CN116720633A - Method for optimizing edible fungus breeding growth parameters - Google Patents
Method for optimizing edible fungus breeding growth parameters Download PDFInfo
- Publication number
- CN116720633A CN116720633A CN202311011774.XA CN202311011774A CN116720633A CN 116720633 A CN116720633 A CN 116720633A CN 202311011774 A CN202311011774 A CN 202311011774A CN 116720633 A CN116720633 A CN 116720633A
- Authority
- CN
- China
- Prior art keywords
- growth
- stage
- colony
- data
- machine learning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000012010 growth Effects 0.000 title claims abstract description 325
- 241000233866 Fungi Species 0.000 title claims abstract description 193
- 238000009395 breeding Methods 0.000 title claims abstract description 63
- 230000001488 breeding effect Effects 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000012549 training Methods 0.000 claims abstract description 141
- 238000010801 machine learning Methods 0.000 claims abstract description 124
- 238000012360 testing method Methods 0.000 claims abstract description 46
- 235000016709 nutrition Nutrition 0.000 claims abstract description 31
- 230000035764 nutrition Effects 0.000 claims abstract description 31
- 230000008901 benefit Effects 0.000 claims abstract description 30
- 238000010521 absorption reaction Methods 0.000 claims abstract description 29
- 239000013598 vector Substances 0.000 claims description 36
- 230000035800 maturation Effects 0.000 claims description 17
- 230000003698 anagen phase Effects 0.000 claims description 16
- 230000015654 memory Effects 0.000 claims description 16
- 235000015816 nutrient absorption Nutrition 0.000 claims description 13
- 235000015097 nutrients Nutrition 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 10
- 235000006286 nutrient intake Nutrition 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 230000005757 colony formation Effects 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000028070 sporulation Effects 0.000 claims description 6
- 238000011081 inoculation Methods 0.000 claims description 5
- 238000013480 data collection Methods 0.000 claims description 4
- 239000002131 composite material Substances 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 230000006641 stabilisation Effects 0.000 claims description 3
- 238000011105 stabilization Methods 0.000 claims description 3
- 239000012531 culture fluid Substances 0.000 claims description 2
- 235000021049 nutrient content Nutrition 0.000 claims 2
- 238000005457 optimization Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 5
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000003062 neural network model Methods 0.000 description 4
- 230000004083 survival effect Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- SRBFZHDQGSBBOR-IOVATXLUSA-N D-xylopyranose Chemical compound O[C@@H]1COC(O)[C@H](O)[C@H]1O SRBFZHDQGSBBOR-IOVATXLUSA-N 0.000 description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 230000000977 initiatory effect Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 229910052757 nitrogen Inorganic materials 0.000 description 2
- 235000003170 nutritional factors Nutrition 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000004763 spore germination Effects 0.000 description 2
- OWEGMIWEEQEYGQ-UHFFFAOYSA-N 100676-05-9 Natural products OC1C(O)C(O)C(CO)OC1OCC1C(O)C(O)C(O)C(OC2C(OC(O)C(O)C2O)CO)O1 OWEGMIWEEQEYGQ-UHFFFAOYSA-N 0.000 description 1
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 description 1
- GUBGYTABKSRVRQ-PICCSMPSSA-N Maltose Natural products O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@@H]1O[C@@H]1[C@@H](CO)OC(O)[C@H](O)[C@H]1O GUBGYTABKSRVRQ-PICCSMPSSA-N 0.000 description 1
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 1
- -1 amino acid salts Chemical class 0.000 description 1
- 150000001413 amino acids Chemical class 0.000 description 1
- PYMYPHUHKUWMLA-UHFFFAOYSA-N arabinose Natural products OCC(O)C(O)C(O)C=O PYMYPHUHKUWMLA-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- SRBFZHDQGSBBOR-UHFFFAOYSA-N beta-D-Pyranose-Lyxose Natural products OC1COC(O)C(O)C1O SRBFZHDQGSBBOR-UHFFFAOYSA-N 0.000 description 1
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 description 1
- GUBGYTABKSRVRQ-QUYVBRFLSA-N beta-maltose Chemical compound OC[C@H]1O[C@H](O[C@H]2[C@H](O)[C@@H](O)[C@H](O)O[C@@H]2CO)[C@H](O)[C@@H](O)[C@@H]1O GUBGYTABKSRVRQ-QUYVBRFLSA-N 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
- 235000019634 flavors Nutrition 0.000 description 1
- 239000008103 glucose Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- WPBNNNQJVZRUHP-UHFFFAOYSA-L manganese(2+);methyl n-[[2-(methoxycarbonylcarbamothioylamino)phenyl]carbamothioyl]carbamate;n-[2-(sulfidocarbothioylamino)ethyl]carbamodithioate Chemical compound [Mn+2].[S-]C(=S)NCCNC([S-])=S.COC(=O)NC(=S)NC1=CC=CC=C1NC(=S)NC(=O)OC WPBNNNQJVZRUHP-UHFFFAOYSA-L 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000050 nutritive effect Effects 0.000 description 1
- 238000000053 physical method Methods 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000011573 trace mineral Substances 0.000 description 1
- 235000013619 trace mineral Nutrition 0.000 description 1
- 239000011701 zinc Substances 0.000 description 1
- 229910052725 zinc Inorganic materials 0.000 description 1
Landscapes
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a method for optimizing edible fungus breeding growth parameters, which relates to the technical field of edible fungus breeding growth parameter optimization, and comprises the steps of collecting speed training data and stage result training data of a test edible fungus colony, collecting nutrition absorption data of a complete cultivation stage, training and predicting a set of first machine learning models of the growth speed of the edible fungus colony in each growth stage, a set of second machine learning models of the stage result data of the edible fungus colony in each growth stage and a third machine learning model of the total nutrition consumption prediction, and analyzing the growth parameters of the edible fungus colony suitable for each growth stage of to-be-bred; the trial-error cost of edible fungus breeding is reduced, and the edible fungus breeding benefit and breeding efficiency are improved.
Description
Technical Field
The invention relates to the technical field of edible fungus breeding growth parameter optimization, in particular to a method for optimizing edible fungus breeding growth parameters.
Background
Edible fungi refer to fungus organisms which can be used as food materials or medicinal resources. They have rich nutritive value, special flavor and medicinal efficacy, so they are widely used in the fields of food, medicine and health care products. In the cultivation process of the edible fungi, the growth parameters are key factors influencing the growth and development of the edible fungi, and the growth and the yield of the edible fungi can be promoted by adjusting and optimizing the growth parameters, so that the suitability for setting the growth parameter values is important;
The existing edible fungus breeding method has the following defects:
lack of efficient quantitative guidance methods: traditional breeding methods mainly depend on experience and trial and error, and lack of scientific quantitative guidance means leads to low breeding efficiency;
the trial-and-error cost is high: the traditional breeding method requires a large amount of tests and time to adjust different growth parameters, and has the disadvantages of higher trial and error cost and low efficiency;
lack of intelligent optimization means: the existing breeding method generally depends on manual operation and experience, lacks intelligent optimization means, and cannot realize automatic and efficient parameter optimization;
the Chinese patent with the authority of the publication number CN113711843B discloses a system and a method for optimizing the growth parameters of edible fungi, wherein a sensor based on a Modbus communication protocol is an environment sensor, a camera is used for monitoring the growth condition of the edible fungi, and the sensor based on the Modbus communication protocol and the camera are connected with a computer through an Ethernet; an edible fungus growth model analysis system with image processing and machine learning functions is installed in a computer, the system can collect experimental records of a sensor, observe the growth situation of edible fungi through a camera, generate an edible fungus growth model, and find optimal edible fungus area and growth environment parameters in the model by using a fine segmentation algorithm; the method solves the problem that the quality of the edible fungi is optimized by optimizing the growth parameters, but the growth speed, the production yield and the total amount of nutrient elements required to be consumed in the growth process of the edible fungi are not considered, so that the production benefit problem is not considered;
Therefore, the invention provides a method for optimizing the breeding growth parameters of the edible fungi.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides the method for optimizing the edible fungus breeding growth parameters, which reduces the trial-and-error cost of edible fungus breeding and improves the edible fungus breeding benefit and breeding efficiency.
In order to achieve the above object, according to embodiment 1 of the present invention, a method for optimizing edible fungi breeding growth parameters is provided, comprising the following steps:
collecting a set of growth stages of edible fungus breeding;
in an experimental environment, collecting speed training data and stage result training data of the edible fungi in each growth stage;
collecting nutrition absorption data of each test edible fungus colony in the complete cultivation stage;
training a set of first machine learning models that predict growth rates of edible fungus colonies at various growth stages based on the rate training data;
training a set of second machine learning models that predict stage outcome data of the edible fungus colony at each growth stage based on the stage outcome training data;
training a third machine learning model that predicts total nutrient consumption based on the stage outcome data and the nutrient absorption data;
Analyzing growth parameters suitable for each growth stage of the edible fungus colony to be bred based on the first machine learning model set, the second machine learning model set and the third machine learning model;
wherein the growth stage set comprises growth stages which are undergone by edible fungus colonies from inoculation of a culture container to colony maturation; the growth stage set comprises a spore formation stage, a hypha growth stage, a colony formation stage and a colony maturation stage;
in the experimental environment, an experimenter observes different breeding processes and breeding results of each test edible fungus colony by actively controlling culture container parameters, culture fluid parameters and environmental parameters of each test edible fungus colony so as to collect speed training data of the test edible fungus colony in different growth stages and area training data of the test edible fungus colony in a colony maturation stage; the test edible fungus colony is an edible fungus colony bred by an experimenter in an experimental environment;
the speed training data comprise growth parameter characteristic data, colony initial characteristic data and growth speed of all the tested edible fungus colonies in the corresponding growth stages;
Wherein the growth parameter characteristic data comprises environmental parameter values which influence the growth speed of the edible fungus colony and the colony area of the mature stage in each growth stage of the edible fungus colony;
the colony initial characteristic data are all growth characteristic data of edible fungi at the beginning time of all growth phases of the edible fungi colony;
in the sporulation stage, the growth characteristic values include the number of spores inoculated by the experimenter into the culture vessel;
in the mycelium growth stage, the growth characteristic values include the number of spore germination in the inoculated spores at the end of the sporulation stage;
in the colony formation stage, the growth characteristic values include the density of the mycelium network at the end of the mycelium growth stage; the mycelium network density is counted by experimenters according to industry experience;
at the colony maturation stage, the growth characteristic values include the survival rate of the colonies at the end of the colony formation stage; the survival rate of the colony is obtained through statistics of experimenters;
the growth speed is the duration of time that the test edible fungus colony goes from the current growth stage to the next growth stage when providing the environment of corresponding growth parameter characteristic data for each test edible fungus colony in each growth stage;
The stage result training data comprise growth parameter characteristic data, colony initial characteristic data and stage result characteristic data of the edible fungus colony in a corresponding growth stage; the stage result characteristic data are colony initial characteristic data of the edible fungus colony in the next growth stage;
the stage result characteristic data of the colony maturation stage is the result value of the colony when the colony is stable; when the colony is stable, the area of the colony of the edible fungi is not increased any more, and the area of the colony can be obtained through computer vision technology or measurement of experimenters; the calculation formula of the result value is as follows:
when the colony is stable, testing the weight mark W, the area mark S and the result value F of the edible fungus colony; the outcome value f=a1×w+a2×s; wherein a1 and a2 are respectively preset proportionality coefficients;
the nutrient absorption data comprises the sum of consumption of various nutrient factors from the inoculation of spores to the culture container to the stabilization of the colonies for each test edible fungus colony;
the consumption of each nutrient factor is the difference between the nutrient element content when spores are inoculated into the culture container and the nutrient element content when colonies are stable in the culture container;
The method for training and predicting the first machine learning model of the growth speed of the edible fungus colony in each growth stage is as follows:
marking the number of the growth phase in the growth phase set as j;
for the j-th growth stage, combining the growth parameter characteristic data and the colony initial characteristic data in the speed training data into the form of characteristic vectors, wherein the characteristic vectors are used as the input of a first machine learning model, the first machine learning model takes the predicted growth speed of each group of characteristic vectors as the output, the growth speed corresponding to the characteristic vectors in the speed training data is used as a prediction target, and the sum of prediction errors of all the characteristic vectors is minimized as the training target; training the first machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain a first machine learning model for predicting the growth speed of the edible fungus colony in the j growth stage according to the growth parameter characteristic data and the colony initial characteristic data; the characteristic values in the characteristic vector comprise environment parameter values in growth parameter characteristic data and colony initial characteristic data; the first machine learning model is any one of a polynomial regression model or an SVM model;
The second machine learning model set for training and predicting the stage result data of the edible fungus colony in each growth stage is as follows:
for the j-th growth stage, combining each group of growth parameter feature data and colony initial feature data in the stage result training data into a feature vector form, wherein the feature vector is used as the input of a second machine learning model, the second machine learning model takes the predicted stage result feature data of each group of feature vectors as the output, takes the stage result feature data corresponding to the feature vector in the stage result training data as a prediction target, and takes the sum of prediction errors of all the feature vectors as a training target; training the second machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain a second machine learning model for outputting phase result characteristic data for predicting the j-th growth phase according to the growth parameter characteristic data and the colony initial characteristic data; the first machine learning model is any one of a polynomial regression model or an SVM model;
the way to train the third machine learning model that predicts total nutrient consumption is:
taking the result value of each test edible fungus colony as the input of a third machine learning model, wherein the third machine learning model takes the nutrition absorption data predicted for each test edible fungus colony as the output, takes the collected nutrition absorption data of each test edible fungus colony as a prediction target, and takes the sum of all prediction errors for predicting the nutrition absorption data as a training target; training the third machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain the third machine learning model which outputs predicted nutrition absorption data according to the result value; the first machine learning model is any one of a polynomial regression model or an SVM model;
Analyzing growth parameters suitable for each growth stage of the edible fungus colony to be bred comprises the following steps:
step P1: setting the number of inoculated spores for the edible fungus colonies to be cultivated in advance, and setting a group of initial growth parameters for each growth stage; marking the number of the growth parameter type as i, marking the value of the ith initial growth parameter of the jth growth stage as Rji, and presetting a step length bi for the ith growth parameter;
step P2: calculating comprehensive benefits; marking the composite benefit as Z;
step P3: updating each initial growth parameter Rji in a way of randomly increasing or decreasing the corresponding step bi, marking the updated initial growth parameters Rji as Rji1, recalculating the updated comprehensive benefit, and marking the updated comprehensive benefit as Z1;
step P4: calculating the gradient Tji of the ith initial growth parameter of the jth growth stage, wherein the calculation formula of the gradient Tji is as followsThe method comprises the steps of carrying out a first treatment on the surface of the Updating each updated initial growth parameter Rji1 to Rji1-bi×Tji, and recalculating the comprehensive benefits; original Rji1 was re-labeled Rji, Z1 was re-labeled Z, and was re-countedThe calculated comprehensive benefit is marked as Z1;
step P5: step P4 is executed circularly until the cycle times reach a preset cycle times threshold value; taking the ith initial growth parameter Rji1 of the jth growth stage as the value of the corresponding growth parameter of the edible fungus colony to be cultivated in the corresponding stage when the cycle is finished;
The way to calculate the comprehensive benefit is:
marking the initial growth parameters of the j-th growth stage as Cj;
taking initial growth parameters Cj of the jth growth stage and initial characteristic data of the colony as input of a second machine learning model corresponding to the jth growth stage to obtain predicted stage result characteristic data of the jth growth stage;
marking a result value corresponding to the stage result characteristic data of the colony maturation stage predicted by the edible fungus colony to be cultivated as Fp;
taking initial growth parameters Cj of a jth growth stage and colony initial characteristic data as input of a first machine learning model corresponding to the jth growth stage, obtaining a predicted growth rate of the jth growth stage, and marking the predicted growth rate of the jth growth stage as Vj;
inputting the result value Fp into a third machine learning model to obtain predicted nutrition absorption data; marking the predicted nutrient absorption data as X;
calculating a combined comprehensive benefit Z of the combination of the initial growth parameters; the calculation formula of the comprehensive benefit Z is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein b1 and b2 are preset scaling factors.
According to the embodiment 2 of the invention, an edible fungus breeding growth parameter optimizing system is provided, which comprises a data collecting module, a model training module and a growth parameter analyzing module; wherein, each module is connected by a wired mode;
The data collection module is mainly used for collecting a growing stage set of edible fungus breeding, collecting speed training data and stage result training data of the tested edible fungus colonies in each growing stage in an experimental environment, collecting nutrition absorption data of each tested edible fungus colony in a complete breeding stage, and sending the collected speed training data, stage result training data and nutrition absorption data to the model training module;
the model training module is mainly used for training a set of first machine learning models for predicting the growth speed of the edible fungus colony in each growth stage based on the speed training data; training a set of second machine learning models that predict stage outcome data of the edible fungus colony at each growth stage based on the stage outcome training data; training a third machine learning model for predicting total nutrient consumption based on the stage outcome data and the nutrient absorption data, and transmitting the first machine learning model, the second machine learning model and the third machine learning model to a growth parameter analysis module;
the growth parameter analysis module is mainly used for analyzing growth parameters suitable for all growth stages of edible fungus colonies to be bred based on the first machine learning model set, the second machine learning model set and the third machine learning model.
An electronic device according to embodiment 3 of the present invention includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the method for optimizing the edible fungus breeding growth parameters in the background of bill transaction by calling the computer program stored in the memory.
A computer-readable storage medium according to embodiment 4 of the present invention has stored thereon a computer program that is erasable; when the computer program runs on the computer equipment, the computer equipment is caused to execute the method for optimizing the edible fungi breeding growth parameters.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, a growth stage set of edible fungus breeding is collected in advance, speed training data and stage result training data of testing edible fungus colonies in each growth stage are collected in an experimental environment, nutrition absorption data of each testing edible fungus colony in a complete breeding stage are collected, a first machine learning model set for predicting the growth speed of the edible fungus colony in each growth stage is trained based on the speed training data, a second machine learning model set for predicting the stage result data of the edible fungus colony in each growth stage is trained based on the stage result training data, a third machine learning model for predicting the total nutrition consumption is trained based on the stage result data and the nutrition absorption data, the growth speed and the final result value of each growth stage are analyzed by dividing the edible fungus colony in the growth stage, and the total amount of nutrient elements required to reach the result value is analyzed based on the final result value, so that the growth parameters suitable for each growth stage of the edible fungus colony to be bred are analyzed based on the first machine learning model set, the second machine learning model set and the third machine learning model; by optimizing the intelligent parameters of the growth parameters of each growth stage of the edible fungus colony, the edible fungus breeding is quantitatively guided, the trial-and-error cost of the edible fungus breeding is reduced, and the edible fungus breeding benefit and the breeding efficiency are improved.
Drawings
FIG. 1 is a flow chart of a method for optimizing edible fungi breeding growth parameters in embodiment 1 of the invention;
FIG. 2 is a diagram showing the connection relationship between modules of the edible fungi breeding growth parameter optimizing system in the embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of an electronic device in embodiment 3 of the present invention;
fig. 4 is a schematic diagram of a computer-readable storage medium according to embodiment 4 of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, the method for optimizing the breeding growth parameters of the edible fungi comprises the following steps:
step one: collecting a set of growth stages of edible fungus breeding;
step two: in an experimental environment, collecting speed training data and stage result training data of the edible fungi in each growth stage;
collecting nutrition absorption data of each test edible fungus colony in the complete cultivation stage;
Step three: training a set of first machine learning models that predict growth rates of edible fungus colonies at various growth stages based on the rate training data; training a set of second machine learning models that predict stage outcome data of the edible fungus colony at each growth stage based on the stage outcome training data; training a third machine learning model that predicts total nutrient consumption based on the stage outcome data and the nutrient absorption data;
step four: analyzing growth parameters suitable for each growth stage of the edible fungus colony to be bred based on the first machine learning model set, the second machine learning model set and the third machine learning model;
wherein the growth stage set comprises growth stages which are undergone by edible fungus colonies from inoculation of a culture container to colony maturation; preferably, the set of growth phases includes, but is not limited to, a sporulation phase, a mycelium growth phase, a colony formation phase, a colony maturation phase, and the like;
in the experimental environment, an experimenter observes different breeding processes and breeding results of each test edible fungus colony by actively controlling the culture container parameters, the culture solution parameters and the environment parameters of each test edible fungus colony, so that speed training data of the test edible fungus colony in different growth stages and area training data of the test edible fungus colony in a colony maturation stage are accurately collected, and accuracy and controllability of training data collection are improved; the test edible fungus colony is an edible fungus colony bred by an experimenter in an experimental environment;
The speed training data comprise growth parameter characteristic data, colony initial characteristic data and growth speed of all the tested edible fungus colonies in the corresponding growth stages;
wherein the growth parameter characteristic data comprises environmental parameter values which influence the growth speed of the edible fungus colony and the colony area of the mature stage in each growth stage of the edible fungus colony; preferably, the environmental parameter values include, but are not limited to, temperature, humidity, illumination intensity, pH, etc.; it is understood that the temperature, humidity, illumination and pH can be measured in real time by corresponding sensors;
the colony initial characteristic data are all growth characteristic data of edible fungi at the beginning time of all growth phases of the edible fungi colony; it should be noted that, at each stage, the growth characteristic data is different;
in a preferred embodiment of the invention, the growth characteristic value comprises the number of spores inoculated by the experimenter into the culture vessel during the sporulation stage;
in the mycelium growth stage, the growth characteristic values include the number of spore germination in the inoculated spores at the end of the sporulation stage;
in the colony formation stage, the growth characteristic values include the density of the mycelium network at the end of the mycelium growth stage; the mycelium network density is counted by experimenters according to industry experience;
At the colony maturation stage, the growth characteristic values include the survival rate of the colonies at the end of the colony formation stage; the survival rate of the colony is obtained through statistics of experimenters;
the growth speed is the duration of time that the test edible fungus colony goes from the current growth stage to the next growth stage when providing the environment of corresponding growth parameter characteristic data for each test edible fungus colony in each growth stage;
it should be noted that, dividing the starting time of each stage according to the manual division of experimenters by virtue of industry experience or automatic division based on computer vision technology; the method based on the computer vision technology can be that pictures at the starting time of each growth stage are manually collected in advance, labels corresponding to the growth stages are manually marked on each picture, all the marked pictures are used as input of a CNN neural network model, the CNN neural network model takes the labels of the growth stages as output, the CNN neural network model is trained, and the growth stages of edible fungus colonies are automatically distinguished by using the CNN neural network model after training;
the stage result training data comprise growth parameter characteristic data, colony initial characteristic data and stage result characteristic data of the edible fungus colony in a corresponding growth stage; the stage result characteristic data are colony initial characteristic data of the edible fungus colony in the next growth stage; it can be understood that the initial characteristic data of the colony in the next growth stage is the cultivation result in the current growth stage;
The stage achievement characteristic data of the colony maturation stage is the achievement value of the colony when the colony is stable; when the colony is stable, the area of the colony of the edible fungi is not increased any more, and the area of the colony can be obtained through computer vision technology or measurement of experimenters; the calculation formula of the result value is as follows:
when the colony is stable, testing the weight mark W, the area mark S and the result value F of the edible fungus colony; the outcome value f=a1×w+a2×s; wherein a1 and a2 are respectively preset proportionality coefficients;
the nutrient absorption data comprises the sum of consumption of various nutrient factors from the inoculation of spores to the culture container to the stabilization of the colonies for each test edible fungus colony;
preferably, the nutritional factors include, but are not limited to, carbon sources, nitrogen sources, trace elements, and the like; such carbon sources include, but are not limited to, glucose, xylose, maltose, and the like; such nitrogen sources include, but are not limited to, amino acids, proteins, and amino acid salts; the microelements include, but are not limited to, iron, zinc, manganese, etc.; various nutritional factors can be measured by chemical and physical methods; the present invention is not described in detail herein;
the consumption of each nutrient factor is the difference between the nutrient element content when spores are inoculated into the culture container and the nutrient element content when colonies are stable in the culture container;
The method for training and predicting the first machine learning model of the growth speed of the edible fungus colony in each growth stage is as follows:
marking the number of the growth phase in the growth phase set as j;
for the j-th growth stage, combining the growth parameter characteristic data and the colony initial characteristic data in the speed training data into the form of characteristic vectors, wherein the characteristic vectors are used as the input of a first machine learning model, the first machine learning model takes the predicted growth speed of each group of characteristic vectors as the output, the growth speed corresponding to the characteristic vectors in the speed training data is used as a prediction target, and the sum of prediction errors of all the characteristic vectors is minimized as the training target; training the first machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain a first machine learning model for predicting the growth speed of the edible fungus colony in the j growth stage according to the growth parameter characteristic data and the colony initial characteristic data; the characteristic values in the characteristic vector comprise environment parameter values in growth parameter characteristic data and colony initial characteristic data; preferably, the first machine learning model is any one of a polynomial regression model or an SVM model;
The second machine learning model set for training and predicting the stage result data of the edible fungus colony in each growth stage is as follows:
for the j-th growth stage, combining each group of growth parameter feature data and colony initial feature data in the stage result training data into a feature vector form, wherein the feature vector is used as the input of a second machine learning model, the second machine learning model takes the predicted stage result feature data of each group of feature vectors as the output, takes the stage result feature data corresponding to the feature vector in the stage result training data as a prediction target, and takes the sum of prediction errors of all the feature vectors as a training target; training the second machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain a second machine learning model for outputting phase result characteristic data for predicting the j-th growth phase according to the growth parameter characteristic data and the colony initial characteristic data; preferably, the first machine learning model is any one of a polynomial regression model or an SVM model;
the way to train the third machine learning model that predicts total nutrient consumption is:
taking the result value of each test edible fungus colony as the input of a third machine learning model, wherein the third machine learning model takes the nutrition absorption data predicted for each test edible fungus colony as the output, takes the collected nutrition absorption data of each test edible fungus colony as a prediction target, and takes the sum of all prediction errors for predicting the nutrition absorption data as a training target; training the third machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain the third machine learning model which outputs predicted nutrition absorption data according to the result value; preferably, the first machine learning model is any one of a polynomial regression model or an SVM model;
It should be noted that, the calculation formula of the prediction error is:wherein->For the number of training feature data +.>For prediction error +.>Is->Predicted training tag data corresponding to the group training feature data, < ->Is->Actual training label data corresponding to the group training data;
for example, for a first machine learning model, the feature vector is training feature data, and the training tag data is growth speed; for the second machine learning model, the feature vector is training feature data, and the training label data is stage result training data; for the third machine learning model, the feature vector is the result value, and the training label data is nutrition absorption data;
analyzing growth parameters suitable for each growth stage of the edible fungus colony to be bred comprises the following steps:
step P1: setting the number of inoculated spores for the edible fungus colonies to be cultivated in advance, and setting a group of initial growth parameters for each growth stage; marking the number of the growth parameter type as i, marking the value of the ith initial growth parameter of the jth growth stage as Rji, and presetting a step length bi for the ith growth parameter;
step P2: calculating comprehensive benefits; marking the composite benefit as Z;
Step P3: updating each initial growth parameter Rji, preferably by randomly increasing or decreasing the corresponding step bi, marking the updated initial growth parameters Rji as Rji1, recalculating the updated comprehensive benefit, and marking the updated comprehensive benefit as Z1;
step P4: calculating the gradient Tji of the ith initial growth parameter of the jth growth stage, wherein the calculation formula of the gradient Tji is as followsThe method comprises the steps of carrying out a first treatment on the surface of the Updating each updated initial growth parameter Rji1 to Rji1-bi×Tji, and recalculating the comprehensive benefits; the original Rji1 is re-labeled Rji, Z1 is re-labeled Z, and the re-calculated combined benefit is labeled Z1;
step P5: step P4 is executed circularly until the cycle times reach a preset cycle times threshold value; taking the ith initial growth parameter Rji1 of the jth growth stage as the value of the corresponding growth parameter of the edible fungus colony to be cultivated in the corresponding stage when the cycle is finished;
the way to calculate the comprehensive benefit is:
marking the initial growth parameters of the j-th growth stage as Cj;
taking initial growth parameters Cj of the jth growth stage and initial characteristic data of the colony as input of a second machine learning model corresponding to the jth growth stage to obtain predicted stage result characteristic data of the jth growth stage; it will be appreciated that the colony initiation profile for the 1 st growth stage is the number of spores inoculated, and in the j (j > 1) following growth stage, the colony initiation profile is the predicted stage outcome profile for the j-1 th growth stage;
Marking a result value corresponding to the stage result characteristic data of the colony maturation stage predicted by the edible fungus colony to be cultivated as Fp;
taking initial growth parameters Cj of a jth growth stage and colony initial characteristic data as input of a first machine learning model corresponding to the jth growth stage, obtaining a predicted growth rate of the jth growth stage, and marking the predicted growth rate of the jth growth stage as Vj;
inputting the result value Fp into a third machine learning model to obtain predicted nutrition absorption data; marking the predicted nutrient absorption data as X;
calculating a combined comprehensive benefit Z of the combination of the initial growth parameters; the calculation formula of the comprehensive benefit Z is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein b1 and b2 are preset scaling factors.
Example 2
As shown in fig. 2, the edible fungus breeding growth parameter optimizing system comprises a data collecting module, a model training module and a growth parameter analyzing module; wherein, each module is connected by a wired mode;
the data collection module is mainly used for collecting a growing stage set of edible fungus breeding, collecting speed training data and stage result training data of the tested edible fungus colonies in each growing stage in an experimental environment, collecting nutrition absorption data of each tested edible fungus colony in a complete breeding stage, and sending the collected speed training data, stage result training data and nutrition absorption data to the model training module;
The model training module is mainly used for training a set of first machine learning models for predicting the growth speed of the edible fungus colony in each growth stage based on the speed training data; training a set of second machine learning models that predict stage outcome data of the edible fungus colony at each growth stage based on the stage outcome training data; training a third machine learning model for predicting total nutrient consumption based on the stage outcome data and the nutrient absorption data, and transmitting the first machine learning model, the second machine learning model and the third machine learning model to a growth parameter analysis module;
the growth parameter analysis module is mainly used for analyzing growth parameters suitable for all growth stages of edible fungus colonies to be bred based on the first machine learning model set, the second machine learning model set and the third machine learning model.
Example 3
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 3, there is also provided an electronic device 100 according to yet another aspect of the present application. The electronic device 100 may include one or more processors and one or more memories. Wherein the memory has stored therein computer readable code which, when executed by the one or more processors, performs a method of edible fungi breeding growth parameter optimization as described above.
The method or system according to embodiments of the application may also be implemented by means of the architecture of the electronic device shown in fig. 3. As shown in fig. 3, the electronic device 100 may include a bus 101, one or more CPUs 102, a Read Only Memory (ROM) 103, a Random Access Memory (RAM) 104, a communication port 105 connected to a network, an input/output component 106, a hard disk 107, and the like. The storage device in the electronic device 100, such as the ROM103 or the hard disk 107, may store a method for optimizing the edible fungi breeding growth parameters provided by the present application. The method for optimizing the edible fungi breeding growth parameters can comprise the following steps: step one: collecting a set of growth stages of edible fungus breeding; step two: in an experimental environment, collecting speed training data and stage result training data of the edible fungi in each growth stage; collecting nutrition absorption data of each test edible fungus colony in the complete cultivation stage; step three: training a set of first machine learning models that predict growth rates of edible fungus colonies at various growth stages based on the rate training data; training a set of second machine learning models that predict stage outcome data of the edible fungus colony at each growth stage based on the stage outcome training data; training a third machine learning model that predicts total nutrient consumption based on the stage outcome data and the nutrient absorption data; step four: analyzing growth parameters suitable for each growth stage of the edible fungus colony to be bred based on the first machine learning model set, the second machine learning model set and the third machine learning model;
Further, the electronic device 100 may also include a user interface 108. Of course, the architecture shown in fig. 3 is merely exemplary, and one or more components of the electronic device shown in fig. 3 may be omitted as may be practical in implementing different devices.
Example 4
FIG. 4 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present application. As shown in fig. 4, is a computer-readable storage medium 200 according to one embodiment of the application. The computer-readable storage medium 200 has stored thereon computer-readable instructions. When the computer readable instructions are executed by the processor, a method for optimizing edible fungi breeding growth parameters according to the embodiment of the application described with reference to the above drawings can be performed. Computer-readable storage medium 200 includes, but is not limited to, for example, volatile memory and/or nonvolatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
In addition, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions executable by a processor to perform instructions corresponding to the method steps provided by the present application, which when executed by a Central Processing Unit (CPU), perform the functions defined above in the method of the present application.
The methods and apparatus, devices of the present application may be implemented in numerous ways. For example, the methods and apparatus, devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present application may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present application. Thus, the present application also covers a recording medium storing a program for executing the method according to the present application.
In addition, in the foregoing technical solutions provided in the embodiments of the present application, parts consistent with implementation principles of corresponding technical solutions in the prior art are not described in detail, so that redundant descriptions are avoided.
The above preset parameters or preset thresholds are set by those skilled in the art according to actual conditions or are obtained by mass data simulation.
The above embodiments are only for illustrating the technical method of the present application and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present application may be modified or substituted without departing from the spirit and scope of the technical method of the present application.
Claims (14)
1. The method for optimizing the edible fungi breeding growth parameters is characterized by comprising the following steps:
collecting a set of growth stages of edible fungus breeding;
in an experimental environment, collecting speed training data and stage result training data of the edible fungi in each growth stage;
collecting nutrition absorption data of each test edible fungus colony in the complete cultivation stage;
training a set of first machine learning models that predict growth rates of edible fungus colonies at various growth stages based on the rate training data;
training a set of second machine learning models that predict stage outcome data of the edible fungus colony at each growth stage based on the stage outcome training data;
training a third machine learning model that predicts total nutrient consumption based on the stage outcome data and the nutrient absorption data;
and analyzing growth parameters suitable for each growth stage of the edible fungus colony to be bred based on the first machine learning model set, the second machine learning model set and the third machine learning model.
2. The method of claim 1, wherein the set of growth stages comprises each growth stage that an edible fungus colony undergoes from inoculation of a culture vessel to colony maturation; the set of growth phases includes a sporulation phase, a mycelium growth phase, a colony formation phase, and a colony maturation phase.
3. The method for optimizing edible fungi breeding growth parameters according to claim 2, wherein in the experimental environment, an experimenter observes different breeding processes and breeding results of each test edible fungi colony by actively controlling culture container parameters, culture fluid parameters and environment parameters of each test edible fungi colony so as to collect speed training data of the test edible fungi colony in different growth stages and area training data of the test edible fungi colony in a colony maturation stage; the test edible fungus colony is an edible fungus colony bred by an experimenter in an experimental environment.
4. A method for optimizing edible fungi breeding growth parameters according to claim 3, wherein the speed training data comprises growth parameter characteristic data, colony initial characteristic data and growth speed of all test edible fungi colonies in corresponding growth stages;
wherein the growth parameter characteristic data comprises environmental parameter values which influence the growth speed of the edible fungus colony and the colony area of the mature stage in each growth stage of the edible fungus colony;
the colony initial characteristic data are all growth characteristic data of edible fungi at the beginning time of all growth phases of the edible fungi colony;
The growth rate is the length of time that a test edible fungus colony experiences from a current growth stage to a next growth stage when providing an environment for corresponding growth parameter characteristic data for each test edible fungus colony in each growth stage.
5. The method of claim 4, wherein the stage result training data comprises growth parameter characteristic data, colony initial characteristic data and stage result characteristic data of the test edible fungus colony in the corresponding growth stage; the stage result characteristic data are colony initial characteristic data of the edible fungus colony in the next growth stage;
the stage result characteristic data of the colony maturation stage is the result value of the colony when the colony is stable; the stable colony refers to the condition that the area of the edible fungus colony is not increased any more; the calculation formula of the result value is as follows:
when the colony is stable, testing the weight mark W, the area mark S and the result value F of the edible fungus colony; the outcome value f=a1×w+a2×s; wherein a1 and a2 are preset proportionality coefficients respectively.
6. The method of claim 5, wherein the nutrient absorption data comprises a sum of consumption of various nutrient factors for each test edible fungus colony from the time of inoculating spores into the culture vessel to the time of colony stabilization;
The consumption of each nutrient is the difference between the nutrient content when spores are inoculated into the culture vessel and the nutrient content when colonies are stable in the culture vessel.
7. The method of claim 6, wherein the training the set of first machine learning models for predicting the growth rate of the colony of edible fungi at each growth stage is as follows:
marking the number of the growth phase in the growth phase set as j;
for the j-th growth stage, combining the growth parameter characteristic data and the colony initial characteristic data in the speed training data into the form of characteristic vectors, wherein the characteristic vectors are used as the input of a first machine learning model, the first machine learning model takes the predicted growth speed of each group of characteristic vectors as the output, the growth speed corresponding to the characteristic vectors in the speed training data is used as a prediction target, and the sum of prediction errors of all the characteristic vectors is minimized as the training target; training the first machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain a first machine learning model for predicting the growth speed of the edible fungus colony in the j growth stage according to the growth parameter characteristic data and the colony initial characteristic data; the feature values in the feature vector include environmental parameter values in the growth parameter feature data and colony initial feature data.
8. The method of claim 7, wherein the training the set of second machine learning models for predicting the stage outcome data of the edible fungus colony in each growth stage is:
for the j-th growth stage, combining each group of growth parameter feature data and colony initial feature data in the stage result training data into a feature vector form, wherein the feature vector is used as the input of a second machine learning model, the second machine learning model takes the predicted stage result feature data of each group of feature vectors as the output, takes the stage result feature data corresponding to the feature vector in the stage result training data as a prediction target, and takes the sum of prediction errors of all the feature vectors as a training target; training the second machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain the second machine learning model for outputting the phase result characteristic data for predicting the j growth phase according to the growth parameter characteristic data and the colony initial characteristic data.
9. The method for optimizing edible fungi breeding growth parameters according to claim 8, wherein the third machine learning model for predicting the total nutrient consumption is trained by:
Taking the result value of each test edible fungus colony as the input of a third machine learning model, wherein the third machine learning model takes the nutrition absorption data predicted for each test edible fungus colony as the output, takes the collected nutrition absorption data of each test edible fungus colony as a prediction target, and takes the sum of all prediction errors for predicting the nutrition absorption data as a training target; and training the third machine learning model until the sum of the prediction errors reaches convergence, and stopping training to obtain the third machine learning model which outputs predicted nutrition absorption data according to the result value.
10. The method of optimizing edible fungi breeding growth parameters according to claim 9, wherein analyzing growth parameters suitable for each growth stage of edible fungi colonies to be bred comprises the steps of:
step P1: setting the number of inoculated spores for the edible fungus colonies to be cultivated in advance, and setting a group of initial growth parameters for each growth stage; marking the number of the growth parameter type as i, marking the value of the ith initial growth parameter of the jth growth stage as Rji, and presetting a step length bi for the ith growth parameter;
step P2: calculating comprehensive benefits; marking the composite benefit as Z;
Step P3: updating each initial growth parameter Rji in a way of randomly increasing or decreasing the corresponding step bi, marking the updated initial growth parameters Rji as Rji1, recalculating the updated comprehensive benefit, and marking the updated comprehensive benefit as Z1;
step P4: calculating the gradient Tji of the ith initial growth parameter of the jth growth stage, wherein the calculation formula of the gradient Tji is as followsThe method comprises the steps of carrying out a first treatment on the surface of the Updating each updated initial growth parameter Rji1 to Rji1-bi×Tji, and recalculating the comprehensive benefits; the original Rji1 is re-labeled Rji, Z1 is re-labeled Z, and the re-calculated combined benefit is labeled Z1;
step P5: step P4 is executed circularly until the cycle times reach a preset cycle times threshold value; and taking the ith initial growth parameter Rji1 of the jth growth stage as the value of the corresponding growth parameter of the edible fungus colony to be cultivated in the corresponding stage when the cycle is finished.
11. The method for optimizing edible fungi breeding growth parameters according to claim 10, wherein the comprehensive profit is calculated by the following steps:
marking the initial growth parameters of the j-th growth stage as Cj;
taking initial growth parameters Cj of the jth growth stage and initial characteristic data of the colony as input of a second machine learning model corresponding to the jth growth stage to obtain predicted stage result characteristic data of the jth growth stage;
Marking a result value corresponding to the stage result characteristic data of the colony maturation stage predicted by the edible fungus colony to be cultivated as Fp;
taking initial growth parameters Cj of a jth growth stage and colony initial characteristic data as input of a first machine learning model corresponding to the jth growth stage, obtaining a predicted growth rate of the jth growth stage, and marking the predicted growth rate of the jth growth stage as Vj;
inputting the result value Fp into a third machine learning model to obtain predicted nutrition absorption data; marking the predicted nutrient absorption data as X;
calculating a combined comprehensive benefit Z of the combination of the initial growth parameters; the calculation formula of the comprehensive benefit Z is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein b1 and b2 are preset scaling factors.
12. An edible fungus breeding growth parameter optimizing system realized based on the edible fungus breeding growth parameter optimizing method according to any one of claims 1-11, which is characterized by comprising a data collecting module, a model training module and a growth parameter analyzing module; wherein, each module is connected by a wired mode;
the data collection module is mainly used for collecting a growing stage set of edible fungus breeding, collecting speed training data and stage result training data of the tested edible fungus colonies in each growing stage in an experimental environment, collecting nutrition absorption data of each tested edible fungus colony in a complete breeding stage, and sending the collected speed training data, stage result training data and nutrition absorption data to the model training module;
The model training module is mainly used for training a set of first machine learning models for predicting the growth speed of the edible fungus colony in each growth stage based on the speed training data; training a set of second machine learning models that predict stage outcome data of the edible fungus colony at each growth stage based on the stage outcome training data; training a third machine learning model for predicting total nutrient consumption based on the stage outcome data and the nutrient absorption data, and transmitting the first machine learning model, the second machine learning model and the third machine learning model to a growth parameter analysis module;
the growth parameter analysis module is mainly used for analyzing growth parameters suitable for all growth stages of edible fungus colonies to be bred based on the first machine learning model set, the second machine learning model set and the third machine learning model.
13. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs the method of optimizing edible fungi breeding growth parameters of any of claims 1-11 by invoking a computer program stored in the memory.
14. A computer readable storage medium having stored thereon a computer program that is erasable;
the computer program, when run on a computer device, causes the computer device to perform the method of optimizing edible fungi breeding growth parameters according to any of the claims 1-11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311011774.XA CN116720633B (en) | 2023-08-11 | 2023-08-11 | Method for optimizing edible fungus breeding growth parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311011774.XA CN116720633B (en) | 2023-08-11 | 2023-08-11 | Method for optimizing edible fungus breeding growth parameters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116720633A true CN116720633A (en) | 2023-09-08 |
| CN116720633B CN116720633B (en) | 2023-10-27 |
Family
ID=87870192
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311011774.XA Active CN116720633B (en) | 2023-08-11 | 2023-08-11 | Method for optimizing edible fungus breeding growth parameters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116720633B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117148902A (en) * | 2023-10-31 | 2023-12-01 | 安徽农业大学 | Intelligent fungus stick growth environment self-adaptive control system and method |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1916789A (en) * | 2006-09-01 | 2007-02-21 | 上海农业信息有限公司 | System and method for monitoring breed of edible fungus |
| CN108739052A (en) * | 2018-06-01 | 2018-11-06 | 北京中环易达设施园艺科技有限公司 | A kind of system and method for edible fungi growth parameter optimization |
| CN108958204A (en) * | 2018-08-15 | 2018-12-07 | 天津农学院 | A kind of edible fungus culturing investigating method based on expert system knowledge base |
| CN111176238A (en) * | 2020-01-02 | 2020-05-19 | 科芯(天津)生态农业科技有限公司 | AIPA intelligent decision-making type precision agricultural system |
| RU2723189C1 (en) * | 2019-06-21 | 2020-06-09 | Автономная некоммерческая образовательная организация высшего образования "Сколковский институт науки и технологий" | Method for automatic selection of optimum parameters of plants growing based on methods of machine learning |
| CN111368643A (en) * | 2020-02-12 | 2020-07-03 | 杭州电子科技大学 | Escherichia coli dynamic growth monitoring method |
| CN113317125A (en) * | 2021-05-31 | 2021-08-31 | 江苏华骏生物科技有限公司 | Culture medium for industrial cultivation of maitake mushroom and preparation method thereof |
| US20210378190A1 (en) * | 2020-06-03 | 2021-12-09 | Nanotronics Imaging, Inc. | Controlled growth system for biologicals |
| CN114793671A (en) * | 2022-05-17 | 2022-07-29 | 青岛海礼荟景观艺术有限公司 | Method, equipment and medium for breeding indoor plants |
| CN114879786A (en) * | 2022-05-23 | 2022-08-09 | 连云港银丰食用菌科技有限公司 | Method, system, device and medium for acquiring edible fungus decision scheme |
| CN114901794A (en) * | 2019-11-15 | 2022-08-12 | 隆萨有限公司 | Process and system for producing an inoculum |
| JP2022158620A (en) * | 2021-04-02 | 2022-10-17 | 住友化学株式会社 | Prediction method, prediction device, and method of creating prediction model |
| CN115392576A (en) * | 2022-08-30 | 2022-11-25 | 上海第二工业大学 | Mushroom growth state space-time prediction method |
| CN115774391A (en) * | 2022-11-30 | 2023-03-10 | 宁夏农产品质量标准与检测技术研究所(宁夏农产品质量监测中心) | System and method for optimizing growth parameters of edible fungi |
-
2023
- 2023-08-11 CN CN202311011774.XA patent/CN116720633B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1916789A (en) * | 2006-09-01 | 2007-02-21 | 上海农业信息有限公司 | System and method for monitoring breed of edible fungus |
| CN108739052A (en) * | 2018-06-01 | 2018-11-06 | 北京中环易达设施园艺科技有限公司 | A kind of system and method for edible fungi growth parameter optimization |
| CN108958204A (en) * | 2018-08-15 | 2018-12-07 | 天津农学院 | A kind of edible fungus culturing investigating method based on expert system knowledge base |
| RU2723189C1 (en) * | 2019-06-21 | 2020-06-09 | Автономная некоммерческая образовательная организация высшего образования "Сколковский институт науки и технологий" | Method for automatic selection of optimum parameters of plants growing based on methods of machine learning |
| CN114901794A (en) * | 2019-11-15 | 2022-08-12 | 隆萨有限公司 | Process and system for producing an inoculum |
| CN111176238A (en) * | 2020-01-02 | 2020-05-19 | 科芯(天津)生态农业科技有限公司 | AIPA intelligent decision-making type precision agricultural system |
| CN111368643A (en) * | 2020-02-12 | 2020-07-03 | 杭州电子科技大学 | Escherichia coli dynamic growth monitoring method |
| US20210378190A1 (en) * | 2020-06-03 | 2021-12-09 | Nanotronics Imaging, Inc. | Controlled growth system for biologicals |
| JP2022158620A (en) * | 2021-04-02 | 2022-10-17 | 住友化学株式会社 | Prediction method, prediction device, and method of creating prediction model |
| CN113317125A (en) * | 2021-05-31 | 2021-08-31 | 江苏华骏生物科技有限公司 | Culture medium for industrial cultivation of maitake mushroom and preparation method thereof |
| CN114793671A (en) * | 2022-05-17 | 2022-07-29 | 青岛海礼荟景观艺术有限公司 | Method, equipment and medium for breeding indoor plants |
| CN114879786A (en) * | 2022-05-23 | 2022-08-09 | 连云港银丰食用菌科技有限公司 | Method, system, device and medium for acquiring edible fungus decision scheme |
| CN115392576A (en) * | 2022-08-30 | 2022-11-25 | 上海第二工业大学 | Mushroom growth state space-time prediction method |
| CN115774391A (en) * | 2022-11-30 | 2023-03-10 | 宁夏农产品质量标准与检测技术研究所(宁夏农产品质量监测中心) | System and method for optimizing growth parameters of edible fungi |
Non-Patent Citations (2)
| Title |
|---|
| 周志远: "基于深度学习的香菇菌丝生长程度识别系统研究", 《中国优秀硕士学位论文全文数据库 (农业科技辑)》, pages 048 - 206 * |
| 王丽芳等: "痂状炭角菌鉴定、培养条件优化及发酵产物活性分析", 《华南农业大学学报》, vol. 43, no. 2, pages 49 - 56 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117148902A (en) * | 2023-10-31 | 2023-12-01 | 安徽农业大学 | Intelligent fungus stick growth environment self-adaptive control system and method |
| CN117148902B (en) * | 2023-10-31 | 2024-03-19 | 安徽农业大学 | Intelligent fungus stick growth environment self-adaptive control system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116720633B (en) | 2023-10-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116720633B (en) | Method for optimizing edible fungus breeding growth parameters | |
| CN110378435B (en) | Apple leaf disease identification method based on convolutional neural network | |
| CN113711843B (en) | System and method for optimizing growth parameters of edible fungi | |
| CN108304970B (en) | Method and system for rapidly predicting storage period of apples and controlled atmosphere storage monitoring system | |
| US20200202051A1 (en) | Method for Predicting Outcome of an Modelling of a Process in a Bioreactor | |
| US20190197445A1 (en) | Information processing apparatus, method, and program thereof | |
| CN117073768B (en) | Beef cattle cultivation management system and method thereof | |
| CN109816267A (en) | A kind of intelligence Soybean production management method and system | |
| Anguraj et al. | Crop recommendation on analyzing soil using machine learning | |
| CN106774540A (en) | A kind of intelligent culture control system and method based on video identification | |
| CN115812634B (en) | Method and device for nondestructively sorting pre-hatching egg fertilization information | |
| CN114994299A (en) | Ruminant carbon emission gas detection method, device and system | |
| CN109711590B (en) | Fishery water quality analysis monitoring method, device and system | |
| CN114895731A (en) | Breeding monitoring system and method based on digital twins | |
| US11889797B2 (en) | Controlled growth system for biologicals | |
| CN114403486A (en) | Intelligent control method of airflow type cut-tobacco drier based on local peak value coding circulation network | |
| US20230309464A1 (en) | Method and apparatus for automated crop recipe optimization | |
| CN116630080B (en) | Method and system for determining capacity of aquatic product intensive culture feed based on image recognition | |
| CN108845075B (en) | Compost maturity real-time prediction method based on deep learning network | |
| CN109359624A (en) | The prediction technique and prediction meanss of laying hen foraging behaviour neural network based | |
| CN115469614A (en) | Tobacco processing control method based on multiple linear regression | |
| CN114612898A (en) | YOLOv5 network-based litchi fruit borer emergence rate detection method | |
| US11921039B2 (en) | Method and active control system for food treatment processes | |
| WO2024010461A1 (en) | A method and apparatus for fish farming by means of utilizing a computer-implemented fish farm simulation model. | |
| CN118151614A (en) | Control method and control system for adjusting organic material fermentation process by septic tank water |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |