CN109508824A - A kind of detection of crop growth situation and yield predictor method - Google Patents
A kind of detection of crop growth situation and yield predictor method Download PDFInfo
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Abstract
A kind of detection of crop growth situation and yield predictor method, union feature vector data library is constructed including constructing the agriculture Internet of things system for crop growth environment information, own physiological physicochemical data and Image Acquisition and data prediction, then by preprocessed data and historical data；By constructing the crop yield prediction model based on sparse depth belief network, the model includes that an input layer, the sparse limited Boltzmann machine model of multilayer and a backpropagation neural network are constituted, pre-training and fine tuning are carried out to crop yield prediction model using the union feature vector in contrast difference's fast learning algorithm and database, obtain optimum parameter value, prediction model is tested with the testing data in database again, repetition training；Finally crop yield is estimated according to BP network output valve；The present invention has to acquire effective field management measure in time, the advantages of accurately being estimated to crop yield.
Description
Technical field
The invention belongs to the field of applying IT extensively to agricultural development, it is related to a kind of crop growth situation detection and yield predictor method.
Background technique
With the rapid development of Internet of Things and big data technology, wisdom agricultural has been the important composition portion of wisdom economy
Point.China is large agricultural country, and non-agricultural power, therefore to realize high-caliber industrialized agriculture production and optimization facility biocycle
Border control, agriculture relevant information obtains, analytical technology is one of technology of most critical in agricultural production.
Currently, in IT application to agriculture mainly by the sensor probe of different function obtain environmental factor such as temperature and humidity,
Illuminance, CO2Concentration, wind-force, wind direction, soil moisture etc., and integrated management is carried out by computer, implement the processes such as liquid manure oneself
Dynamic control.However, any single-sensor data all have the limitation of certain application range, it can not reflect crop comprehensively
Physiological and biochemical property.Internet of Things provides important technology guarantee for the acquisition of multi-source information in agricultural production process.Multi-source information
Provided data have redundancy, complementarity and cooperative, are multi-source information by the message complementary sense between different data sources
Processing, analysis provide most effective application, maximally utilise the information that crops multi-source data is included and judge agriculture in time
The real-time growth of cereal crop seedlings, liquid manure and the disease pest and weed situation of crop, provide and in time may be used for crop production management personnel or management decision-maker
The growth information leaned on accurately estimates crop yield convenient for acquiring effective field management measure in time, is Chinese people
The life condition and grain security of the people provides safeguard.
Summary of the invention
In order to overcome the above-mentioned deficiencies of the prior art, invention provides a kind of detection of crop growth situation and yield is estimated
Method improves predicting reliability and precision, by excluding artificial subjective impact to realize the detection of crop growth situation and producing
It measures the information system management estimated and certain theoretical foundation and technical support is provided.
In order to achieve the above object, the technical solution adopted by the present invention are as follows:
A kind of detection of crop growth situation and yield predictor method, the steps include:
Step 1: crop growth environment information collection and pretreatment obtain environmental information；
Step 2: the acquisition of crops own physiological physicochemical data and pretreatment obtain physiological and biochemical property vector；
Step 3: the acquisition of crops leaf image and feature extraction: crops leaf is acquired in real time using video monitoring equipment
Picture, then extracts mean value, deviation and the third-order matrix of tri- components of R, G, B of figure, then is normalized, and obtains
Color of image feature；
Step 4: Environmental Information Feature, physiological and biochemical property feature vector and the image that step 1 to step 3 is obtained
Color feature value forms the union feature vector of farming growth, is advised according to the historical data of crop growth and yield and variation
Rule constructs the union feature vector data library of crop growth situation and yield；
Step 5: crop growth situation detection and yield prediction model of the building based on sparse depth belief network；
Step 6: contrast difference's fast learning algorithm and building crop growth situation obtained in step 4 and production are utilized
The union feature vector data library of amount, crop yield prediction model carry out pre-training and fine tuning, obtain Production Forecast Models；
Step 7: reliability test: building crop growth situation obtained in step 4 is carried out to Production Forecast Models
And union feature vector to be tested is input in step 6 training and has finely tuned in the union feature vector data library of yield
In Production Forecast Models, crop yield estimation results are calculated, estimation results are compared with actual production, if estimation results
Greater than actual production, then step 6 again is needed, adjustment is optimized to crop yield prediction model, repetition training yield is pre-
Survey model.
Further, the crop growth environment information collection and pretreatment: the biography of multiple types and function is laid
Sensor acquires the acquisition of crop growth environment, mainly includes 1) meteorological condition relevant to crop growth, including air pressure, gas
Temperature, relative humidity, illuminance, lighting delay number, photosynthetically active radiation, overcast and rainy days, rainfall, wind speed, wind direction, frost-free period；2)
Related soil data include the soil texture, bulk density, aggregate, pH value, nitrogen, phosphorus, potassium, organic matter, topsoil thickness, microorganism and in
Microelement；3) hydrologic regime includes river title, water level, flow, the flow of water and rainfall；4) Pesticide use situation include type,
Dosage and number；Above-mentioned data are subjected to grade classification or normalized, obtain Environmental Information Feature.
Further, crops own physiological physicochemical data acquisition and pretreatment: growing way and production with crops
Measure relevant physiology, biochemical indicator includes dielectric property, leaf area index, chlorophyll content, blade N-P-K content；It will be above-mentioned
Data carry out grade classification normalized, obtain crops own physiological biochemical character.
Further, crop growth situation detection and yield prediction model are sparse by input layer, a multilayer
Limited Boltzmann machine model and a backpropagation neural network are constituted, and each layer captures height from upper one layer of hidden unit
Relevant association；The backpropagation neural network is made of input layer, hidden layer and output layer neuron.
Further, utilization contrast difference's fast learning algorithm and the building crop growth obtained in step 4
The union feature vector data library of situation and yield, crop yield prediction model carry out the steps include: for pre-training
1, each sparse limited Boltzmann machine model is trained to obtain the connection weight of visual layers and hiding interlayer layer by layer
Value repeats n times, until obtaining optimum parameter value；
2, pre-training model parameter determine after, in order to make model have better character representation ability, by n-th layer it is sparse by
Input of the output of Boltzmann machine model as backpropagation neural network is limited, utilizes conjugate gradient method with the data of tape label
The differentiation performance of model is optimized and revised, the output of BP network is that crop yield estimates situation.
Beneficial effects of the present invention:
The present invention passes through environmental information, physiological and biochemical property vector sum color of image feature, the joint of composition farming growth
Feature vector constructs the detection of crop growth situation and yield prediction model based on sparse depth belief network, arranges as far as possible
Except artificial subjective impact, predicting reliability and accuracy are improved, to realize letter that the detection of crop growth situation and yield are estimated
Breathization management provides certain theoretical foundation and technical support.
Detailed description of the invention
Fig. 1 is sparse depth belief network schematic diagram of the present invention.
Specific embodiment
1 the present invention is described in detail with reference to the accompanying drawing.
A kind of detection of crop growth situation and yield predictor method, the steps include:
Step 1: crop growth environment information collection and pretreatment: the sensor acquisition of multiple types and function is laid
The acquisition of crop growth environment, the concrete type of sensor, model, quantity are according to the cultivated area and feature of practical crops
Selected, mainly include 1) meteorological condition relevant to crop growth, including air pressure, temperature, relative humidity, illuminance,
Lighting delay number, photosynthetically active radiation, overcast and rainy days, rainfall, wind speed, wind direction, frost-free period etc.；2) related soil data include soil
Loamy texture, bulk density, aggregate, pH value, nitrogen, phosphorus, potassium, organic matter, topsoil thickness, microorganism and middle microelement etc.；3) hydrology
Situation includes river title, water level, flow, the flow of water and rainfall etc.；4) Pesticide use situation includes type, dosage and number etc..
Above-mentioned data are subjected to grade classification or normalized, obtain Environmental Information Feature.
Step 2: the acquisition of crops own physiological physicochemical data and pretreatment: relevant to the growing way of crops and yield
Physiology, biochemical indicator include dielectric property (conductance, capacitor etc.), leaf area index, chlorophyll content, blade N-P-K content
Deng.Above-mentioned data are subjected to grade classification normalized, obtain crops own physiological biochemical character.
Step 3: the acquisition of crops leaf image and feature extraction: crops leaf is acquired in real time using video monitoring equipment
Picture, then extracts mean value, deviation and the third-order matrix of tri- components of R, G, B of figure, then is normalized, and obtains
Color of image feature.
Step 4: Environmental Information Feature, physiological and biochemical property feature vector and the image that step 1 to step 3 is obtained
Color feature value forms the union feature vector of farming growth, and then according to crop growth and the historical data and variation of yield
Rule constructs the union feature vector data library of crop growth situation and yield.
Step 5: crop growth situation detection and yield prediction model of the building based on sparse depth belief network；Ginseng
According to shown in Fig. 1, the detection of crop growth situation and yield prediction model are by an input layer, the sparse limited Boltzmann machine of multilayer
Model and a backpropagation neural network are constituted, and each layer captures highly relevant association from upper one layer of hidden unit；Institute
The backpropagation neural network stated is made of input layer, hidden layer and output layer neuron.
Step 6: pre- to crop yield using the union feature vector in contrast difference's fast learning algorithm and database
Estimate model and carry out pre-training and fine tuning, obtains Production Forecast Models, specific training step is as follows:
1, each sparse limited Boltzmann machine model is trained to obtain the connection weight of visual layers and hiding interlayer layer by layer
Value repeats n times, until obtaining optimum parameter value；
2, pre-training model parameter determine after, in order to make model have better character representation ability, by n-th layer it is sparse by
Input of the output of Boltzmann machine model as backpropagation neural network is limited, utilizes conjugate gradient method with the data of tape label
The differentiation performance of model is optimized and revised, the output of BP network is that crop yield estimates situation.
Step 7: carrying out reliability test to Production Forecast Models, and union feature vector to be tested in database is defeated
Enter to trained Production Forecast Models, calculate crop yield estimation results, estimation results are compared with actual production,
If estimation results are greater than actual production, step 6 optimizes adjustment to prediction model structure and data, and repetition training yield is pre-
Survey model.
Claims (5)
1. a kind of crop growth situation detection and yield predictor method, which is characterized in that the steps include:
Step 1: crop growth environment information collection and pretreatment obtain environmental information；
Step 2: the acquisition of crops own physiological physicochemical data and pretreatment obtain physiological and biochemical property vector；
Step 3: the acquisition of crops leaf image and feature extraction: crops blade figure is acquired in real time using video monitoring equipment
Picture, then extracts mean value, deviation and the third-order matrix of tri- components of R, G, B of figure, then is normalized, and obtains image
Color characteristic；
Step 4: Environmental Information Feature, physiological and biochemical property feature vector and the color of image that step 1 to step 3 is obtained
The union feature vector that eigenvalue cluster is grown at farming, according to crop growth and the historical data and changing rule of yield, structure
Build the union feature vector data library of crop growth situation and yield；
Step 5: crop growth situation detection and yield prediction model of the building based on sparse depth belief network；
Step 6: contrast difference's fast learning algorithm and building crop growth situation obtained in step 4 and yield are utilized
Union feature vector data library, crop yield prediction model carry out pre-training and fine tuning, obtain Production Forecast Models；
Step 7: reliability test: building crop growth situation obtained in step 4 is carried out to Production Forecast Models are obtained
And union feature vector to be tested is input in step 6 training and has finely tuned in the union feature vector data library of yield
In Production Forecast Models, crop yield estimation results are calculated, estimation results are compared with actual production, if estimation results
It is bigger than normal then to need step 6 again in actual production, adjustment is optimized to crop yield prediction model, repetition training obtains
Production Forecast Models.
2. a kind of crop growth situation detection according to claim 1 and yield predictor method, which is characterized in that described
Crop growth environment information collection and pretreatment: the sensor for laying multiple types and function acquires crop growth environment
Acquisition, mainly include 1) meteorological condition relevant to crop growth, including air pressure, temperature, relative humidity, illuminance, light
According to when number, photosynthetically active radiation, overcast and rainy days, rainfall, wind speed, wind direction, frost-free period；2) related soil data include soil matter
Ground, bulk density, aggregate, pH value, nitrogen, phosphorus, potassium, organic matter, topsoil thickness, microorganism and middle microelement；3) hydrologic regime packet
Include river title, water level, flow, the flow of water and rainfall；4) Pesticide use situation includes type, dosage and number；By above-mentioned data
Grade classification or normalized are carried out, Environmental Information Feature is obtained.
3. a kind of crop growth situation detection according to claim 1 and yield predictor method, which is characterized in that described
Crops own physiological physicochemical data acquisition and pretreatment: physiology relevant to the growing way of crops and yield, biochemical indicator
Including dielectric property, leaf area index, chlorophyll content, blade N-P-K content；Above-mentioned data are subjected to grade classification normalizing
Change processing, obtains crops own physiological biochemical character.
4. a kind of crop growth situation detection according to claim 1 and yield predictor method, which is characterized in that described
The detection of crop growth situation and the sparse limited Boltzmann machine model of one input layer of yield prediction model, multilayer and one
Backpropagation neural network is constituted, and each layer captures highly relevant association from upper one layer of hidden unit；The backward biography
Neural network is broadcast to be made of input layer, hidden layer and output layer neuron.
5. a kind of crop growth situation detection according to claim 1 and yield predictor method, which is characterized in that described
Utilize contrast difference's fast learning algorithm and it is obtained in step 4 building crop growth situation and yield union feature
Vector data library, crop yield prediction model carry out the steps include: for pre-training
1) each sparse limited Boltzmann machine model, is trained to obtain the connection weight weight values of visual layers and hiding interlayer layer by layer,
N times are repeated, until obtaining optimum parameter value；
2) it is in order to make model that there is better character representation ability, n-th layer is sparse limited after, pre-training model parameter determines
Input of the output of Boltzmann machine model as backpropagation neural network utilizes conjugate gradient method pair with the data of tape label
The differentiation performance of model is optimized and revised, and the output of BP network is that crop yield estimates situation.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110244804A (en) * | 2019-06-14 | 2019-09-17 | 武汉合创源科技有限公司 | A kind of big data agricultural management system and method |
| CN110309960A (en) * | 2019-06-20 | 2019-10-08 | 武汉华电工研科技有限公司 | A kind of big data intellectual analysis forecasting system |
| CN110332957A (en) * | 2019-07-12 | 2019-10-15 | 仲恺农业工程学院 | A kind of arviculture information processing system and method |
| CN111461435A (en) * | 2020-04-01 | 2020-07-28 | 中国农业科学院农业信息研究所 | Crop yield prediction method and system |
| CN111667167A (en) * | 2020-06-03 | 2020-09-15 | 福建慧政通信息科技有限公司 | Agricultural grain yield estimation method and system |
| CN112070241A (en) * | 2020-09-11 | 2020-12-11 | 腾讯科技（深圳）有限公司 | Plant growth prediction method, device and equipment based on machine learning model |
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107506790A (en) * | 2017-08-07 | 2017-12-22 | 西京学院 | Greenhouse winter jujube plant disease prevention model based on agriculture Internet of Things and depth belief network |
-
2018
- 2018-11-07 CN CN201811321081.XA patent/CN109508824A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107506790A (en) * | 2017-08-07 | 2017-12-22 | 西京学院 | Greenhouse winter jujube plant disease prevention model based on agriculture Internet of Things and depth belief network |
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| CN110244804A (en) * | 2019-06-14 | 2019-09-17 | 武汉合创源科技有限公司 | A kind of big data agricultural management system and method |
| CN110309960A (en) * | 2019-06-20 | 2019-10-08 | 武汉华电工研科技有限公司 | A kind of big data intellectual analysis forecasting system |
| CN110332957A (en) * | 2019-07-12 | 2019-10-15 | 仲恺农业工程学院 | A kind of arviculture information processing system and method |
| WO2021109120A1 (en) * | 2019-12-06 | 2021-06-10 | 深圳市大疆创新科技有限公司 | Crop growth condition evaluation method and device |
| CN113033262A (en) * | 2019-12-25 | 2021-06-25 | 中移(成都)信息通信科技有限公司 | Model training method and crop yield estimation method |
| CN113033262B (en) * | 2019-12-25 | 2022-08-05 | 中移(成都)信息通信科技有限公司 | Model training method and crop yield estimation method |
| CN111461435A (en) * | 2020-04-01 | 2020-07-28 | 中国农业科学院农业信息研究所 | Crop yield prediction method and system |
| CN111667167A (en) * | 2020-06-03 | 2020-09-15 | 福建慧政通信息科技有限公司 | Agricultural grain yield estimation method and system |
| CN111667167B (en) * | 2020-06-03 | 2022-12-06 | 福建慧政通信息科技有限公司 | Agricultural grain yield estimation method and system |
| CN112070241A (en) * | 2020-09-11 | 2020-12-11 | 腾讯科技（深圳）有限公司 | Plant growth prediction method, device and equipment based on machine learning model |
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