CN117350422A - Method for estimating yield of flammulina velutipes - Google Patents
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Abstract
The invention relates to a method for estimating the yield of flammulina velutipes. The method solves the problems that the traditional crop yield prediction mode in the prior art is difficult to accurately evaluate according to the growth environment of the crop and the actual condition of the growth stage, and the evaluation accuracy is poor. S1, acquiring growth image data of flammulina velutipes mycelia; s2, monitoring mycelium growth stages of flammulina velutipes; s3, monitoring parameters of the flammulina velutipes mycelium growing environment; s4, establishing a prediction model to evaluate the yield of the flammulina velutipes. The invention has the advantages that: the yield evaluation accuracy is better, the reference value of the prediction result is higher, the evaluation accuracy can be continuously improved in the prediction process, and the method is suitable for the evaluation prediction of the yield of the flammulina velutipes in areas or ranges with different sizes.
Description
Technical Field
The invention relates to the technical field of agriculture, in particular to a method for estimating the yield of flammulina velutipes.
Background
In order to scientifically make agricultural product import and export plans, regulate and control grain markets, guide planting structure adjustment and the like, accurate and timely monitoring and evaluation of yield of regional grain crops are required. The crops are used as main grain crops, so that the yield of the crops can be accurately and timely predicted, the stable yield and the high yield of the crops can be ensured, and the grain safety is ensured.
At present, the crop yield estimation mainly utilizes historical meteorological data and satellite remote sensing data, but the historical meteorological data has strong territory, and the historical meteorological data in different areas has relatively large change, so that the crop yield estimation cannot be performed on a large scale by utilizing the historical meteorological data and the satellite remote sensing data, and the crop yield estimation precision is low; in addition, the existing crop yield evaluation scheme is difficult to accurately evaluate according to the growth environment of the crop and the actual condition of the growth stage, and the evaluation accuracy is low.
In order to solve the defects existing in the prior art, long-term exploration is performed, and various solutions are proposed. For example, chinese patent literature discloses a model training method and a crop yield estimation method [ cn201911352742.X ], which includes obtaining a sample normalized vegetation index NDVI, a sample soil moisture SM, and sample medium resolution imaging spectrometer MODIS remote sensing data and a sample crop yield; according to the sample NDVI, the sample SM and the sample MODIS remote sensing data, respectively corresponding NDVI and SM of each growing period of crops are determined; according to NDVI and SM and sample crop yield which correspond to each growing period of the crops, a yield estimation model is trained, and crop yield estimation accuracy is improved.
The scheme solves the problems that in the prior art, a crop prediction mode by utilizing historical meteorological data and satellite remote sensing data is difficult to perform large-scale prediction and the estimation precision is low to a certain extent, but the scheme still has a plurality of defects, such as: accurate assessment is difficult to be carried out according to the growth environment of the crops and the actual conditions of the growth stage, and the assessment accuracy is difficult to be improved.
Disclosure of Invention
The invention aims at providing a method for estimating the yield of flammulina velutipes aiming at the problems.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a method for estimating the yield of flammulina velutipes comprises the following steps:
s1, acquiring growth image data of flammulina velutipes mycelia;
s2, monitoring mycelium growth stages of flammulina velutipes;
s3, monitoring parameters of the flammulina velutipes mycelium growing environment;
s4, establishing a prediction model to evaluate the yield of the flammulina velutipes.
The method and the device detect the growth condition and the growth environment of the flammulina velutipes by adopting a target detection technology based on deep learning, and predict the yield and sales plan of the flammulina velutipes by combining multidimensional data, so that the yield assessment of the flammulina velutipes is based on the actual growth condition and the growth environment of the flammulina velutipes, and the accuracy of the yield assessment of the flammulina velutipes is effectively improved.
In step S1, step S1 specifically includes the steps of:
s11, dividing image acquisition areas of needle mushrooms;
s12, establishing a multi-dimensional image acquisition camera in each image acquisition area;
s13, acquiring flammulina velutipes growth images from different angles;
s14, the acquired image data are subjected to data division according to the image acquisition area and are stored in a database in a classified mode.
In step S11, a circular division mode is implemented when the image acquisition areas of the needle mushrooms are divided, and the number of the needle mushrooms in each image acquisition area is more than 20; in the steps S12-S13, the multi-dimensional image acquisition cameras are in an annular arrangement mode, and the number of flammulina velutipes image acquisition samples in the same image acquisition area is larger than 10 while multi-angle acquisition is carried out; in the process of acquiring the growth images of the flammulina velutipes, a mode of time-division acquisition is adopted, and all the image acquisition areas 2 in the same time period are synchronously acquired.
In step S14, the image stored in the classification is processed by using a data processing platform connected to the database, and the image processing specifically includes the steps of:
(1) clearing invalid pictures;
(2) sequencing according to the shooting time sequence to obtain the growth sequence of flammulina velutipes mycelia of each sample;
(3) and judging the average growth speed of flammulina velutipes mycelia in the image acquisition area where the current sample is positioned and the final maturation period according to the sampled flammulina velutipes mycelia growth sequence.
In the step S2, when monitoring the growth stages of flammulina velutipes mycelia, the monitored items comprise identification of the growth stages of flammulina velutipes and comparison of the heights of the flammulina velutipes mycelia in each growth stage, and monitoring the conditions of mites, dry rot and spot diseases harmful to flammulina velutipes growth in each growth stage;
the identification of the growth stages of the flammulina velutipes is judged through growth image data, the height of each growth stage of flammulina velutipes mycelium is compared with the standard height input in advance in a database, samples for monitoring the diseases and insect pests are summarized, a safety threshold of the samples for monitoring the diseases and insect pests is set, and when the threshold of the samples for monitoring the diseases and insect pests exceeds the safety threshold of the samples for monitoring the diseases and insect pests, a prompt is sent out to perform manual intervention treatment.
In the step S2, the parameter monitoring items of the flammulina velutipes mycelium growing environment mainly comprise a light receiving parameter, a nutrient parameter of a culture medium and an air environment parameter;
the light receiving parameters comprise illumination time and illumination area;
the nutrient parameters of the culture medium comprise the content proportion of water-soluble nutrient, exchangeable nutrient, slow-release nutrient and insoluble nutrient;
the air environmental parameters include the concentration of carbon dioxide and oxygen in the air and the air humidity.
In the method for estimating the yield of the flammulina velutipes, parameters of flammulina velutipes mycelium growth environments in different growth stages are recorded, the flammulina velutipes mycelium growth speed under the parameters of the current flammulina velutipes mycelium growth environments is recorded, and recorded data are input into a database.
In the method for predicting the yield of the flammulina velutipes, the prediction model 1 is connected with the database, and is established through the growth image of the mycelia of the flammulina velutipes, the growth environment of the mycelia of the flammulina velutipes, the environmental parameters of each growth stage of the mycelia of the flammulina velutipes and the growth condition of the stage, and is used for carrying out deep learning based on the data provided by the database to predict the yield of the flammulina velutipes in different stages in the future.
In the above method for predicting the yield of flammulina velutipes, the predicted yield of the prediction model is compared with the actual yield of the prediction time period, an error threshold is set, and when the predicted yield of the prediction model exceeds the error threshold, the stored data parameters of the database are secondarily optimized; when the predicted yield of the prediction model is within the set error threshold, recording the predicted result of the yield into a history prediction library as a history reference value of the next prediction, thereby continuously optimizing the accuracy of the predicted yield of the prediction model.
Compared with the prior art, the invention has the advantages that:
1. the corresponding evaluation of the flammulina velutipes is based on the actual production environment of the flammulina velutipes and the actual condition of each growth stage, and the yield evaluation accuracy is better;
2. the prediction model is based on multi-dimensional sampling data, and is combined with monitoring parameters of the Internet of things equipment to conduct diversified model training, so that the reference value of a prediction result is higher, and the evaluation accuracy can be continuously improved in the prediction process;
3. the data collection is carried out through the subarea multiple groups of samples, accurate and effective data can be obtained through the data processing of the data processing platform, the method has universality, and the method is suitable for evaluating and predicting the yield of the flammulina velutipes in areas or ranges with different sizes.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of a multi-dimensional image acquisition camera profile in the present invention;
FIG. 3 is a block diagram of a partial connection in the present invention;
in the figure: the system comprises a prediction model 1, an image acquisition area 2, a multi-dimensional image acquisition camera 3, a data processing platform 4 and a database 5.
Detailed Description
The invention will be described in further detail with reference to the drawings and the detailed description.
As shown in fig. 1-3, a method for estimating the yield of flammulina velutipes comprises the following steps:
s1, acquiring growth image data of flammulina velutipes mycelia;
s2, monitoring mycelium growth stages of flammulina velutipes;
s3, monitoring parameters of the flammulina velutipes mycelium growing environment;
s4, establishing a prediction model 1 to evaluate the yield of the flammulina velutipes.
The yield evaluation of the flammulina velutipes is based on self-growth data and growth environment of the flammulina velutipes in the specification stage, and the evaluation mode is fit with the actual situation.
In step S1, step S1 specifically includes the steps of:
s11, dividing an image acquisition area 2 of needle mushrooms;
s12, establishing a multi-dimensional image acquisition camera 3 in each image acquisition area 2,
s13, acquiring flammulina velutipes growth images from different angles;
s14, the acquired image data are subjected to data division according to the image acquisition area 2 and are stored in the database 5 in a classified mode.
The image acquisition area 2 is divided for partition acquisition, so that partition statistics is facilitated, and due to the difference of the growth environments of each area, data refinement can be ensured by using partition data acquisition, and the accuracy of yield evaluation is improved.
In step S11, when the image acquisition areas 2 of the needle mushrooms are divided, a circular division is performed, and the number of needle mushrooms in each image acquisition area 2 is greater than 20; in the steps S12-S13, the multi-dimensional image acquisition cameras 3 are in an annular arrangement mode, and the number of flammulina velutipes image acquisition samples in the same image acquisition area 2 is more than 10 while multi-angle acquisition is carried out; in the process of acquiring the growth images of the flammulina velutipes, a mode of time-division acquisition is adopted, and all the image acquisition areas 2 in the same time period are synchronously acquired.
The needle mushroom image acquisition samples are acquired at intervals, namely, the minimum distance is set between the adjacent needle mushroom image acquisition samples, so that the acquired samples are representative, and data errors are reduced.
In step S14, the data processing platform 4 connected to the database 5 is used to process the images stored separately, the image processing specifically comprising the steps of:
(1) clearing invalid pictures;
(2) sequencing according to the shooting time sequence to obtain the growth sequence of flammulina velutipes mycelia of each sample;
(3) and judging the average growth speed of flammulina velutipes mycelia in the image acquisition area 2 where the current sample is positioned and the final maturation period according to the sampled flammulina velutipes mycelia growth sequence.
And predicting the final maturation period according to the average normal speed of the flammulina velutipes mycelia and the height of the current stage, so as to evaluate the yield time of the flammulina velutipes in the image acquisition region 2 after the flammulina velutipes are totally matured.
In the step S2, when monitoring the growth stages of flammulina velutipes mycelia, the monitored items comprise identification of the growth stages of flammulina velutipes mycelia and comparison of the heights of the flammulina velutipes mycelia in each growth stage, and monitoring the conditions of mites, dry rot and spot diseases harmful to flammulina velutipes growth in each growth stage;
the identification of the growth stages of the flammulina velutipes mycelia is judged through the growth image data, the height of each growth stage of the flammulina velutipes mycelia is compared with the standard height input in advance in the database 5, samples for monitoring the diseases and insect pests are summarized, a safety threshold of the samples for monitoring the diseases and insect pests is set, and when the threshold of the samples for monitoring the diseases and insect pests exceeds the safety threshold of the samples for monitoring the diseases and insect pests, a prompt is sent out to perform manual intervention treatment.
In the step S2, the parameter monitoring items of the flammulina velutipes growing environment mainly comprise a light receiving parameter, a nutrient parameter of a culture medium and an air environment parameter;
the light receiving parameters comprise illumination time and illumination area;
the nutrient parameters of the culture medium comprise the content proportion of water-soluble nutrient, exchangeable nutrient, slow-release nutrient and insoluble nutrient;
the air environmental parameters include the concentration of carbon dioxide and oxygen in the air and the air humidity.
And the most suitable growth environment is judged by combining the growth height and average growth speed of the flammulina velutipes in the current stage through parameter monitoring of the flammulina velutipes growth environment.
Specifically, parameters of the flammulina velutipes growing environment in different growing stages are recorded, the flammulina velutipes mycelium growing speed under the parameters of the current flammulina velutipes growing environment is recorded, and recorded data are input into the database 5.
Therefore, the most proper growth environment is convenient to judge, and the improvement of the growth environment during the next sequential planting is facilitated, so that the yield is improved.
Preferably, the prediction model 1 is connected with the database 5, and the prediction model 1 is built by the growth image of the hypha of the flammulina velutipes in the database 5, the growth environment of the hypha of the flammulina velutipes, the environmental parameters of each growth stage of the hypha of the flammulina velutipes and the growth condition of the stage, and the prediction model 1 performs deep learning based on the data provided by the database 5 to predict the flammulina velutipes yield in different stages in the future.
And predicting and evaluating the yields of the flammulina velutipes in different time periods through the multidimensional data parameters, and carrying out fitting actual accurate evaluation according to the actual growth conditions of the flammulina velutipes.
In detail, the predicted yield of the prediction model 1 is compared with the actual yield of the prediction time period, an error threshold is set, and when the predicted yield of the prediction model 1 exceeds the error threshold, the stored data parameters of the database 5 are secondarily optimized; when the predicted yield of the prediction model 1 is within the set error threshold, the predicted result of the yield is recorded into a history prediction library as a history reference value of the next prediction, so that the accuracy of the predicted yield of the prediction model 1 is continuously optimized.
In summary, the principle of this embodiment is as follows: the method comprises the steps of establishing a prediction model 1 through multidimensional data of growth environment parameters, growth stage changes and average growth speed of the growth stage, deep learning by the prediction model 1 through multidimensional data, predicting the flammulina velutipes mycelium sample of each image acquisition region 2 based on a database 5, summarizing prediction results of all the image acquisition regions 2, obtaining flammulina velutipes yield prediction results of the whole region, setting a prediction error threshold value for comparison, continuously optimizing the prediction model 1 according to the prediction results, and improving prediction accuracy.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Although the terms predictive model 1, image acquisition area 2, multi-dimensional image acquisition camera 3, data processing platform 4, database 5, etc. are used more herein, the possibility of using other terms is not precluded. These terms are used merely for convenience in describing and explaining the nature of the invention; they are to be interpreted as any additional limitation that is not inconsistent with the spirit of the present invention.
Claims (10)
1. The method for estimating the yield of the flammulina velutipes is characterized by comprising the following steps of:
s1, acquiring growth image data of flammulina velutipes mycelia;
s2, monitoring mycelium growth stages of flammulina velutipes;
s3, monitoring parameters of the flammulina velutipes mycelium growing environment;
s4, establishing a prediction model (1) to evaluate the yield of the flammulina velutipes.
2. The method for estimating the yield of needle mushrooms is characterized in that in the step S1, the step S1 specifically comprises the following steps:
s11, dividing an image acquisition area (2) of needle mushrooms;
s12, establishing a multi-dimensional image acquisition camera (3) in each image acquisition area (2);
s13, acquiring flammulina velutipes growth images from different angles;
s14, the acquired image data are subjected to data division according to the image acquisition area (2) and are stored in the database (5) in a classified mode.
3. The method for predicting yield of needle mushrooms according to claim 2, wherein in step S11, a circular division is performed when dividing the image acquisition areas (2) of needle mushrooms, and the number of needle mushrooms in each image acquisition area (2) is greater than 20; in the steps S12-S13, the multi-dimensional image acquisition cameras (3) are in an annular arrangement mode, and the number of flammulina velutipes image acquisition samples in the same image acquisition area (2) is more than 10 while multi-angle acquisition is carried out; in the process of acquiring the growth images of the flammulina velutipes, a mode of time-division acquisition is adopted, and all the image acquisition areas (2) in the same time period are synchronously acquired.
4. A method for predicting yield of needle mushrooms according to claim 3, wherein in step S14, the images stored separately are processed by means of a data processing platform (4) connected to a database (5), the image processing comprising in particular the steps of:
(1) clearing invalid pictures;
(2) sequencing according to the shooting time sequence to obtain the growth sequence of flammulina velutipes mycelia of each sample;
(3) and judging the average growth speed of flammulina velutipes mycelia in the image acquisition area (2) where the current sample is positioned and the final maturation period according to the sampled flammulina velutipes mycelia growth sequence.
5. The method for predicting yield of flammulina velutipes according to claim 2, wherein in step S2, when monitoring the growth stages of flammulina velutipes mycelia, the monitored items include identification of the growth stage of flammulina velutipes and comparison of the height of each growth stage of flammulina velutipes mycelia, and monitoring the pest and disease conditions of mites, dry rot and spot on flammulina velutipes growth in each growth stage;
6. the method for predicting the yield of flammulina velutipes according to claim 5, wherein identification of the growth stage in which flammulina velutipes hypha is located is judged through growth image data, the height of each growth stage of flammulina velutipes hypha is compared with a standard height input in advance in a database (5), samples monitored by diseases and insect pests are summarized, a safety threshold of the samples monitored by the diseases and insect pests is set, and when the safety threshold of the samples monitored by the diseases and insect pests exceeds the safety threshold of the samples monitored by the diseases and insect pests, a prompt is sent out to perform manual intervention treatment.
7. The method for predicting yield of needle mushrooms according to claim 2, wherein in step S2, the parameters of the mycelium growth environment of needle mushrooms are monitored mainly by light receiving parameters, nutrient parameters of a culture medium and air environment parameters;
the light receiving parameters comprise illumination time and illumination area;
the nutrient parameters of the culture medium comprise the content proportion of water-soluble nutrient, exchangeable nutrient, slow-release nutrient and insoluble nutrient;
the air environmental parameters include the concentration of carbon dioxide and oxygen in the air and the air humidity.
8. The method for predicting yield of needle mushrooms according to claim 7, wherein parameters of the needle mushrooms in different growth stages are recorded, the growth speed of the mycelia of the needle mushrooms under the parameters of the current needle mushrooms in the growth stages is recorded, and recorded data are input into the database (5).
9. The method for predicting the yield of the flammulina velutipes according to claim 8, wherein the prediction model (1) is connected with the database (5), and the prediction model (1) is established through a growth image of mycelia of the flammulina velutipes in the database (5), a growth environment of mycelia of the flammulina velutipes, environmental parameters of each growth stage of mycelia of the flammulina velutipes and growth conditions of the stages, and the prediction model (1) performs deep learning based on data provided by the database (5) to predict the yield of the flammulina velutipes in different stages in the future.
10. The method for predicting the yield of flammulina velutipes according to claim 9, wherein the predicted yield of the prediction model (1) is compared with the actual yield in the prediction time period, an error threshold is set, and when the predicted yield of the prediction model (1) exceeds the error threshold, the stored data parameters of the database (5) are secondarily optimized; when the predicted yield of the prediction model (1) is within a set error threshold, recording a result of the yield prediction into a history prediction library as a history reference value of the next prediction, so as to continuously optimize the accuracy of the predicted yield of the prediction model (1).
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| CN118519403A (en) * | 2024-07-11 | 2024-08-20 | 江西高田生态农业科技有限公司 | Intelligent production monitoring system and method for agricultural greenhouse based on Internet of things |
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| CN118519403A (en) * | 2024-07-11 | 2024-08-20 | 江西高田生态农业科技有限公司 | Intelligent production monitoring system and method for agricultural greenhouse based on Internet of things |
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