings based on the characteristic values and actual production conditions.

[0129] Specifically, if auxiliary environmental control settings are required, you need to:

[0130] a. Clarify the specific values of the flowering period and each output value;

[0131] b. The planting area for this variety must be equipped with environmental sensors with a reasonable measurement range and that take into account the height of flowering and vegetative branches. These sensors can collect all environmental data (temperature, relative humidity, photosynthetically active radiation intensity, and carbon dioxide concentration) at regular intervals of 15 minutes, 30 minutes, or one hour. These sensors can also be exported through the client for convenient data analysis and calculations.

[0132] c. Conduct a field survey using a five-point sampling method within the planting area of this variety to understand the actual field spacing between rows, spacing between soilless cultivation substrates, the number of buds (retained buds) on each plant, and the date of budding. Ensure that within the same planting cycle, the spacing between plants is 15 cm, the spacing between rows is 15 cm, and the spacing between substrates is 80 cm. The number of buds must be consistent with the several scenarios that the model can predict: 3 (branches) or 5 (branches). The nutrient branches and leaves of the surveyed plants must be ( )Down( ) Photosynthetically active radiation intensity measurement, , and are brought into the Lambert-Beer formula (see formula 34) to calculate the leaf area index. Using the model for prediction requires ensuring that the leaf area index of the vegetative branches and foliage curtain is consistent with the scenario for which the model can provide predictions: If the number of flower branches or the leaf area index of vegetative branches and leaves do not meet the applicable conditions of the model (the source-sink conditions do not meet), pruning is required to control them;

[0133] The characteristic values of each predicted value under the corresponding source and storage conditions, namely the environmental factors and setting values, are solved through formulas, and then the environmental control settings can be made based on the actual production conditions.

[0134] The final fit, that is, the prediction accuracy, is determined by the mean squared error (MSE) between the predicted value and the measured value. The formula is:

[0135] (37)

[0136] in, is the observed value, is the predicted value, and n is the number of predictions. The smaller the MSE, the better the prediction effect.

[0137] By comparing with the measured values of the validation group, the MSE of the prediction results under various source-to-sink ratio conditions is shown in Table 3.

[0138]

[0139] Table 3:

[0140]

[0141] Figure 3 A block diagram of a device for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain" provided in at least one embodiment of the present application. The device includes:

[0142] Memory 201; and a processor 202 connected to the memory 201, the processor 202 being configured to: collect internal environmental data of the greenhouse where the cut rose "Peach Snow Mountain" is grown through sensors with the Internet of Things;

[0143] Further subdividing and processing the internal environment data collected by the sensor into multiple environmental factors to obtain input values;

[0144] Perform source-sink ratio processing on the cut rose "Peach Snow Mountain" to obtain the output value;

[0145] Generate multiple data sets containing different input values and corresponding output values by independently and randomly sampling each of the output values and all the input values multiple times with replacement;

[0146] Use the random forest algorithm to obtain input values that have a high correlation with each output value, namely the eigenvalues;

[0147] The relationship formula between each output value and its related characteristic value is solved by polynomial fitting, thereby obtaining the flowering period and quality prediction model.

[0148] In some embodiments, the processor 202 is further configured to: the internal environment data acquired by the sensor is data information of the top of the vertical flower branch and the surface of the nutrient branch;

[0149] The internal environment data includes at least one of photosynthetically active radiation intensity, temperature, relative humidity and carbon dioxide concentration.

[0150] In some embodiments, the processor 202 is further configured such that the input value includes at least one of daytime average temperature, nighttime average temperature, daytime average humidity, nighttime average humidity, photosynthetically active radiation intensity, carbon dioxide concentration, relative radiant product, and radiant product.

[0151] In some embodiments, the processor 202 is further configured to: the source-storage ratio processing includes: obtaining 3 flower branches and 2 leaf area index nutrient branch and leaf curtains, 3 flower branches and 4 leaf area index nutrient branch and leaf curtains, 5 flower branches and 2 leaf area index nutrient branch and leaf curtains, and 5 flower branches and 4 leaf area index nutrient branch and leaf curtains, recording the dates of each growth period during the growth of the flower branches, including the germination period, bud period, and harvest period, measuring the appearance quality at the time of harvest, and obtaining the output value, which specifically includes: at least one of the flower branch length, flower branch thickness, flower bud height, leaf area, and stem, leaf, and flower dry weight.

[0152] In some embodiments, the processor 202 is further configured to: obtain the feature value by using the random forest algorithm, specifically including:

[0153] The influence of the environmental factor is calculated by summing the reduction in impurity caused by the feature in the node splitting of each decision tree;

[0154] The permutation importance evaluation method was used to further verify the impact of features.

[0155] In some embodiments, the processor 202 is further configured to: solve the relationship formula between each output value and its related characteristic value by polynomial fitting to obtain the flowering period and quality prediction model, specifically including:

[0156] Initialize the polynomial regression prediction model, assuming there is growth prediction features and merge all features into one vector, i.e. , , and at the same time set the initial parameter vector according to the number of features ,

[0157] ,

[0158] vector The initial values of all parameter values in are random values. The parameter vector is trained and adjusted according to the real data of the prediction target and feature value. The initial polynomial regression prediction model is:

[0159] ,

[0160] Where, Represents the predicted value, that is, the output value, including flower branch thickness, flower branch length, leaf area, bud height, stem weight, and flower dry weight;

[0161] represents the i-th eigenvalue, 1≤i≤n;

[0162] w i It represents the parameter corresponding to the i-th feature associated with the predicted target, and w0 is the intercept term.

[0163] In some embodiments, the processor 202 is further configured to: if the degree of the fitting polynomial is too high, perform ridge regression on w i Perform regularization processing, that is, w i After derivation, the result approaches 0.

[0164] In some embodiments, the processor 202 is further configured to: after obtaining the flowering period and quality prediction model, further include:

[0165] Ensure that the cut rose "Peach Snow Mountain" in the greenhouse is planted within the same planting cycle with a spacing of 15 cm between plants, a row spacing of 15 cm, a substrate trough spacing of 80 cm, and a leaf area index of 2 or 4. Obtain greenhouse internal environmental data at regular time intervals, further segment and process them to obtain characteristic values, and substitute the processed characteristic values into the flowering period and quality prediction model to obtain daily predicted values, i.e., output values.

[0166] After obtaining the prediction results of flowering period and quality, it also includes:

[0167] Obtain characteristic values of each predicted value under corresponding source and storage conditions, and perform environmental control settings based on the characteristic values and actual production conditions.

[0168] The specific implementation method is referred to the aforementioned method embodiment and will not be repeated here.

[0169] The present application may be a method, apparatus, system and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present application.

[0170] A computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure within a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0171] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0172] The computer program instructions used to perform the operations of the present application can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The computer-readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, the state information of the computer-readable program instructions is used to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), so that the electronic circuit can execute the computer-readable program instructions, thereby implementing various aspects of the present application.

[0173] Various aspects of the present application are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0174] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0175] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0176] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a special hardware-based system that performs the function or action of the specification, or can be implemented by a combination of special hardware and computer instructions.

[0177] Note that, unless otherwise explicitly stated, all features disclosed in this specification (including any accompanying claims, abstracts, and drawings) may be replaced by alternative features that achieve the same, equivalent, or similar purposes. Therefore, unless explicitly stated otherwise, each feature disclosed is merely an example of a group of equivalent or similar features. Where used, further, preferably, further, and more preferably are simply the beginning of another embodiment based on the previous embodiment, and the content following further, preferably, further, or more preferably is combined with the previous embodiment as a complete construction of another embodiment. Several further, preferably, further, or more preferably settings following the same embodiment can be arbitrarily combined to form another embodiment.

[0178] Although the present application has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications or improvements may be made based on the present application. Therefore, such modifications or improvements, which do not depart from the spirit of the present application, are within the scope of protection claimed in the present application.

Claims

1. A method for constructing a flowering period and quality prediction model for cut rose "Peach Snow Mountain", characterized in that: The following steps are involved: IoT-enabled sensors are used to collect environmental data from the greenhouse where the cut rose "Peach Snow Mountain" is grown. Further subdividing and processing the internal environment data collected by the sensor into multiple environmental factors to obtain input values; The source-storage ratio of the cut rose "Peach Snow Mountain" is processed to obtain the output value. The source-sink ratio processing includes: obtaining 3 flower branches and 2 leaf area index vegetative branch and leaf curtains, 3 flower branches and 4 leaf area index vegetative branch and leaf curtains, 5 flower branches and 2 leaf area index vegetative branch and leaf curtains, and 5 flower branches and 4 leaf area index vegetative branch and leaf curtains, recording the dates of each growth period during the growth of the flower branches, including the budding period, the budding period, and the harvest period, measuring the appearance quality at the time of harvest, and obtaining the output value, the output value specifically including: at least one of the flower branch length, flower branch thickness, flower bud height, leaf area, and stem, leaf, and flower dry weight; Generate multiple data sets containing different input values and corresponding output values by independently and randomly sampling each of the output values and all the input values multiple times with replacement; Use the random forest algorithm to obtain input values that have a high correlation with each output value, namely the eigenvalues; The relationship formula between each output value and its related characteristic value is solved by polynomial fitting, thereby obtaining the flowering period and quality prediction model. Specifically include: Initialize the polynomial regression prediction model, assuming there is growth prediction features and merge all features into one vector, i.e. , , and at the same time set the initial parameter vector according to the number of features , ， vector The initial values of all parameter values in are random values. The parameter vector is trained and adjusted according to the real data of the prediction target and feature value. The initial polynomial regression prediction model is: ， Where, Represents the predicted value, that is, the output value, including flower branch thickness, flower branch length, leaf area, bud height, stem weight, and flower dry weight; represents the i-th eigenvalue, 1≤i≤n; w i It represents the parameter corresponding to the i-th feature associated with the predicted target, and w0 is the intercept term.

2. the construction method of cut flower rose " peach snow mountain " florescence and quality prediction model according to claim 1, is characterized in that, The internal environment data acquired by the sensor is data information of the top of the vertical flower branch and the surface of the nutrient branch; The internal environment data includes at least one of photosynthetically active radiation intensity, temperature, relative humidity and carbon dioxide concentration.

3. the construction method of cut flower rose " peach snow mountain " florescence and quality prediction model according to claim 1, is characterized in that, The input value includes at least one of the daytime average temperature, nighttime average temperature, daytime average humidity, nighttime average humidity, photosynthetically active radiation intensity, carbon dioxide concentration, relative radiant product and radiant product.

4. the construction method of cut flower rose " peach snow mountain " florescence and quality prediction model according to claim 1, is characterized in that, The method of obtaining the characteristic value by using the random forest algorithm specifically includes: The influence of the environmental factor is calculated by summing the reduction in impurity caused by the feature in the node splitting of each decision tree; The permutation importance evaluation method was used to further verify the impact of features.

5. the construction method of cut flower rose " peach snow mountain " florescence and quality prediction model according to claim 1, is characterized in that, If the degree of the fitted polynomial is too high, then w is i Perform regularization processing, that is, w i After derivation, the result approaches 0.

6. the construction method of cut flower rose " peach snow mountain " florescence and quality prediction model according to claim 1, is characterized in that, After obtaining the flowering period and quality prediction model, the method further includes: Ensure that the cut rose "Peach Snow Mountain" in the greenhouse is planted within the same planting cycle with a spacing of 15 cm between plants, a row spacing of 15 cm, a spacing of 80 cm between substrate troughs, and a leaf area index of 2 or 4. Obtain greenhouse internal environmental data at regular time intervals, further segment and process them to obtain characteristic values, and substitute the processed characteristic values into the flowering period and quality prediction model to obtain daily predicted values, i.e., output values. After obtaining the prediction results of flowering period and quality, it also includes: Obtain characteristic values of each predicted value under corresponding source and storage conditions, and perform environmental control settings based on the characteristic values and actual production conditions.

7. A device for constructing a flowering period and quality prediction model for the cut rose "Peach Snow Mountain", characterized in that: include: Memory; as well as A processor connected to the memory, the processor being configured to perform the steps of the method according to any one of claims 1 to 6.

8. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a machine, the steps of the method according to any one of claims 1 to 6 are implemented.

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