CN107766938A - A kind of plant cover cultivation methods based on BP neural network - Google Patents
A kind of plant cover cultivation methods based on BP neural network Download PDFInfo
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 42
- 238000012364 cultivation method Methods 0.000 title claims abstract description 10
- 230000012010 growth Effects 0.000 claims abstract description 39
- 239000002689 soil Substances 0.000 claims abstract description 28
- 238000001514 detection method Methods 0.000 claims abstract description 18
- 238000000034 method Methods 0.000 claims abstract description 7
- 241000196324 Embryophyta Species 0.000 claims description 71
- 238000007689 inspection Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 210000004218 nerve net Anatomy 0.000 claims description 3
- 230000007935 neutral effect Effects 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 239000002585 base Substances 0.000 description 5
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 3
- 150000003839 salts Chemical class 0.000 description 3
- CDBYLPFSWZWCQE-UHFFFAOYSA-L Sodium Carbonate Chemical compound [Na+].[Na+].[O-]C([O-])=O CDBYLPFSWZWCQE-UHFFFAOYSA-L 0.000 description 2
- PMZURENOXWZQFD-UHFFFAOYSA-L Sodium Sulfate Chemical compound [Na+].[Na+].[O-]S([O-])(=O)=O PMZURENOXWZQFD-UHFFFAOYSA-L 0.000 description 2
- 239000003513 alkali Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000011780 sodium chloride Substances 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 description 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 239000011575 calcium Substances 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- BVKZGUZCCUSVTD-UHFFFAOYSA-N carbonic acid Chemical compound OC(O)=O BVKZGUZCCUSVTD-UHFFFAOYSA-N 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 125000004435 hydrogen atom Chemical class [H]* 0.000 description 1
- 238000003475 lamination Methods 0.000 description 1
- 239000011777 magnesium Substances 0.000 description 1
- 229910052749 magnesium Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 230000008635 plant growth Effects 0.000 description 1
- 229910052708 sodium Inorganic materials 0.000 description 1
- 239000011734 sodium Substances 0.000 description 1
- 229910000029 sodium carbonate Inorganic materials 0.000 description 1
- 229910052938 sodium sulfate Inorganic materials 0.000 description 1
- 235000011152 sodium sulphate Nutrition 0.000 description 1
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Abstract
The present invention relates to a kind of plant cover cultivation methods based on BP neural network, its step mainly includes the growth performance situation based on the plant transmitted in advance in controller, initial data analyze and process using BP neural network, obtains initial growth desirable value；Acid-base value, ph, the salinity of the soil detected based on acid-base value detector, detection data are passed to controller, detection data are analyzed and processed using BP neural network, obtain environment measuring value；By environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances.The detector of beneficial effects of the present invention quickly to the ph values of the acid-base value of soil, salinity and soil, and by BP neural network with judge be adapted to plant arable in the present context.
Description
Technical field
The present invention relates to plant planting field, more particularly to a kind of plant cover cultivation methods based on BP neural network.
Background technology
Flow down location in the Yellow River, and the salinization of soil problem of soil is extremely serious, and soluble organic fraction is constantly to soil table in soil
Process of the lamination combinate form into saline-alkali soil.Excessive solubility salt (sodium sulphate, sodium chloride, sodium carbonate, carbonic acid are accumulated in soil
Hydrogen sodium and calcium, magnesium etc.) phenomenon.The salinization of soil also has a strong impact on the soil property of soil, influences crop growth, and different salt
Be adapted to alkali growth crops there is also difference.When saline Land refers to that soil salt content is too high (more than 0.3%), and make
Crops low yield can not grow.
Different plant growths, there is different demands to the ph values of the oxygen content of soil, soil moisture content and soil, because
This needs to carry out quick detection to soil, to judge arable plant.
The content of the invention
To solve the above problems, the present invention provides a kind of plant cover cultivation methods based on BP neural network.
The present invention provides following technical scheme：A kind of plant cover cultivation methods based on BP neural network, methods described include：
S1. the growth performance situation based on the plant transmitted in advance in controller, initial data is carried out using BP neural network
Analyzed and processed, obtain initial growth desirable value；
S2. the acid-base value of the soil detected based on acid-base value detector, detection data is passed to controller, utilize BP nerve nets
Network analyzes and processes to detection data, obtains environment measuring value x1；
S3. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P1；
S4. the ph of the soil detected based on ph detectors, detection data are passed to controller, using BP neural network to inspection
Survey data to be analyzed and processed, obtain environment measuring value x2；
S5. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P2；
S6. the salinity of the soil detected based on salinity measurement device, detection data is passed to controller, utilize BP neural network pair
Detection data are analyzed and processed, and obtain environment measuring value x3；
S7. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P3；
S8. kind P1, P2, P3 of suitable growing plants S3, S5, S7 step obtained are using BP neural network to testing number
According to being analyzed and processed, obtain being adapted to the growing plants kind P under these three environment.
Wherein, the discrepancy layer of BP neural network includes the growth performance situation of plant in the S1, and output layer is original life
Long desirable value；The discrepancy layer of BP neural network includes initial growth desirable value, environment measuring value x1 in the S3, and output layer is suitable
The plant variety P1 of symphysis length；The discrepancy layer of BP neural network includes initial growth desirable value, environment measuring value x2 in the S5,
Output layer is to be adapted to growing plants kind P2；The discrepancy layer of BP neural network includes initial growth desirable value, ring in the S7
Border detected value x3, output layer are to be adapted to growing plants kind P3；The layer that comes in and goes out of BP neural network includes growth in the S8
Plant variety P1, it is adapted to growing plants kind P2, is adapted to growing plants kind P3, output layer is to be adapted in these three environment
Lower growing plants kind P.
Further, BP neural network also includes 3 hidden layers in the S8, and the hidden layer includes being adapted to the plant of growth
Article kind P1, it is adapted to growing plants kind P2's to collect P12, is adapted to growing plants kind P1, is adapted to growing plants product
Kind P3's collects P13, is adapted to growing plants kind P2, is adapted to growing plants kind P3's to collect P23.
Further, the initial growth desirable value and environment measuring value x1, environment measuring value x2, environment measuring value x3 it
Between relative error be 0-0.1%.
Beneficial effects of the present invention：Detector passes through BP god quickly to the ph values of the acid-base value of soil, salinity and soil
Through network with judge be adapted to plant arable in the present context.
Embodiment
With reference to embodiment, the present invention is furture elucidated, it should be understood that following embodiments are only used for
The bright present invention rather than limitation the scope of the present invention.\
A kind of plant cover cultivation methods based on BP neural network, comprise the following steps,
S1. the growth performance situation based on the plant transmitted in advance in controller, initial data is carried out using BP neural network
Analyzed and processed, obtain initial growth desirable value；
S2. the acid-base value of the soil detected based on acid-base value detector, detection data is passed to controller, utilize BP nerve nets
Network analyzes and processes to detection data, obtains environment measuring value x1；
S3. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P1；
S4. the ph of the soil detected based on ph detectors, detection data are passed to controller, using BP neural network to inspection
Survey data to be analyzed and processed, obtain environment measuring value x2；
S5. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P2；
S6. the salinity of the soil detected based on salinity measurement device, detection data is passed to controller, utilize BP neural network pair
Detection data are analyzed and processed, and obtain environment measuring value x3；
S7. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P3；
S8. kind P1, P2, P3 of suitable growing plants S3, S5, S7 step obtained are using BP neural network to testing number
According to being analyzed and processed, obtain being adapted to the growing plants kind P under these three environment.
Wherein, the discrepancy layer of BP neural network includes the growth performance situation of plant in the S1, and output layer is original life
Long desirable value；The discrepancy layer of BP neural network includes initial growth desirable value, environment measuring value x1 in the S3, and output layer is suitable
The plant variety P1 of symphysis length；The discrepancy layer of BP neural network includes initial growth desirable value, environment measuring value x2 in the S5,
Output layer is to be adapted to growing plants kind P2；The discrepancy layer of BP neural network includes initial growth desirable value, ring in the S7
Border detected value x3, output layer are to be adapted to growing plants kind P3；The layer that comes in and goes out of BP neural network includes growth in the S8
Plant variety P1, it is adapted to growing plants kind P2, is adapted to growing plants kind P3, output layer is to be adapted in these three environment
Lower growing plants kind P.
Further, BP neural network also includes 3 hidden layers in the S8, and the hidden layer includes being adapted to the plant of growth
Article kind P1, it is adapted to growing plants kind P2's to collect P12, is adapted to growing plants kind P1, is adapted to growing plants product
Kind P3's collects P13, is adapted to growing plants kind P2, is adapted to growing plants kind P3's to collect P23.
Further, the initial growth desirable value and environment measuring value x1, environment measuring value x2, environment measuring value x3 it
Between relative error be 0-0.1%.When relative error is between 0-0.1%, exportable P1, P2, P3.
Technological means disclosed in the present invention program is not limited only to the technological means disclosed in above-mentioned embodiment, in addition to
Formed technical scheme is combined by above technical characteristic.It should be pointed out that for those skilled in the art
For, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (3)
1. a kind of plant cover cultivation methods based on BP neural network, it is characterised in that methods described includes：
S1. the growth performance situation based on the plant transmitted in advance in controller, initial data is carried out using BP neural network
Analyzed and processed, obtain initial growth desirable value；
S2. the acid-base value of the soil detected based on acid-base value detector, detection data is passed to controller, utilize BP nerve nets
Network analyzes and processes to detection data, obtains environment measuring value x1；
S3. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P1；
S4. the ph of the soil detected based on ph detectors, detection data are passed to controller, using BP neural network to inspection
Survey data to be analyzed and processed, obtain environment measuring value x2；
S5. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P2；
S6. the salinity of the soil detected based on salinity measurement device, detection data is passed to controller, utilize BP neural network pair
Detection data are analyzed and processed, and obtain environment measuring value x3；
S7. by environment measuring value compared with initial growth desirable value, it is adapted to growing plants kind in such circumstances
P3；
S8. kind P1, P2, P3 of suitable growing plants S3, S5, S7 step obtained are using BP neural network to testing number
According to being analyzed and processed, obtain being adapted to the growing plants kind P under these three environment,
Wherein, the discrepancy layer of BP neural network includes the growth performance situation of plant in the S1, and output layer is initial growth institute
Need to be worth；The discrepancy layer of BP neural network includes initial growth desirable value, environment measuring value x1 in the S3, and output layer is suitable life
Long plant variety P1；The discrepancy layer of BP neural network includes initial growth desirable value, environment measuring value x2, output in the S5
Layer is suitable growing plants kind P2；The discrepancy layer of BP neural network includes initial growth desirable value in the S7, environment is examined
Measured value x3, output layer are to be adapted to growing plants kind P3；The discrepancy layer of BP neural network includes growing plants in the S8
Kind P1, it is adapted to growing plants kind P2, is adapted to growing plants kind P3, output layer is to be adapted to the life under these three environment
Long plant variety P.
A kind of 2. plant cover cultivation methods based on BP neural network as claimed in claim 1, it is characterised in that BP in the S8
Neutral net also includes 3 hidden layers, and the hidden layer includes being adapted to growing plants kind P1, is adapted to growing plants kind
P2's collects P12, is adapted to growing plants kind P1, is adapted to growing plants kind P3's to collect P13, is adapted to growing plants
Kind P2, it is adapted to growing plants kind P3's to collect P23.
A kind of 3. plant cover cultivation methods based on BP neural network as claimed in claim 2, it is characterised in that the original life
Relative error between long desirable value and environment measuring value x1, environment measuring value x2, environment measuring value x3 is 0-0.1%.
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| CN111783946A (en) * | 2020-06-23 | 2020-10-16 | 珠海格力电器股份有限公司 | Plant nutrient automatic separation method and device based on image processing and electronic equipment |
| CN109359814B (en) * | 2018-09-06 | 2021-03-09 | 和辰（深圳）科技有限公司 | Soil composition information processing method, device, system, storage medium and equipment |
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