CN100468240C - System and method for monitoring breed of crop - Google Patents
System and method for monitoring breed of crop Download PDFInfo
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- CN100468240C CN100468240C CNB2006100307636A CN200610030763A CN100468240C CN 100468240 C CN100468240 C CN 100468240C CN B2006100307636 A CNB2006100307636 A CN B2006100307636A CN 200610030763 A CN200610030763 A CN 200610030763A CN 100468240 C CN100468240 C CN 100468240C
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Abstract
A method for monitoring culture of crops includes setting multigroup different culture conditions and sensing key control point data of corps under each culture condition, comparing key control point data according to growth state of each group corps under different culture condition for obtaining data threshold of each key control point for realizing to obtain data threshold automatically, analyzing said data to obtain relation of key control point to growth state of crops in order to realize effective control on growth state of crops.
Description
Technical field
The present invention relates to a kind of breed of crop supervisory system and method for supervising.
Background technology
Along with international community is more and more higher to the requirement of agricultural product quality, the security monitoring of agricultural product has become the focus that various countries pay close attention to.Now, for guaranteeing agricultural product security, Codex Committee on Food of the United Nations has adopted hazard, and (hazardanalysis critical control point, HACCP) technical system are brought the hierarchy of control of HACCP into the robotization cultivation of agricultural in the world especially.
With the breed of edible fungus is example, usually, the planting technique of edible fungi comprises that raw material mixes, bottling, sterilization, cooling, inoculation, cultivate, mycelium stimulation, urge flower bud, the sporophore growth and the packing multiple working procedure of gathering, China introduces the automation production flow line of agricultural the end of the nineties, bring into use the robotization filling machine, sterilizing equipment decontaminating apparatus, inoculating facility and artificial climate equipment etc. carry out batch production production agricultural operation, owing to start late, numerous manufacturing enterprises fail to carry out automatically-monitored to each critical control point that may influence edible fungi safety, usually still adopt the mode of manual detection manual record, and critical control point relates to the materials requirement, water safety and production environment condition, for example temperature, humidity, CO
2The water cut of concentration, materials, potential of hydrogen etc., and each key point all needs periodic record, cause the manual work amount various, very easily fail in time to handle to influence follow-up regulation and control especially to each critical control point because of the data of each critical control point, when serious even can influence the quality of edible fungi, bring potential safety hazard to the consumer.
Moreover, critical control point related in the HACCP system is many, and because China is vast in territory, various places climatic environment and geographical conditions are different, each agricultural is cultivated the merchant and is looked for the threshold value of each critical control point and all be considered as trade secret and will not disclose for having spent a large amount of manpower and materials early stage, cause each agricultural to cultivate the work that the merchant can repeat equally when looking for the threshold value of each critical control point, totally unfavorable in improving industry efficient, and because each pass keying point data is by artificial collection, process is very complicated, and very easily make mistakes, so also be easy to reduce the precision of the data threshold of each critical control point, simultaneously agricultural is cultivated the relation between data that the merchant also is difficult to obtain each critical control point and the agriculture upgrowth situation, therefore, want to improve agriculture upgrowth situation if agricultural is cultivated the merchant, only the data threshold with reference to resulting each critical control point is difficult to effectively regulate.
Therefore, how to solve the problems that exist in the existing agricultural cultivation and become the technical task that industry needs to be resolved hurrily in fact.
Summary of the invention
The object of the present invention is to provide a kind of breed of crop supervisory system and method for supervising, realize obtaining automatically the threshold value of each critical control point, can increase work efficiency by the data of automatic collection critical control point simultaneously, save human resources, and can improve agricultural producer's economic benefit by the analysis of data being realized effective control to the upgrowth situation of agricultural.
Reach other purposes in order to achieve the above object, the invention provides a kind of breed of crop supervisory system and method for supervising, wherein, described breed of crop supervisory system comprises at least: one is used for setting in agriculture growth course the control assembly of the different breeding condition of many groups; One is used for the sensing module of the data of corresponding crops each critical control point of sensing under the many groups breeding condition that sets; One is electrically connected with described sensing module, be used to receive the data of each critical control point that sensing module is sent to, and respectively organize the data of critical control point at the upgrowth situation of each group under breeding condition according to crops, with the computer monitoring module of the data threshold of determining critical control point.
Wherein, described sensing apparatus comprises a plurality of or whole in infrared moisture meter, PH meter, electronic scales and the thermometer, described control assembly comprises automated packing equipment, temperature control equipment and Humidity regulating equipment, described computer monitoring module also comprises and being used for according to obtaining many group critical control point data and corresponding agriculture upgrowth situation, analyze by artificial neural network technology, obtain the critical control point analysis module of relation of critical control point and the upgrowth situation of agricultural.
This breed of crop method for supervising comprises step: 1) set many group breeding conditions by control assembly in plant growing process; 2) data of the corresponding crops critical control point of sensing under the many groups breeding condition that sets; And 3) receive the data of each critical control point be sent to, and respectively organize the data of critical control point, to determine the data threshold of critical control point according to the upgrowth situation of crops under each group breeding condition.
Wherein, after described step 3), also comprise step: 4) according to obtaining many group critical control point data and corresponding agriculture upgrowth situation, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and agricultural, described step 2) institute's sensed data comprises bottling water cut, bottling pH value, bottling average weight, reserve temperature and mycelium stimulation pH value at least in, and the breeding condition that described control assembly sets comprises temperature, humidity and materials water cut.
In sum, breed of crop supervisory system of the present invention and method for supervising are the data of obtaining each critical control point of agricultural cultivation by sensing module, realize the data automatic collecting of critical control point, increase work efficiency, save human resources, and can obtain the relation of the upgrowth situation of the data threshold of each critical control point and each critical control point and crops by analysis to the crop growth situation, realization is to effective control of the upgrowth situation of crops, and can obtain the data threshold of each critical control point automatically.
Description of drawings
Fig. 1 is the structural representation of breed of crop supervisory system of the present invention.
Fig. 2 is the operating process synoptic diagram of breed of crop method for supervising of the present invention.
Embodiment
See also Fig. 1, the invention provides a kind of breed of crop supervisory system 1, wherein, in breed of crop, have a plurality of critical control point that influence crops safety, health and quality, these critical control point are by materials require hazard analysis and critical control point technical system relating to of being proposed, the critical control point of water safety and working condition etc., in the present embodiment, the cultivation with edible fungi is that example describes.The critical control point in the edible fungi robotization cultivating process that requires according to the HACCP system comprises raw material year censorship situation, bottling water cut, bottling pH value, bottling average weight, whipping temp, sterilization time, the anti-pollution rate of cultivation, mycelium stimulation water cut, mycelium stimulation pH value, urges flower bud the 9th day sense organ sensing value, knurl lid mushroom incidence, storage temperature, new wind to filter situation, colony growth situation and regularity etc. totally 15 (as shown in table 1 below).
Table 1 critical control point signal table
| Sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| The critical control point title | Raw material year censorship situation | The bottling water cut | The bottling pH value | The bottling average weight | Whipping temp | Sterilization time | Cultivate the room pollution rate | The mycelium stimulation water cut |
| Sequence number | 9 | 10 | 11 | 12 | 13 | 14 | 15 | |
| The critical control point title | Mycelium stimulation PH value | Urge the 9th day sense organ sensing value of flower bud | Knurl lid mushroom incidence | Reserve temperature | New wind filters situation | The colony growth situation | Regularity (the 16th day whole fruiting situation of mycelium stimulation) |
Described breed of crop supervisory system 1 comprises at least: a control assembly 11, a computer monitoring module 12 and a sensing module 13 (annexation of aforementioned each assembly is as shown in Figure 1), below will be described in detail aforementioned each assembly.
Described control assembly 11 is to set many group breeding conditions in plant growing process, in breed of edible fungus, described control assembly 11 comprises the robotization filling machine at least, temperature control equipment and Humidity regulating equipment, wherein, temperature control equipment and Humidity regulating equipment are replaced by artificial climate equipment, so, described control assembly 11 can be set the temperature in the breed of edible fungus process, humidity, bottling general assembly (TW) and bottling water cut, it is noted that, described control assembly 11 is not to exceed with present embodiment, it can select distinct device as required, for example can comprise also that sterilizing equipment decontaminating apparatus pollutes control etc.
Described sensing module 13 is to be electrically connected with described computer monitoring module 12, real data in order to corresponding each critical control point of sensing under many group breeding conditions, in breed of edible fungus, described sensing module 13 comprises gas chromatograph and the atomic fluorescence spectrophotometer that is used to test raw material year censorship situation, the infrared moisture meter that is used for sensing bottling water cut and mycelium stimulation water cut, be used to test the PH meter of bottling pH value, be used to test the electronic scales of bottling average weight, be used to test the automatically-monitored instrument of machine filial generation of sterilization time, be used to test the PH meter of mycelium stimulation pH value, the mercury temperature that is used to test storage temperature is taken into account and is used to test the machine open frequency proving installation that new wind filters situation, what must please note is, sensing module 13 set sensing apparatus are not as limit, and the user can select needed sensing apparatus according to practical situation.
Described computer monitoring module 12 is electrically connected with described sensing module 13, be used to receive the data of each critical control point that sensing module 13 is sent to, and respectively organize the data of critical control point at the upgrowth situation of each group under breeding condition according to crops, to determine the data threshold of critical control point, it also comprises a critical control point analysis module 121, be used for according to obtaining many group critical control point data and corresponding agriculture upgrowth situation data, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and agricultural.In breed of edible fungus, computer monitoring module 12 has received the data of 8 groups of critical control point, and obtain the upgrowth situation data of 8 groups of edible fungis under the breeding condition, embody with the edible fungi output (kilogram) and the quality of per 75 kilograms of composts, as shown in table 2 below:
Show output and the quality record table of 2:1-8 group of critical control point data and edible fungi
| Group | The critical control point value | Output | Quality |
| 1 | Bottling water cut 63%, 118 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45 | 1 grade of product |
| 2 | Bottling water cut 63%, 120 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.1 | 1 grade of product |
| 3 | Bottling water cut 65%, 121 ℃ of whipping temps, bottled general assembly (TW) 640, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.2 | 1 grade of product |
| 4 | Bottling water cut 64%, 120 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.2 | 1 grade of product |
| 5 | Bottling water cut 64%, 118 ℃ of whipping temps, bottling general assembly (TW) 640, sterilization time 9 minutes, raw material year censorship situation is excellent ... | 45.2 | 1 grade of product |
| 6 | Bottling water cut 65%, 118 ℃ of whipping temps, bottling general assembly (TW) 625, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45 | 1 grade of product |
| 7 | Bottling water cut 65%, 118 ℃ of whipping temps, bottling general assembly (TW) 620, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 42 | 1 grade of product |
| 8 | Bottling water cut 61%, 116 ℃ of whipping temps, bottling general assembly (TW) 630, sterilization time 10 minutes, raw material year censorship situation is excellent ... | 45.1 | 1 grade of product |
Data according to the resulting edible fungi growth situation of last table 2, computer monitoring module 12 compares each group edible fungi output and qualitative data thinks that output is about 45 kilograms, quality is that the upgrowth situation of edible fungi of 1 grade of product is preferable, so can obtain the data of each critical control point, i.e. data of 1-6 group and the 8th group of critical control point in the table 2, and can obtain corresponding data threshold according to the data of these light key control points, the threshold value of the water cut of promptly bottling is 61%-65%, the whipping temp threshold value is 116 ℃-121 ℃, bottling general assembly (TW) threshold value is 625-630, the sterilization time threshold value is 9-10 minute or the like, it is noted that, because overabundance of data, be simplified illustration, omitted the partial data in the data of aforesaid 15 critical control point in the table 2, but these data all can obtain by corresponding apparatus, correspondingly, the data threshold of these critical control point also can obtain after comparing through computer monitoring module 12, in addition, critical control point analysis module 121 is according to resulting 8 groups of data, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and edible fungi, before analyzing with artificial neural network technology, computer monitoring module 12 need be carried out pre-service to 8 groups of data that obtained, be about to these data that can not quantize and carry out coded quantization, and make its data value [0 through handling each data, 1] within the scope, usually the coded quantization method is for to be divided into 3 grades respectively with corresponding data, each grade correspondence one numerical value, for example be 0.7,0.4,0.1, if variation has taken place in a certain data, then on its developing direction, increase by 0.01, if deep variation has taken place, then increase by 1 grade on the original basis, and the quality of product can be set respective level to the requirement of quality according to manufacturer, in the present embodiment, quality is divided into 3 grades, i.e. 1 grade of product, 2 grades of product and 3 grades of product, use numerical value 0 respectively, 0.5,1 represents, after computer monitoring module 12 is with aforementioned 15 data process quantification treatment, can obtain the critical control point data set of 1-8 group:
In following formula: every group of 15 critical control point respective value of each row representative, each row is represented the respective value of certain reference mark in each group, and as input matrix, corresponding output matrix then is with following formula:
Wherein, Y
1Represent the output of edible fungi, Y
2Represent the quality of edible fungi.
Critical control point analysis module 121 with the data sample of 1-7 group as learning sample, the 8th group data sample is as test samples, and the input layer of setting neural network is 15, output neuron is 2, and each training parameter of setting the BP network is: the number of hidden neuron is 16, and the margin of error is 0.001, initial weight between each layer of network and each layer threshold value are [1,1] Nei value immediately, the study step-length is 0.8, maximum study number of times is 2000 times.
At first import the 1st group of sample, begin training, training step is as follows:
(1) calculates j neuronic output a1 in the hidden layer
j, adopt logarithm saturability (Logsig) mapping function.
(2) calculate output layer output.Adopt linear (purelin) transforming function transformation function, the output of hidden neuron as the output layer nerve.The input of unit can get the neuronic output of output layer a2.
(3) error of calculation value is established y
(i)Through normalized is y
1', this is to obtain square mean error amount:
If error amount less than the margin of error, changes (5) over to; If error amount greater than the margin of error, carries out next step, begin to adjust weights.
(4) adjust weights and threshold value, at first adjust connection weights and threshold value between output layer and the hidden layer, adjust then and be connected weights and threshold value between hidden layer and the input layer.
(5) train next group sample, import next group sample, change (1) over to
(6) recording learning number of times, if less than preset value 2000, training finishes, if greater than preset value, the signature failure to train is adjusted each adjustable parameter, changes (1) over to and trains again.
After with preceding 7 samples network being learnt, the connection weight value matrix between input layer and the hidden layer:
The threshold matrix of hidden neuron:
θ1
10×1＝[0.0540.317....1.783]
T
Connection weight value matrix between hidden layer and the output layer:
W2
1×10＝[4.2570.884....5.763]
The neuronic threshold value of output layer:
θ2＝1.097
Again the input quantity X in the 8th group of sample
(8)Be input in the network, obtain test value:
Y
(8) 1＝41.2，Y
(8) 2＝0.2
Close with the 8th group of edible fungi output and quality, the proof prediction accurately, at this moment, critical control point analysis module 121 promptly generates each critical control point and agriculture upgrowth situation relational model, according to the relational model of being set up as can be known, if the data of each critical control point do not change, the output of then cultivating resulting corresponding edible fungi under similarity condition still can remain on about 45 kilograms, but if wish the output of edible fungi is improved 10 kilograms according to the demand in market, promptly reach 55 kilograms, then critical control point analysis module 121 is by the analysis to each critical control point data, the elder generation trial will bottle general assembly (TW) be arranged on 660-665 between the variation, then, as shown in table 3 below according to measurable output and the quality that goes out corresponding 4 groups of edible fungis of the output of resulting edible fungi and the relational model between quality and each critical control point:
Table 3:4 group critical control point value variation prediction table
| Group | The critical control point value | Output | Quality |
| 9 | Bottling water cut 63%, 118 ℃ of whipping temps, bottling general assembly (TW) 660, sterilization time 10 minutes ... | 51 | 1 grade of product |
| 10 | Bottling water cut 64%, 120 ℃ of whipping temps, bottling general assembly (TW) 662, sterilization time 10 minutes ... | 51.1 | 1 grade of |
| 11 | Bottling water cut 64%, 121 ℃ of whipping temps, bottling general assembly (TW) 663, sterilization time 10 minutes ... | 51.3 | 1 grade of |
| 12 | Bottling water cut 65%, 120 ℃ of whipping temps, bottling general assembly (TW) 665, sterilization time 10 minutes ... | 50.2 | 1 grade of product |
As seen from the above table, change between only the bottling general assembly (TW) being arranged on 660-665, then the output predicted of critical control point analysis module 121 has only improved about 6 kilograms, be about 51 kilograms, therefore also undesirable, therefore, further adjust the value of bottling water cut again, it is changed between 58%-60%, and the value input artificial nerve network model with this bottling water cut then can obtain output of corresponding edible fungi and the value of quality (as shown in table 4 below):
Table 4:4 group critical control point value variation prediction table
| Group | The critical control point value | Output | Quality |
| 9 | Bottling water cut 60%, 118 ℃ of whipping temps, bottling general assembly (TW) 660, sterilization time 10 minutes ... | 55 | 1 grade of product |
| 10 | Bottling water cut 59%, 120 ℃ of whipping temps, bottling general assembly (TW) 662, sterilization time 10 minutes ... | 55.1 | 1 grade of |
| 11 | Bottling water cut 58%, 121 ℃ of whipping temps, bottling general assembly (TW) 663, sterilization time 10 minutes ... | 54.8 | 1 grade of |
| 12 | Bottling water cut 60%, 120 ℃ of whipping temps, bottling general assembly (TW) 665, sterilization time 10 minutes ... | 55.3 | 1 grade of product |
Thus, analyzing back acquisition bottling general assembly (TW) threshold value through critical control point analysis module 121 is [660,665] and bottling water cut threshold value be [58%, 60%], other critical control point threshold values do not change, control assembly 11 can improve the upgrowth situation of edible fungi according to resulting data threshold respective settings breeding condition, and the output of edible fungi is improved 10 kilograms.
See also Fig. 2 again, it is the operating process synoptic diagram of breed of crop method for supervising of the present invention, execution in step S10 at first, described control assembly 11 is set the breeding condition of many groups in the breed of edible fungus process, for example, set the raw material bottling general assembly (TW) of edible fungi and the data of 2 critical control point of bottling water cut by regulating the robotization filling machine, set the data of the critical control point of the cultivation temperature of edible fungi and humidity by regulating artificial climate device, then proceed to step S11.
In step S11, the real data of each critical control point of sensing apparatus 13 sensings under many groups that set different breeding conditions, it comprises raw material year censorship situation, bottling water cut, bottling pH value, bottling average weight, whipping temp, sterilization time, the anti-pollution rate of cultivation, mycelium stimulation water cut, mycelium stimulation pH value, urges flower bud the 9th day sense organ sensing value, knurl lid mushroom incidence, storage temperature, new wind to filter 15 critical control point data (as above shown in the table 1) such as situation, colony growth situation and regularity, then proceeds to step S12.
In step S12, the data of each critical control point that computer monitoring module 12 reception sensing modules 13 are sent to, respectively organize the data of critical control point according to edible fungi at the upgrowth situation of each group under breeding condition,, then proceed to step S13 to determine the data threshold of critical control point.
In step S13, critical control point analysis module 121 is according to obtaining many group critical control point data and corresponding edible fungi growth status data, analyze by artificial neural network technology, obtain the relational model of the upgrowth situation of critical control point and edible fungi, and can carry out correlation predictive according to this relational model, for example, under existing critical control point condition, the output of edible fungi is 45 kilograms, relation according to the upgrowth situation of resulting critical control point and edible fungi, as want to make output to improve 10 kilograms, then critical control point analysis module 121 can analyze corresponding critical control point data threshold, sets corresponding breeding condition according to the data threshold control assembly 11 that is analyzed and can improve the upgrowth situation of edible fungi.
It is noted that, the present invention is not limited in the cultivation that is used in edible fungi, also can be used in the cultivation of other crops, for example being used for booth vegetable cultivates, just be used for other breed of crop, control assembly and sensing module can be chosen according to actual needs accordingly, but not exceed with all kinds of opertaing devices and sensing apparatus described in the present embodiment, with the cucumber is example, be used for the cultivation of cucumber as the present invention, accordingly, the critical control point that relates to cultivating cucumber comprises soil property, the selection of seed, pesticidal preparations and concentration, irrigation water water quality, humiture, the degree of becoming thoroughly decomposed of fertilizer, the corresponding sense module then comprises the soil property detector of testing soil property, the agricultural residual detector of test residue of pesticide, the thermometer of probe temperature, the humidity of testing humidity is taken into account the water quality testing meter of sensing irrigation water water quality etc., and the control device that is used to be provided with the cultivating cucumber condition then comprises temperature control equipment, Humidity regulating equipment, irrigation equipment etc.
Following table 5 is listed the title of multiple kinds of crops and corresponding critical control point in detail, and following table 6 is listed the sensing apparatus of each critical control point and each critical control point data of sensing in detail, and the present invention goes for other crops in order to supplementary notes.
Table 5: the crops and the corresponding critical control point table of comparisons
| Cultivar name | Close keying point 1 | Close keying point 2 | Close keying point 3 | Close keying point 4 | Close keying point 5 | Close keying point 6 | Close keying point 7 |
| Cucumber | Soil property | Seed is selected (accumulation ability of agricultural chemicals and nitrate) | Pesticidal preparations and concentration | Irrigation water water quality | Humiture | The degree of becoming thoroughly decomposed of fertilizer | |
| Wild rice stem | Water quality | Pesticidal preparations and concentration | Humiture | Storage period prevention of damage by disease | |||
| Little green vegetables | Soil property | Seed is selected (diseases and insect pests resistance) | Pesticidal preparations and concentration | The degree of becoming thoroughly decomposed of fertilizer | Transplanting phase seedling is selected | Irrigation water water quality | Humiture |
| Chinese cabbage | Soil property | Seed is selected (diseases and insect pests resistance) | Seedling stage humiture | Transplanting phase seedling is selected | Pesticidal preparations and concentration | Storage period prevention of damage by disease | |
| Tomato | Soil property | Seed is selected (disease and insect resistance, anti-adversity ability) | Seedling raise period | Pesticidal preparations and concentration | Irrigation volume behind first fringe fruit setting | Become the strain phase incidence of disease | Storage period prevention of damage by disease |
| Cauliflower | Soil property | Seed is selected (diseases and insect pests resistance) | Seed soaking time | Prevention and control of plant diseases, pest control mode (chemical prevention, biological control, physical control) | Seedling stage humiture | Pesticidal preparations and concentration | Storage period prevention of damage by disease |
Table 6: the table of comparisons of each critical control point and corresponding sensing apparatus
| Closing the keying roll-call claims | Soil property | Water quality | Agricultural chemicals | Humiture |
| The device of each critical control point of sensing | The soil detector | Water quality testing meter | The residual detector of farming | The thermometer hygrometer |
In sum, the present invention's breed of crop supervisory system and method for supervising are by setting many group breeding conditions, and sense each critical control point data and the upgrowth situation data of crops under corresponding breeding condition under the corresponding breeding condition, these data are analyzed to obtain the data threshold of each critical control point, realize the automatic acquisition of critical control point data threshold, can increase work efficiency by the data of automatic collection critical control point simultaneously, save human resources, moreover, can obtain the upgrowth situation of crops and the relational model of critical control point by data analysis, can realize improvement by this relational model, improve the economic benefit of breed of crop the crop growth situation.
Claims (8)
1. breed of crop supervisory system is characterized in that comprising:
Control assembly is used for setting many group breeding conditions at plant growing process;
Sensing module is used for the data of the corresponding crops critical control point of sensing under many groups breeding condition that described control assembly is set;
The computer monitoring module, be electrically connected with described sensing module, be used to receive the data of each critical control point that sensing module is sent to, and respectively organize the data of critical control point, to determine the data threshold of critical control point according to the upgrowth situation of edible fungi under each group breeding condition;
Described computer monitoring module also comprises the critical control point analysis module, be used for according to obtaining many group critical control point data and corresponding crop growth situation, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and crops;
The computer monitoring module receives the data of some groups of critical control point, and is respectively organized the upgrowth situation data of the edible fungi under the breeding condition;
The critical control point analysis module with one group data sample in the described some groups data sample as test samples, other data samples are as learning sample, and set input layer number, the output neuron number of neural network, set each training parameter of BP network simultaneously.
2. breed of crop supervisory system as claimed in claim 1 is characterized in that: described sensing module comprises a plurality of or whole in infrared moisture meter, PH meter, electronic scales and the thermometer.
3. breed of crop supervisory system as claimed in claim 1, it is characterized in that: the input layer that described critical control point analysis module is set neural network is 15, output neuron is 2, each training parameter of setting the BP network is: the number of hidden neuron is 16, the margin of error is 0.001, and the initial weight between each layer of network and each layer threshold value are the value immediately in [1,1], the study step-length is 0.8, and maximum study number of times is 2000 times.
4. breed of crop supervisory system as claimed in claim 1 is characterized in that: described control assembly comprises automatic filling machine, temperature control equipment and Humidity regulating equipment.
5. a breed of crop method for supervising adopts as right 1 to 4 arbitrary described breed of crop supervisory system, it is characterized in that may further comprise the steps:
1) in plant growing process, sets many group breeding conditions by control assembly;
2) data of the corresponding crops critical control point of sensing under the many groups breeding condition that sets;
3) receive the data of each critical control point be sent to, relatively the upgrowth situation data of crops under the many groups breeding condition that sets are selected the data of the critical control point of corresponding crops, to determine the data threshold of critical control point; Described step 3) comprises following training step:
(1) calculates j neuronic output a1j in the hidden layer, adopt logarithm saturability mapping function;
(2) calculate output layer output; Adopt the linear transformations function, as the neuronic input of output layer, can get the neuronic output of output layer a2 to the output of hidden neuron;
(3) error of calculation value, establishing y (i) is y1 ' through normalized, this is to obtain square mean error amount:
If error amount less than the margin of error, changes step (5) over to; If error amount greater than the margin of error, enters step (4), begin to adjust weights;
(4) adjust weights and threshold value, at first adjust connection weights and threshold value between output layer and the hidden layer, adjust then and be connected weights and threshold value between hidden layer and the input layer;
(5) train next group sample, import next group sample, change step (1) over to; If as sample, change step (6) over to;
(6) recording learning number of times, if less than preset value, training finishes, if greater than preset value, the signature failure to train is adjusted each adjustable parameter, changes step (1) over to and trains again.
6. breed of crop method for supervising as claimed in claim 5 is characterized in that: described step 2) institute's sensed data comprises bottling water cut, bottling pH value, bottling average weight, reserve temperature and mycelium stimulation pH value.
7. breed of crop method for supervising as claimed in claim 5, it is characterized in that after described step 3), also comprising step: 4) according to obtaining many group critical control point data and corresponding crop growth situation, analyze by artificial neural network technology, obtain the relation of the upgrowth situation of critical control point and crops.
8. breed of crop method for supervising as claimed in claim 5 is characterized in that: the breeding condition that described control assembly sets comprises temperature, humidity and materials water cut.
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| CN101980249B (en) * | 2010-11-12 | 2012-11-07 | 中国气象局气象探测中心 | Automatic observation method and device for crop development and growth |
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| CN105812739B (en) * | 2016-03-23 | 2019-07-30 | 中国农业大学 | A kind of system and method for automatic collection plant growth information |
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