CN114705810A - Crop fertilizer inspection and detection method and system - Google Patents
Crop fertilizer inspection and detection method and system Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 230
- 239000003337 fertilizer Substances 0.000 title claims abstract description 218
- 238000007689 inspection Methods 0.000 title claims abstract description 34
- 239000002689 soil Substances 0.000 claims abstract description 148
- 238000004364 calculation method Methods 0.000 claims abstract description 36
- 230000004720 fertilization Effects 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 27
- 239000012895 dilution Substances 0.000 claims description 63
- 238000010790 dilution Methods 0.000 claims description 63
- 238000012549 training Methods 0.000 claims description 29
- 238000000034 method Methods 0.000 claims description 16
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- 238000013145 classification model Methods 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 8
- 230000003044 adaptive effect Effects 0.000 claims description 7
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 4
- 239000011591 potassium Substances 0.000 claims description 4
- 229910052700 potassium Inorganic materials 0.000 claims description 4
- 229910052761 rare earth metal Inorganic materials 0.000 claims description 4
- 239000011573 trace mineral Substances 0.000 claims description 4
- 235000013619 trace mineral Nutrition 0.000 claims description 4
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 3
- 229910052698 phosphorus Inorganic materials 0.000 claims description 3
- 239000011574 phosphorus Substances 0.000 claims description 3
- 238000002372 labelling Methods 0.000 claims description 2
- 230000001737 promoting effect Effects 0.000 abstract description 4
- 238000012800 visualization Methods 0.000 abstract description 3
- 238000013139 quantization Methods 0.000 abstract description 2
- 239000000243 solution Substances 0.000 description 16
- 238000012271 agricultural production Methods 0.000 description 12
- 230000035558 fertility Effects 0.000 description 3
- 210000003608 fece Anatomy 0.000 description 2
- 239000010871 livestock manure Substances 0.000 description 2
- 238000002156 mixing Methods 0.000 description 2
- 235000015097 nutrients Nutrition 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000618 nitrogen fertilizer Substances 0.000 description 1
- 239000003895 organic fertilizer Substances 0.000 description 1
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
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- G01N33/245—
Abstract
The invention provides a crop fertilizer inspection and detection method and system, and relates to the field of fertilizer inspection and detection. According to the invention, through the mutual cooperation of the crop state detection system, the soil state detection system, the crop fertilizer detection system and the deviation calculation system, the optimal fertilization strategy suitable for the current growth state of crops can be output without depending on individual experience, so that the problem of limited number of agricultural practitioners is relieved; as each system can quantize the element indexes, the formulated fertilization strategy has pertinence, thereby better promoting the growth of crops and having practicability and safety; each detection result is subjected to quantization processing, so that subsequent informatization and visualization processing are facilitated, and the advantages of rapidness, accuracy, easiness in operation, low cost and the like are taken into consideration; the soil state matching system can accurately classify and match the crop state detection result according to the prior data, so that experience dependence on individual personnel is eliminated.
Description
Technical Field
The invention relates to the field of fertilizer inspection and detection, in particular to a crop fertilizer inspection and detection method and system.
Background
The fertilizer is a substance which can provide nutrients required by the growth and development of crops, improve the soil properties and improve the yield and quality of the crops. Is an important production data in agricultural production. Generally, the fertilizer is divided into organic fertilizer, inorganic fertilizer and biological fertilizer. And may be divided into farmyard manure and chemical manure according to the source. Dividing the fertilizer into complete fertilizer and incomplete fertilizer according to the amount of the contained nutrients; according to the characteristics of fertilizer supply, the fertilizer is divided into direct fertilizer and indirect fertilizer; according to the components, the fertilizer is divided into nitrogen fertilizer, potassium fertilizer, trace element fertilizer and rare earth element fertilizer. The fertilizer can improve the soil property and the soil fertility level, and plays a vital role in modern agricultural production.
The existing agricultural fertilization is that agricultural production personnel judge the growth state of crops according to the growth condition of the crops, carry out fertilizer matching according to the growth state of the crops, and fertilize the crops after the fertilizer is proportioned, so that the aim of promoting the healthy growth of the crops is fulfilled. However, the fertilizer application mode needs agricultural production personnel to have rich fertilizer application experience and can accurately judge the growth condition of crops; meanwhile, agricultural production personnel also need to accurately grasp the content and the dilution ratio of each element of the fertilizer, if the fertilizer is applied by mistake, the growth of crops cannot be promoted, and the condition that the crops die in a large area can even happen seriously and even directly.
The fertility requirements of different growth periods and states of crops on the land are different, and agricultural production personnel are required to adjust the fertility of the land at any time according to actual conditions, but the number of the agricultural production personnel with rich agricultural production experience is limited, the energy of the personnel is limited, the crop fertilization guidance can be only carried out on local agricultural production areas in many cases, and more agricultural production areas cannot obtain professional fertilization guidance. In addition, the existing agricultural production lacks a method or a system for detecting whether the crop fertilizer is matched with the growth condition of the crops.
Therefore, it is necessary to provide a method and a system for inspecting and detecting fertilizer of crops to help agricultural production personnel to understand the growth status of crops and to correspondingly conduct fertilization guidance.
Disclosure of Invention
In order to solve one or more technical problems, the invention provides a crop fertilizer detection system, which is used for detecting the matching degree between a crop fertilizer and crops; the system comprises a crop state detection system, a soil state matching system, a soil state detection system, a crop fertilizer detection system, a deviation calculation system and a fertilizer inspection detection system.
Specifically, the crop state detection system is used for detecting the existing growth state of crops and obtaining a crop state detection result; the soil state matching system is used for matching the soil state suitable for crop growth according to the crop state result and obtaining the soil optimal state result; the soil state detection system is used for detecting the state of soil in the existing crop growth environment and obtaining a soil state detection result; the crop fertilizer detection system is used for detecting fertilizer components used by the existing crop growth and obtaining a fertilizer component detection result; the deviation calculation system is used for calculating element content deviation between the soil optimal state result and the soil state detection result and obtaining a soil element deviation result; the deviation calculation system is also used for calculating the element content deviation between the soil element deviation result and the fertilizer component detection result and obtaining a fertilizer element deviation result; and the fertilizer inspection and detection system is used for adjusting the element indexes of the fertilizer according to the fertilizer element deviation result and outputting the optimal fertilization strategy.
As a further solution, the crop state detection result, the soil optimum state result, the soil state detection result and the fertilizer component detection result are presented by a plurality of element indexes; the element indexes comprise a nitrogen element index, a phosphorus element index, a potassium element index, a plurality of trace element indexes, a plurality of organic element indexes and a plurality of rare earth element indexes; the deviation calculation system can carry out transverse deviation comparison calculation on single or multiple element indexes to obtain an element deviation result.
As a further solution, the crop status detection system is deployed through a crop status analysis model based on image recognition; acquiring growth state pictures of crops, and marking the crop state through manpower and/or machines to obtain a crop state training set; the crop state analysis model carries out image recognition training and negative feedback recognition training through a crop state training set to obtain a crop state recognition model; and outputting the crop state recognition model meeting the recognition accuracy, finishing the training of the crop state analysis model and deploying the model to obtain the crop state detection system.
As a further solution, the soil state matching system is deployed through a state classification model based on prior data; the method comprises the steps of acquiring optimal soil states corresponding to various growth states of crops as prior data, and carrying out classification labeling on the crop states through manpower and/or machines to obtain a soil state matching training set; the state classification model carries out classification matching training through a soil state matching training set to obtain a soil state matching model; and outputting the soil state matching model meeting the matching accuracy, finishing training of the state classification model, and deploying the model to obtain the soil state matching system.
As a further solution, the crop state detection system can also judge the crop state through manual detection, and obtain a crop state detection result; the soil state matching system can also select the soil state suitable for the growth of crops through manual matching, and obtain the optimal soil state result.
A crop fertilizer inspection and detection method is applied to any one of the crop fertilizer inspection and detection systems, the difference between the soil state and the optimal soil state is inspected through the following steps, and the optimal crop fertilization strategy is obtained:
A1, obtaining a soil optimal state result through a soil state matching system;
a2, acquiring a soil state detection result through a soil state detection system;
a3, calculating element content deviation between the soil optimal state result and the soil state detection result through a deviation calculation system, and obtaining a soil element deviation result;
a4, obtaining a fertilizer component detection result through a crop fertilizer detection system;
a5, calculating element content deviation between the soil element deviation result and the fertilizer component detection result through a deviation calculation system, and obtaining a fertilizer element deviation result, wherein the deviation calculation system also calculates and carries out self-adaptive concentration matching processing on the fertilizer component detection result;
a6, inputting the deviation result of the fertilizer elements into a fertilizer inspection and detection system;
a7 fertilizer inspection and detection system adjusts the element indexes of the fertilizer and records the adjustment steps to obtain the optimal fertilization strategy.
As a further solution, the crop status is checked and matched with the soil optimum status corresponding to the crop status by the following steps:
b1 shooting growth state pictures of crops in the same soil environment, and inputting the growth state pictures into a crop state detection system;
B2 the crop state detection system carries out image recognition on the growth state picture of each crop to obtain the crop state detection result of each crop;
b3, carrying out sample processing on the crop state detection result of each crop to obtain a large sample crop state detection result;
namely: setting a confidence threshold value, and if the same occurrence times of the crop state detection results of all crops are not lower than the confidence threshold value, determining that the crops are large-sample crop state detection results; if the same occurrence times of the crop state detection results of the crops are lower than the confidence threshold value, the crops are regarded as small-sample crop state detection results;
b4, inputting the large-sample crop state detection result into a soil state detection system, and performing classification matching to obtain a soil optimal state result.
As a further solution, the deviation calculation system calculates the adaptive concentration matching processing of the fertilizer component detection result by:
c1, corresponding the soil element deviation result to the same element indexes of the fertilizer component detection result one by one;
c2, calculating the dilution ratio of the single element indexes in the fertilizer component detection results according to the element indexes corresponding to the soil element deviation results; the calculation formula is as follows: ki is Mi/Ni; wherein, i is the index serial number of the element; ki is a dilution ratio coefficient; mi represents the element concentration corresponding to the i element indexes in the fertilizer component detection result; ni represents the element concentration corresponding to the i-item element index in the soil element deviation result;
C3, performing dilution concentration calculation on each element index in the fertilizer component detection result according to the dilution proportion coefficient Ki to obtain a fertilizer component dilution detection result;
c4, comparing the fertilizer component dilution detection result with the soil element deviation result item by item; if any element index exceeds the standard, discarding the element index; otherwise, storing the test result as a candidate fertilizer component dilution test result;
c5, treating each element index in the fertilizer component detection result according to the steps from C2 to C4 to obtain each candidate fertilizer component dilution detection result;
c6, carrying out matching degree grading on the dilution detection results of the candidate fertilizer components to obtain a candidate matching degree grading table;
c7, taking the candidate fertilizer component dilution detection result with the highest matching degree score as an optimal fertilizer component dilution detection result, and taking the corresponding dilution proportionality coefficient as an optimal dilution proportionality coefficient K;
c8, outputting the dilution detection result of the optimal fertilizer component, and calculating the deviation result of the fertilizer element to finish the adaptive concentration matching processing.
As a further solution, the fertilizer inspection and detection system scores the matching degree of the fertilizer by the following formula:
wherein ,
Scoring the degree of match; i is the index serial number of each element; m is the total number of indexes of each element; gamma rayiThe weight ratio corresponding to the index of the i element is obtained; ni is the element concentration corresponding to the i-item element index in the soil element deviation result; Mi/K is the element concentration corresponding to the i element index in the dilution detection result of the optimal fertilizer component.
As a further solution, the fertilizer inspection and detection system adjusts the element indexes of the fertilizer by the following steps:
e1, constructing a fertilizer database, wherein the fertilizer database comprises a plurality of fertilizer names and corresponding element index content lists;
e2 obtaining a fertilizer component detection result and a soil element deviation result;
e3, carrying out self-adaptive concentration matching processing on the fertilizer component detection result to obtain an optimal fertilizer component dilution detection result;
e4, carrying out difference processing on the soil element deviation result and each element index of the optimal fertilizer component dilution detection result to obtain an element index adjustment list;
e5 fitting the element index adjustment list through the element index content list in the fertilizer database;
e6 fitting is completed; and recording the fertilizer name and the dilution ratio coefficient used in the fitting, and outputting the fertilizer name and the dilution ratio coefficient as an optimal fertilization strategy.
Compared with the related technology, the crop fertilizer inspection and detection method and the crop fertilizer inspection and detection system provided by the invention have the following beneficial effects:
1. according to the invention, through the mutual cooperation of the crop state detection system, the soil state detection system, the crop fertilizer detection system and the deviation calculation system, the optimal fertilization strategy suitable for the current growth state of crops can be output without depending on individual experience, so that the problem of limited number of agricultural practitioners is relieved; as each system can quantize the element indexes, the formulated fertilization strategy has pertinence, thereby better promoting the growth of crops and having practicability and safety;
2. all detection results are subjected to quantization processing, so that subsequent informatization and visualization processing is facilitated; the crop state detection system is deployed through a crop state analysis model based on image recognition, can realize rapid detection of the growth state of crops, reduces manual processing procedures, and has the advantages of rapidness, accuracy, easiness in operation, low cost and the like;
3. the soil state matching system used by the invention can accurately classify and match the crop state detection result according to the prior data, so that the experience dependence on individual personnel is eliminated, and the trained state classification model can quickly, accurately and efficiently obtain the optimal state of the soil; thereby guiding the subsequent fertilizer component blending;
4. The invention realizes the selection of the optimal fertilizer component dilution detection result through the traversal method, thereby achieving the purpose of self-adaptive adjustment.
Drawings
FIG. 1 is a schematic diagram of a preferred system for inspecting and detecting fertilizer in crops according to an embodiment of the present invention;
fig. 2 is a flowchart of a preferred method for inspecting and detecting fertilizer of crops according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
As shown in fig. 1, the crop fertilizer inspection and detection system provided in this embodiment is used for inspecting and detecting the matching degree between a crop fertilizer and crops; the system comprises a crop state detection system, a soil state matching system, a soil state detection system, a crop fertilizer detection system, a deviation calculation system and a fertilizer inspection detection system.
Specifically, the crop state detection system is used for detecting the existing growth state of crops and obtaining a crop state detection result; the soil state matching system is used for matching the soil state suitable for crop growth according to the crop state result and obtaining the soil optimal state result; the soil state detection system is used for detecting the state of soil in the existing crop growth environment and obtaining a soil state detection result; the crop fertilizer detection system is used for detecting fertilizer components used by the existing crop growth and obtaining a fertilizer component detection result; the deviation calculation system is used for calculating the element content deviation between the soil optimal state result and the soil state detection result and obtaining a soil element deviation result; the deviation calculation system is also used for calculating the element content deviation between the soil element deviation result and the fertilizer component detection result and obtaining a fertilizer element deviation result; and the fertilizer inspection and detection system is used for adjusting the element indexes of the fertilizer according to the fertilizer element deviation result and outputting the optimal fertilization strategy.
It should be noted that: the traditional crop fertilization strategy is formulated by agricultural practitioners with abundant experience according to experience, the end fertilization strategy prepared by the method is not stable enough, and the level of the agricultural practitioners can influence the ideal final fertilization effect to a great extent. In addition, because of the limited number of experienced agricultural practitioners, more crops cannot be purposefully fertilized, thereby affecting the ultimate agricultural yield. Therefore, the system can output the optimal fertilization strategy suitable for the current growth state of crops without depending on individual experience through the mutual cooperation of the crop state detection system, the soil state detection system, the crop fertilizer detection system and the deviation calculation system, so that the problem that the number of agricultural practitioners is limited is solved; as each system can quantize the element indexes, the formulated fertilization strategy has pertinence, thereby better promoting the growth of crops and having practicability and safety.
As a further solution, the crop status detection result, the soil optimum status result, the soil status detection result and the fertilizer component detection result are presented by a plurality of element indexes; the element indexes comprise nitrogen element indexes, phosphorus element indexes, potassium element indexes, a plurality of trace element indexes, a plurality of organic element indexes and a plurality of rare earth element indexes; the deviation calculation system can carry out transverse deviation comparison calculation on single or multiple element indexes to obtain an element deviation result.
It should be noted that: each detection result of the embodiment is quantized, so that subsequent informatization and visualization processing is facilitated.
As a further solution, the crop status detection system is deployed through a crop status analysis model based on image recognition; acquiring growth state pictures of crops, and marking the crop states manually and/or mechanically to obtain a crop state training set; the crop state analysis model carries out image recognition training and negative feedback recognition training through a crop state training set to obtain a crop state recognition model; and outputting the crop state recognition model meeting the recognition accuracy, finishing the training of the crop state analysis model and deploying the model to obtain the crop state detection system.
It should be noted that: the crop state detection system used in the implementation is deployed through the crop state analysis model based on image recognition, can realize the rapid detection of the growth state of crops, reduces manual processing procedures, and has the advantages of rapidness, accuracy, easiness in operation, low cost and the like.
As a further solution, the soil state matching system is deployed through a state classification model based on prior data; acquiring optimal soil states corresponding to all growth states of crops as prior data, and carrying out classification and marking on the crop states through manpower and/or machines to obtain a soil state matching training set; the state classification model carries out classification matching training through a soil state matching training set to obtain a soil state matching model; and outputting the soil state matching model meeting the matching accuracy, finishing training of the state classification model and deploying the model to obtain the soil state matching system.
It should be noted that: the soil state matching system used in the implementation can accurately classify and match the crop state detection result according to the prior data, so that experience dependence on individual personnel is eliminated, and the trained state classification model can quickly, accurately and efficiently obtain the optimal state of the soil; thereby guiding the subsequent fertilizer ingredient blending.
As a further solution, the crop state detection system can also judge the crop state through manual detection, and obtain a crop state detection result; the soil state matching system can also select the soil state suitable for the growth of crops through manual matching, and obtain the result of the optimal state of the soil.
It should be noted that: the crop state detection system and the soil state matching system provided by the embodiment can not only detect and identify through the training model, but also support manual input of crop state detection results and soil optimal state results, thereby increasing the operability of the system, and still ensuring the normal work of the system through a manual detection mode under the condition that the training model cannot normally identify.
As shown in fig. 2, a crop fertilizer testing and detecting method is applied to any one of the crop fertilizer testing and detecting systems, and the difference between the soil state and the optimal soil state is tested through the following steps, and the optimal crop fertilization strategy is obtained:
A1, obtaining a soil optimal state result through a soil state matching system;
a2, acquiring a soil state detection result through a soil state detection system;
a3, calculating element content deviation between the soil optimal state result and the soil state detection result through a deviation calculation system, and obtaining a soil element deviation result;
a4, obtaining a fertilizer component detection result through a crop fertilizer detection system;
a5, calculating element content deviation between the soil element deviation result and the fertilizer component detection result through a deviation calculation system, and obtaining a fertilizer element deviation result, wherein the deviation calculation system also calculates and carries out self-adaptive concentration matching processing on the fertilizer component detection result;
a6, inputting the deviation result of the fertilizer elements into a fertilizer inspection and detection system;
a7 fertilizer inspection and detection system adjusts the element indexes of the fertilizer and records the adjustment steps to obtain the optimal fertilization strategy.
As a further solution, the crop status is checked and matched with the soil optimum status corresponding to the crop status by the following steps:
b1 shooting growth state pictures of crops in the same soil environment, and inputting the growth state pictures into a crop state detection system;
B2 the crop state detection system carries out image recognition on the growth state picture of each crop to obtain the crop state detection result of each crop;
b3, carrying out sample processing on the crop state detection result of each crop to obtain a large sample crop state detection result;
namely: setting a confidence threshold value, and if the same occurrence times of the crop state detection results of all crops are not lower than the confidence threshold value, determining the crop state detection results as large-sample crop state detection results; if the same occurrence times of the crop state detection results of the crops are lower than the confidence threshold value, the crops are regarded as small-sample crop state detection results;
b4, inputting the large-sample crop state detection result into the soil state detection system, and performing classification matching to obtain the soil optimal state result.
It should be noted that: due to the individual phenomenon of the crop state, the crop fertilization is carried out aiming at the whole; therefore, when the crop state detection result is carried out, sample processing is required, and each detection result is screened through a confidence threshold value, so that the crop state detection result of a large sample and the crop state detection result of a small sample are obtained; the detection result of the small-sample crop state can be used as an example analysis material, but the detection result is not representative, so that the large-sample crop state is used for subsequent fertilizer detection, and the finally presented optimal fertilization strategy is more universal.
As a further solution, the deviation calculation system calculates the self-adaptive concentration matching processing of the fertilizer component detection result through the following steps:
c1, the soil element deviation result corresponds to the fertilizer component detection result in the same element indexes one by one;
c2, calculating the dilution ratio of the single element indexes in the fertilizer component detection results according to the element indexes corresponding to the soil element deviation results; the calculation formula is as follows: ki is Mi/Ni; wherein, i is the index serial number of the element; ki is a dilution ratio coefficient; mi represents the element concentration corresponding to the i element indexes in the fertilizer component detection result; ni represents the element concentration corresponding to the i-item element index in the soil element deviation result;
c3, performing dilution concentration calculation on each element index in the fertilizer component detection result according to a dilution proportion coefficient Ki to obtain a fertilizer component dilution detection result;
c4, comparing the fertilizer component dilution detection result with the soil element deviation result item by item; if any element index exceeds the standard, discarding the element index; otherwise, storing the test result as a candidate fertilizer component dilution test result;
c5, treating each element index in the fertilizer component detection result according to the steps from C2 to C4 to obtain each candidate fertilizer component dilution detection result;
C6, carrying out matching degree grading on the dilution detection results of the candidate fertilizer components to obtain a candidate matching degree grading table;
c7, taking the candidate fertilizer component dilution detection result with the highest matching degree score as an optimal fertilizer component dilution detection result, and taking the corresponding dilution ratio coefficient as an optimal dilution ratio coefficient K;
c8, outputting the dilution detection result of the optimal fertilizer component, and calculating the deviation result of the fertilizer element to complete the adaptive concentration matching processing.
It should be noted that: the dilution ratio of the fertilizer components needs to be considered for detection, but the dilution ratio can be adjusted according to actual conditions; therefore, in this embodiment, before calculating the fertilizer element deviation result, the adaptive concentration matching processing needs to be performed on the fertilizer component dilution detection result, and the optimal fertilizer component dilution detection result is selected by the traversal method, so that the purpose of adaptive adjustment is achieved.
As a further solution, the fertilizer inspection and detection system scores the matching degree of the fertilizer by the following formula:
wherein ,
scoring the degree of match; i is the index serial number of each element; m is the total number of indexes of each element; gamma rayiThe weight ratio corresponding to the index of the i element is obtained; ni is the element concentration corresponding to the i-item element index in the soil element deviation result; Mi/K is the element concentration corresponding to the i element index in the dilution detection result of the optimal fertilizer component.
It should be noted that: in this embodiment, matching degree scoring is performed on fertilizers, mainly to quantify whether fertilizers are suitable for crop growth and fertilization, the scoring idea is that the deviation value of each element index is multiplied by the weight ratio corresponding to the element index, and the sum of the weights of the matching degree scoring corresponding to each element index should be 1.
As a further solution, the fertilizer inspection and detection system adjusts the element indexes of the fertilizer by the following steps:
e1, constructing a fertilizer database, wherein the fertilizer database comprises a plurality of fertilizer names and corresponding element index content lists;
e2 obtaining a fertilizer component detection result and a soil element deviation result;
e3, carrying out self-adaptive concentration matching processing on the fertilizer component detection result to obtain an optimal fertilizer component dilution detection result;
e4, carrying out difference processing on the soil element deviation result and each element index of the optimal fertilizer component dilution detection result to obtain an element index adjustment list;
E5 fitting the element index adjustment list through the element index content list in the fertilizer database;
e6 fitting is completed; and recording the fertilizer name and the dilution ratio coefficient used in the fitting, and outputting the fertilizer name and the dilution ratio coefficient as an optimal fertilization strategy.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A crop fertilizer inspection and detection system is used for inspecting and detecting the matching degree between crop fertilizer and crops; the system is characterized by comprising a crop state detection system, a soil state matching system, a soil state detection system, a crop fertilizer detection system, a deviation calculation system and a fertilizer inspection detection system;
the crop state detection system is used for detecting the existing growth state of crops and obtaining a crop state detection result; the soil state matching system is used for matching the soil state suitable for crop growth according to the crop state result and obtaining the soil optimal state result; the soil state detection system is used for detecting the state of soil in the existing crop growth environment and obtaining a soil state detection result; the crop fertilizer detection system is used for detecting fertilizer components used by the existing crop growth and obtaining a fertilizer component detection result; the deviation calculation system is used for calculating element content deviation between the soil optimal state result and the soil state detection result and obtaining a soil element deviation result; the deviation calculation system is also used for calculating the element content deviation between the soil element deviation result and the fertilizer component detection result and obtaining a fertilizer element deviation result; and the fertilizer inspection and detection system is used for adjusting the element indexes of the fertilizer according to the fertilizer element deviation result and outputting the optimal fertilization strategy.
2. The crop fertilizer inspection and detection system of claim 1, wherein the crop condition detection result, the soil optimum condition result, the soil condition detection result and the fertilizer component detection result are presented by a plurality of element indicators; the element indexes comprise nitrogen element indexes, phosphorus element indexes, potassium element indexes, a plurality of trace element indexes, a plurality of organic element indexes and a plurality of rare earth element indexes; the deviation calculation system can carry out transverse deviation comparison calculation on single or multiple element indexes to obtain an element deviation result.
3. The crop fertilizer inspection detection system of claim 2, wherein the crop condition detection system is deployed through a crop condition analysis model based on image recognition; acquiring growth state pictures of crops, and marking the crop state through manpower and/or machines to obtain a crop state training set; the crop state analysis model carries out image recognition training and negative feedback recognition training through a crop state training set to obtain a crop state recognition model; and outputting the crop state recognition model meeting the recognition accuracy, finishing the training of the crop state analysis model and deploying the model to obtain the crop state detection system.
4. The crop fertilizer inspection and detection system of claim 3, wherein the soil condition matching system is deployed through a prior-based condition classification model; the method comprises the steps of acquiring optimal soil states corresponding to various growth states of crops as prior data, and carrying out classification labeling on the crop states through manpower and/or machines to obtain a soil state matching training set; the state classification model carries out classification matching training through a soil state matching training set to obtain a soil state matching model; and outputting the soil state matching model meeting the matching accuracy, finishing training of the state classification model, and deploying the model to obtain the soil state matching system.
5. The crop fertilizer inspection and detection system of claim 4, wherein the crop state detection system is further capable of determining the crop state through manual detection and obtaining a crop state detection result; the soil state matching system can also select the soil state suitable for the growth of crops through manual matching, and obtain the optimal soil state result.
6. A crop fertilizer inspection and detection method applied to a crop fertilizer inspection and detection system as claimed in any one of claims 1 to 5, wherein the difference between the soil condition and the optimal soil condition is inspected by the following steps to obtain the optimal crop fertilizer application strategy:
A1, obtaining a soil optimal state result through a soil state matching system;
a2, acquiring a soil state detection result through a soil state detection system;
a3, calculating element content deviation between the soil optimum state result and the soil state detection result through a deviation calculation system, and obtaining a soil element deviation result;
a4, obtaining fertilizer component detection results through a crop fertilizer detection system;
a5, calculating element content deviation between the soil element deviation result and the fertilizer component detection result through a deviation calculation system, and obtaining a fertilizer element deviation result, wherein the deviation calculation system also calculates and carries out self-adaptive concentration matching processing on the fertilizer component detection result;
a6, inputting the fertilizer element deviation result into a fertilizer inspection and detection system;
and (4) adjusting the element indexes of the fertilizer by the A7 fertilizer inspection and detection system, and recording the adjustment steps to obtain the optimal fertilization strategy.
7. The method for inspecting and detecting the fertilizer of the crops as claimed in claim 6, wherein the crop status is inspected and the soil optimum status corresponding to the crop status is matched by the following steps:
b1, shooting growth state pictures of crops in the same soil environment, and inputting the growth state pictures into a crop state detection system;
B2 the crop state detection system carries out image recognition on the growth state picture of each crop to obtain the crop state detection result of each crop;
b3, carrying out sample processing on the crop state detection result of each crop to obtain a large sample crop state detection result;
b4, inputting the large-sample crop state detection result into a soil state detection system, and performing classification matching to obtain a soil optimal state result.
8. The method for inspecting and detecting fertilizer of crops as claimed in claim 7, wherein said deviation calculation system calculates the adaptive concentration matching processing for the fertilizer component detection result by the following steps:
c1, the soil element deviation result corresponds to the fertilizer component detection result in the same element indexes one by one;
c2, calculating the dilution ratio of the single element indexes in the fertilizer component detection results according to the element indexes corresponding to the soil element deviation results; the calculation formula is as follows: ki is Mi/Ni; wherein, i is the index serial number of the element; ki is a dilution ratio coefficient;
mi represents the element concentration corresponding to the i element indexes in the fertilizer component detection result; ni represents the element concentration corresponding to the i-item element index in the soil element deviation result;
C3, performing dilution concentration calculation on each element index in the fertilizer component detection result according to a dilution proportion coefficient Ki to obtain a fertilizer component dilution detection result;
c4, comparing the fertilizer component dilution detection result with the soil element deviation result item by item; if any element index exceeds the standard, discarding the element index; otherwise, storing the test result as a candidate fertilizer component dilution test result;
c5, treating each element index in the fertilizer component detection result according to the steps from C2 to C4 to obtain each candidate fertilizer component dilution detection result;
c6, carrying out matching degree grading on the dilution detection results of the candidate fertilizer components to obtain a candidate matching degree grading table;
c7, taking the candidate fertilizer component dilution detection result with the highest matching degree score as an optimal fertilizer component dilution detection result, and taking the corresponding dilution proportionality coefficient as an optimal dilution proportionality coefficient K;
c8, outputting the dilution detection result of the optimal fertilizer component, and calculating the deviation result of the fertilizer element to finish the adaptive concentration matching processing.
9. The method for inspecting and detecting the fertilizer of the crops as claimed in claim 8, wherein the fertilizer inspecting and detecting system scores the matching degree of the fertilizer by the following formula:
wherein ,
scoring the degree of match; i is the index serial number of each element; m is the total number of indexes of each element; gamma rayiThe weight ratio corresponding to the i element indexes is obtained; ni is the element concentration corresponding to the i-item element index in the soil element deviation result; Mi/K is the element concentration corresponding to the i element index in the dilution detection result of the optimal fertilizer component.
10. The method for inspecting and detecting the fertilizer of the crops as claimed in claim 9, wherein the fertilizer inspecting and detecting system adjusts the element index of the fertilizer by the following steps:
e1, constructing a fertilizer database, wherein the fertilizer database comprises a plurality of fertilizer names and corresponding element index content lists;
e2 obtaining a fertilizer component detection result and a soil element deviation result;
e3, carrying out self-adaptive concentration matching processing on the fertilizer component detection result to obtain an optimal fertilizer component dilution detection result;
e4, carrying out difference processing on the soil element deviation result and each element index of the optimal fertilizer component dilution detection result to obtain an element index adjustment list;
e5 fitting the element index adjustment list through the element index content list in the fertilizer database;
e6 fitting is completed; and recording the fertilizer name and the dilution ratio coefficient used in the fitting, and outputting the fertilizer name and the dilution ratio coefficient as an optimal fertilization strategy.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1561669A (en) * | 2004-04-12 | 2005-01-12 | 中国科学院合肥智能机械研究所 | Device and method for forming prescription for monitoring crop growing state and nutrition spreading fertilizer |
| JP2005080514A (en) * | 2003-09-04 | 2005-03-31 | Matsushita Electric Ind Co Ltd | Fertilizer information-providing system, soil information input means and fertilizer information-providing method |
| CN107602278A (en) * | 2017-08-18 | 2018-01-19 | 四川诚至诚农业科技开发有限责任公司 | The preparation method of tobacco breeding and seedling nursing with equipment matrix |
| CN108684278A (en) * | 2017-04-06 | 2018-10-23 | 点豆（山东）网络技术有限公司 | Intelligent fertilizer distributing methods, devices and systems |
| CN111279952A (en) * | 2020-03-18 | 2020-06-16 | 嘉应学院 | Water and fertilizer integrated management system based on cloud platform |
| CN111713317A (en) * | 2020-07-24 | 2020-09-29 | 韶关市诚湃新能源科技有限公司 | Crop monitoring system and monitoring method based on big data |
| CN112526909A (en) * | 2020-12-02 | 2021-03-19 | 中国农业科学院农业信息研究所 | Wisdom agricultural equipment system based on thing networking |
| CN113396681A (en) * | 2021-07-02 | 2021-09-17 | 哈尔滨航天恒星数据系统科技有限公司 | Gridding field crop base fertilizer application effect evaluation model |
-
2022
- 2022-03-04 CN CN202210209483.0A patent/CN114705810B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005080514A (en) * | 2003-09-04 | 2005-03-31 | Matsushita Electric Ind Co Ltd | Fertilizer information-providing system, soil information input means and fertilizer information-providing method |
| CN1561669A (en) * | 2004-04-12 | 2005-01-12 | 中国科学院合肥智能机械研究所 | Device and method for forming prescription for monitoring crop growing state and nutrition spreading fertilizer |
| CN108684278A (en) * | 2017-04-06 | 2018-10-23 | 点豆（山东）网络技术有限公司 | Intelligent fertilizer distributing methods, devices and systems |
| CN107602278A (en) * | 2017-08-18 | 2018-01-19 | 四川诚至诚农业科技开发有限责任公司 | The preparation method of tobacco breeding and seedling nursing with equipment matrix |
| CN111279952A (en) * | 2020-03-18 | 2020-06-16 | 嘉应学院 | Water and fertilizer integrated management system based on cloud platform |
| CN111713317A (en) * | 2020-07-24 | 2020-09-29 | 韶关市诚湃新能源科技有限公司 | Crop monitoring system and monitoring method based on big data |
| CN112526909A (en) * | 2020-12-02 | 2021-03-19 | 中国农业科学院农业信息研究所 | Wisdom agricultural equipment system based on thing networking |
| CN113396681A (en) * | 2021-07-02 | 2021-09-17 | 哈尔滨航天恒星数据系统科技有限公司 | Gridding field crop base fertilizer application effect evaluation model |
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