CN103823371B - Agriculture Tree Precise Fertilization system and fertilizing method based on neural network model - Google Patents
Agriculture Tree Precise Fertilization system and fertilizing method based on neural network model Download PDFInfo
- Publication number
- CN103823371B CN103823371B CN201410048817.6A CN201410048817A CN103823371B CN 103823371 B CN103823371 B CN 103823371B CN 201410048817 A CN201410048817 A CN 201410048817A CN 103823371 B CN103823371 B CN 103823371B
- Authority
- CN
- China
- Prior art keywords
- module
- output
- unit
- input
- layer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of agriculture Tree Precise Fertilization system based on neural network model, including the sensor data acquisition module for gathering agricultural land soil parameter, its outfan pass sequentially through gateway, data transmission module with and the data base that is connected of central processing unit be connected, the input/output terminal of central processing unit respectively with RS remote sensing module, GPS locating module, GIS geography information module, DSS plant growth decision support module input/output terminal be connected.The invention also discloses the fertilizing method of a kind of agriculture Tree Precise Fertilization system based on neural network model.The present invention divides by functional module, System information, automatization level degree are high, merge the achievement of field of artificial intelligence neutral net research, utilize neutral net, can fully approach arbitrarily complicated non-linear relation, system uncertain with self adaptation can be learnt, set up neural network model, make it carry out Fertilization Decision according to ecological benefits, maximization of economic benefit, put into practice offer method foundation for variable fertilization.
Description
Technical field
The present invention relates to precision agriculture system farmland fertilization technical field, the fertilizing method of especially a kind of agriculture Tree Precise Fertilization system based on neural network model.
Background technology
Traditional agriculture production technology carries out using big plot as administrative unit, plot environment that thousands of mus are big, edphic factor etc. are widely different, such production technology can cause grants the unbalanced of need, it is impossible to gives full play to plant growth potentiality, affects economic benefit and ecological benefits.Precision agriculture is based on global satellite positioning, present information management technique, crop aid decision supports the integrated Crop management technology assembled such as technology and agriculture project equipment technology, divide farmland into small cell, with the spatio-temporal difference of crop yield and growing environment condition for foundation, crop is carried out agricultural production cultivation management, it is precisely in that, utilize GPS, GIS, RS, DSS system detects crop immediately, farmland, soil parameters, according to administrative unit soil characteristic and crop growth needs, formulate and produce prescription map, sow with this, fertilising, spray medicine, management is thrown in irrigations etc..
Tree Precise Fertilization is the core content in precision agriculture technology; the enforcement of Tree Precise Fertilization can save fertilizer; increase grain yield; balanced soil nutrient, technology is based on soil nutrient status, crop regulation of fertilizer requirement and target output, regulated fertilization amount, nitrogen phosphoris and potassium fertilizer ratio and fertilizing time; to improve chemical fertilizer utilization ratio; maximally utilise land resource, obtain maximum output and maximum economic benefit, protecting agriculture ecological environment and natural resources with rational fertilizers input amount.The heavy difficult point of Tree Precise Fertilization is in that to formulate decision model, owing to target output is difficult to estimate accurately, in highly non-linear between yield and soil nutrient, dose and other influence factor, existing traditional Fertilization Model is many with current production practices incompatibility, and the mass data of accumulation is difficult to Instructing manufacture practice.
Summary of the invention
It is an object of the invention to provide the fertilizing method that a kind of intelligence degree is strong, have the agriculture Tree Precise Fertilization system based on neural network model of higher precision and robustness.
For achieving the above object, present invention employs techniques below scheme: the fertilizing method of a kind of agriculture Tree Precise Fertilization system based on neural network model, the method includes the step of following order:
(1) gathered the soil parameters of agricultural land soil to be detected by sensor data acquisition module, and sent to data base by gateway, data transmission module;
(2) central processing unit calls in data base the soil parameters of storage and carries out computing, first builds neuronal structure model, reselection activation primitive, finally carry out back-propagating study, obtain training objective, the sample data that training obtains, draw the neural network model based on back-propagating study;
(3) on the basis of neural network model, use GIS geography information module, and in conjunction with the Tree Precise Fertilization decision-making that DSS plant growth decision support module provides, provide precision agriculture fertilizing method;
The system implementing described fertilizing method includes the sensor data acquisition module for gathering agricultural land soil parameter, its outfan pass sequentially through gateway, data transmission module with and the data base that is connected of central processing unit be connected, the input/output terminal of central processing unit respectively with RS remote sensing module, GPS locating module, GIS geography information module, DSS plant growth decision support module input/output terminal be connected;Described sensor data acquisition module by soil temperature sensor, soil moisture measurement sensor and for measure Nitrogen In Soils, phosphorus, potassium content soil nutrient elements determination sensor form;Described sensor data acquisition module, RS remote sensing module, GPS locating module are bundled on mobile farm working machinery jointly;Described data transmission module adopts GPRS wireless network module;Described central processing unit is background computer.
In step (2), described structure neuronal structure model refers to, the output y of neuron iiFor:
Wherein, yiBeing the output of neuron i, it is coupled together by power with other multiple neurons;XjIt is network input, j=1,2 ..., n;WjFor inputting weight, j=1,2 ... n;θiBeing the threshold values of neuron i, F is activation primitive.
In step (2), described selection activation primitive refers to, selects activation primitive Sigmoid function:
α is function slope, and e is Euler's constant, and x is independent variable.
In step (2), described back-propagating study refers to, if Artificial Neural Network Structures input layer quantity is N, P, K, Yield tetra-, the i.e. nitrogen of soil nutrient, phosphorus, potassium content and target output, model output layer neuronal quantity is three, it is the nitrogen of fertilising, phosphorus, potassium application rate respectively, hidden layer neuron quantity t rule of thumb formula:
M is output neuron number, and n is input block number, and a selects 1 between 1 to 10, carries out following neutral net back-propagating study:
(1), data normalization processes: first the soil nutrient content gathered by sensor data acquisition module and target output data being normalized, the data collected be distributed between [-1,1], normalization formula is:
Wherein, P is original input data, maximum in maxP and minP respectively P and minima, PnFor the input data after normalization;
(2), random initializtion network weight and threshold value, set end condition as the output valve of neutral net and the mean square error of real output value less than a certain threshold value, when end condition is unsatisfactory for, be repeated below step:
Forward calculates input and the output of hidden layer or each unit j of output layer in each training sample, for
Jth unit inputsOutput
Wherein, e is natural logrithm, θjIt is each neuronic threshold value for changing neuronic activity, wijIndicate that the weights between preceding layer neuron and later layer neuron, be namely by the power of the unit i of last layer to the connection of unit j, OiIt is the output of the unit i of last layer;
Then, the error of each unit j of output layer is calculated:
Errj=Oj(1-Oj)(Tj-Oj)
Wherein, TjIt is the j real output based on the known class label of given training sample;
By last to first hidden layer, for each unit j of hidden layer,
Errj=Oj(1-Oj)∑kErrkwkj
Wherein, wkjIt is to the connection weight of unit j by unit k in next higher level, and ErrkIt it is the error of unit k;
Afterwards, w in network is updatedijWeights, method is:
Δwij=lErrjOi
Wherein, l is learning rate, and after renewal, weights are
wij=wij+Δwij
For deviation θ each in networkj, rising in value is
Δθj=lErrj
After renewal, deviation is θj=θj+Δθj;
Random initializtion network weight and neuronic threshold value, propagated forward calculating hidden layer neuron in layer and the neuronic input of output layer and output, back-propagating correction weights and threshold value, until end condition meets.
As shown from the above technical solution, the present invention divides by functional module, and System information, automatization level degree are high, the link such as including data acquisition, data transmission, data process, provides educated decisions support for agricultural Tree Precise Fertilization.In addition, the present invention has merged the achievement of field of artificial intelligence neutral net research, utilize neutral net, can fully approach arbitrarily complicated non-linear relation, system uncertain with self adaptation can be learnt, set up neural network model so that it is carry out Fertilization Decision according to ecological benefits, maximization of economic benefit, put into practice offer method foundation for variable fertilization.
Accompanying drawing explanation
Fig. 1 is the system structure schematic diagram of the present invention;
Fig. 2 is neuronal structure schematic diagram in the present invention;
Fig. 3,4 be neural network model of the present invention training process schematic.
Detailed description of the invention
A kind of agriculture Tree Precise Fertilization system based on neural network model, including the sensor data acquisition module for gathering agricultural land soil parameter, its outfan pass sequentially through gateway, data transmission module with and the data base that is connected of central processing unit be connected, the input/output terminal of central processing unit respectively with RS remote sensing module, GPS locating module, GIS geography information module, DSS plant growth decision support module input/output terminal be connected.Described sensor data acquisition module by soil temperature sensor, soil moisture measurement sensor and for measure Nitrogen In Soils, phosphorus, potassium content soil nutrient elements determination sensor form.Described data transmission module adopts GPRS wireless network module, and described central processing unit is background computer, as shown in Figure 1.
As shown in Figure 1, described sensor data acquisition module, RS remote sensing module, GPS locating module is bundled on mobile farm working machinery jointly, when Work machine is when Tanaka's operation, community, sensor data acquisition module Real-time Collection each farmland moisture, the data such as fertility, it is sent in data base by data transmission module, simultaneously, the GPS of community, farmland positions information, RS remote sensing image hierarchical information is also correspondingly saved in data base, by information integration and process, unification is called through central processing unit, it is sent to GIS geography information module, via GIS geography information module analysis, process, form farmland spatial geographic information figure, yield spatial distribution map, and carry out the system decision-making in conjunction with DSS plant growth decision support module.
As it is shown in figure 1, described sensor data acquisition module is responsible for gathering in agricultural land soil fertile soil parameters such as nitrogen, phosphorus, potassium, temperature, moisture;Have the soil nutrient elements determination sensor based on the exploitation of soil liquid photoelectric colorimetry, based on the soil moisture measurement sensor of near-infrared (NIR) spectral technique and transfer impedance transformation theory, these sensors can be detected simultaneously by agricultural land soil the instant data of the soil of main nutrient elements.Described data transmission module is that the data coming from sensor data acquisition module are passed through the integrated gateway that is sent to, and will be transferred to the Internet by gateway unification by GPRS wireless network module or 3G network, to reach remotely to receive data function.Described GPS locating module is responsible for obtaining farmland latitude and longitude information data, in order to the later stage carries out agricultural land soil spatial data data management in conjunction with GIS GIS-Geographic Information System module, implements accurate positioning fertilising.Described RS remote sensing module is used for obtaining field numerical map, generates field-crop Yield distribution in space figure by Production rate.
As shown in Figure 1, agricultural land soil is being carried out intelligence sample by described GIS geography information module, after the data acquisitions such as soil nutrient, water content and Soil Nitrogen, phosphorus, potassium are got off, data transmission module sends these data to central processing unit with corresponding GPS location data information, and in conjunction with information such as spatial geographical locations figure, soil nutrient, output distribotion, fertilizer application modes, it is updated to the GIS information geography system module that can be used for native system, instructs follow-up Tree Precise Fertilization to produce.Described central processing unit is by setting up neutral net and by training the data that transmission obtains to draw effective neural network model, just Fertilization Model is formed on this neural network model basis, excavate information potential, useful in data, and generate accurate space Fertilization prescription chart in conjunction with field-crop yield and geographical location information, instruct and carry out accurate farmland operation.Described data base is responsible for storage system and is correlated with various data, positions information, agricultural land soil parameter, output distribotion information including GPS, is connected with central processing unit, it is provided that data are transmitted and Informational support.Described DSS plant growth decision support module is developed based on crop expert knowledge library, including growth course and the nutrient demand for describing crop such as crop growth model, crop alimentary knowledge model.
This method comprises the following steps: the first step, sensor data acquisition module gather the soil parameters of agricultural land soil to be detected, and sent to data base by gateway, data transmission module;Second step, central processing unit calls the soil parameters of storage in data base and carries out computing, first builds neuronal structure model, reselection activation primitive, finally carries out back-propagating study, obtains training objective, the sample data that training obtains, draws the neural network model based on back-propagating study;3rd step, on the basis of neural network model, uses GIS geography information module, and in conjunction with the Tree Precise Fertilization decision-making that DSS plant growth decision support module provides, provides precision agriculture fertilizing method.
In second step, in step (2), described structure neuronal structure model refers to, the output y of neuron iiFor:
Wherein, yiBeing the output of neuron i, it is coupled together by power with other multiple neurons;XjIt is network input, j=1,2 ..., n;WjFor inputting weight, j=1,2 ... n;θiBeing the threshold values of neuron i, F is activation primitive.
Neuron is the basic computational ele-ment of neutral net, is the non-linear unit of multi input, single output, and neuron has excited and suppresses two kinds of duties.One neuron can regard an information process unit as, is made up of three fundamentals: connecting line, adder, activation primitive.Neuronal structure is as shown in Figure 2.
In second step, described selection activation primitive refers to, selects activation primitive Sigmoid function:
α is function slope, and e is Euler's constant, and x is independent variable.Activation primitive, for the result of calculation of sum unit is carried out functional operation, obtains neuron output, and advantage is as x=-∞, and F (x) extreme value is 0, and as x=∞, F (x) extreme value is 1, and can directly suppress or activate weight factor.
In second step, described back-propagating study refers to, if Artificial Neural Network Structures input layer quantity is N, P, K, Yield tetra-, the i.e. nitrogen of soil nutrient, phosphorus, potassium content and target output, model output layer neuronal quantity is three, it is the nitrogen of fertilising, phosphorus, potassium application rate respectively, hidden layer neuron quantity t rule of thumb formula:
M is output neuron number, and n is input block number, and a selects 1 between 1 to 10, carries out following neutral net back-propagating study, as shown in Figure 3,4:
(1), data normalization processes: first the soil nutrient content gathered by sensor data acquisition module and target output data being normalized, the data collected be distributed between [-1,1], normalization formula is:
Wherein, P is original input data, maximum in maxP and minP respectively P and minima, PnFor the input data after normalization;
(2), random initializtion network weight and threshold value, set end condition as the output valve of neutral net and the mean square error of real output value less than a certain threshold value, when end condition is unsatisfactory for, be repeated below step:
Forward calculates input and the output of hidden layer or each unit j of output layer in each training sample, for
Jth unit inputsOutput
Wherein, e is natural logrithm, θjIt is each neuronic threshold value for changing neuronic activity, wijIndicate that the weights between preceding layer neuron and later layer neuron, be namely by the power of the unit i of last layer to the connection of unit j, OiIt is the output of the unit i of last layer;
Then, the error of each unit j of output layer is calculated:
Errj=Oj(1-Oj)(Tj-Oj)
Wherein, TjIt is the j real output based on the known class label of given training sample;
By last to first hidden layer, for each unit j of hidden layer,
Errj=Oj(1-Oj)∑kErrkwkj
Wherein, wkjIt is to the connection weight of unit j by unit k in next higher level, and ErrkIt it is the error of unit k;
Afterwards, w in network is updatedijWeights, method is:
Δwij=lErrjOi
Wherein, l is learning rate, and after renewal, weights are
wij=wij+Δwij
For deviation θ each in networkj, rising in value is
Δθj=lErrj
After renewal, deviation is θj=θj+Δθj;
Random initializtion network weight and neuronic threshold value, propagated forward calculating hidden layer neuron in layer and the neuronic input of output layer and output, back-propagating correction weights and threshold value, until end condition meets.
In sum, the core of the present invention is in that first to be gathered the soil parameters of agricultural land soil to be detected by sensor data acquisition module, and is sent to data by gateway, data transmission module;Central processing unit calls the soil parameters of storage in data base and carries out computing, first builds neuronal structure model, reselection activation primitive, finally carry out back-propagating study, obtain training objective, the sample data that training obtains, draw the neural network model based on back-propagating study;On the basis of neural network model, use GIS geography information module, and in conjunction with the Tree Precise Fertilization decision-making that DSS plant growth decision support module provides, provide precision agriculture fertilizing method.
Claims (4)
1., based on a fertilizing method for the agriculture Tree Precise Fertilization system of neural network model, the method includes the step of following order:
(1) gathered the soil parameters of agricultural land soil to be detected by sensor data acquisition module, and sent to data base by gateway, data transmission module;
(2) central processing unit calls in data base the soil parameters of storage and carries out computing, first builds neuronal structure model, reselection activation primitive, finally carry out back-propagating study, obtain training objective, the sample data that training obtains, draw the neural network model based on back-propagating study;
(3) on the basis of neural network model, use GIS geography information module, and in conjunction with the Tree Precise Fertilization decision-making that DSS plant growth decision support module provides, provide precision agriculture fertilizing method;
The system implementing described fertilizing method includes the sensor data acquisition module for gathering agricultural land soil parameter, its outfan pass sequentially through gateway, data transmission module with and the data base that is connected of central processing unit be connected, the input/output terminal of central processing unit respectively with RS remote sensing module, GPS locating module, GIS geography information module, DSS plant growth decision support module input/output terminal be connected;Described sensor data acquisition module by soil temperature sensor, soil moisture measurement sensor and for measure Nitrogen In Soils, phosphorus, potassium content soil nutrient elements determination sensor form;Described sensor data acquisition module, RS remote sensing module, GPS locating module are bundled on mobile farm working machinery jointly;Described data transmission module adopts GPRS wireless network module;Described central processing unit is background computer.
2. fertilizing method according to claim 1, it is characterised in that: in step (2), described structure neuronal structure model refers to, the output y of neuron iiFor:
Wherein, yiBeing the output of neuron i, it is coupled together by power with other multiple neurons;XjIt is network input, j=1,2 ..., n;WjFor inputting weight, j=1,2 ... n;θiBeing the threshold values of neuron i, F is activation primitive.
3. fertilizing method according to claim 1, it is characterised in that: in step (2), described selection activation primitive refers to, selects activation primitive Sigmoid function:
α is function slope, and e is Euler's constant, and x is independent variable.
4. fertilizing method according to claim 1, it is characterized in that: in step (2), described back-propagating study refers to, if Artificial Neural Network Structures input layer quantity is N, P, K, Yield tetra-, the i.e. nitrogen of soil nutrient, phosphorus, potassium content and target output, model output layer neuronal quantity is three, is the nitrogen of fertilising, phosphorus, potassium application rate respectively, hidden layer neuron quantity t rule of thumb formula:M is output neuron number, and n is input block number, and a selects 1 between 1 to 10, carries out following neutral net back-propagating study:
(1), data normalization processes: first the soil nutrient content gathered by sensor data acquisition module and target output data being normalized, the data collected be distributed between [-1,1], normalization formula is:
Wherein, P is original input data, maximum in maxP and minP respectively P and minima, PnFor the input data after normalization;
(2), random initializtion network weight and threshold value, set end condition as the output valve of neutral net and the mean square error of real output value less than a certain threshold value, when end condition is unsatisfactory for, be repeated below step:
Forward calculates input and the output of hidden layer or each unit j of output layer in each training sample, inputs for jth unitOutput
Wherein, e is natural logrithm, θjIt is each neuronic threshold value for changing neuronic activity, wijIndicate that the weights between preceding layer neuron and later layer neuron, be namely by the power of the unit i of last layer to the connection of unit j, OiIt is the output of the unit i of last layer;
Then, the error of each unit j of output layer is calculated:
Errj=Oj(1-Oj)(Tj-Oj)
Wherein, TjIt is the j real output based on the known class label of given training sample;
By last to first hidden layer, for each unit j of hidden layer,
Errj=Oj(1-Oj)∑kErrkwkj
Wherein, wkjIt is to the connection weight of unit j by unit k in next higher level, and ErrkIt it is the error of unit k;
Afterwards, w in network is updatedijWeights, method is:
Δwij=lErrjOi
Wherein, l is learning rate, and after renewal, weights are
wij=wij+Δwij
For deviation θ each in networkj, rising in value is
Δθj=lErrj
After renewal, deviation is θj=θj+Δθj;
Random initializtion network weight and neuronic threshold value, propagated forward calculating hidden layer neuron in layer and the neuronic input of output layer and output, back-propagating correction weights and threshold value, until end condition meets.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410048817.6A CN103823371B (en) | 2014-02-12 | 2014-02-12 | Agriculture Tree Precise Fertilization system and fertilizing method based on neural network model |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410048817.6A CN103823371B (en) | 2014-02-12 | 2014-02-12 | Agriculture Tree Precise Fertilization system and fertilizing method based on neural network model |
Publications (2)
Publication Number | Publication Date |
---|---|
CN103823371A CN103823371A (en) | 2014-05-28 |
CN103823371B true CN103823371B (en) | 2016-06-29 |
Family
ID=50758489
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201410048817.6A Active CN103823371B (en) | 2014-02-12 | 2014-02-12 | Agriculture Tree Precise Fertilization system and fertilizing method based on neural network model |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103823371B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018101848A1 (en) * | 2016-11-29 | 2018-06-07 | Coolfarm S.A. | Predictive dynamic cloud based system for environmental sensing and actuation and respective method of operation |
Families Citing this family (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104699041B (en) * | 2015-02-04 | 2017-06-16 | 青岛农业大学 | A kind of vibration type deep-loosening machine operation method for monitoring state |
CN104599474B (en) * | 2015-02-04 | 2018-06-19 | 青岛农业大学 | A kind of vibration type deep-loosening machine operation condition monitoring system |
CN105230447B (en) * | 2015-09-06 | 2017-10-03 | 淮阴工学院 | Tomato irrigates intelligence control system |
CN106197539A (en) * | 2016-06-30 | 2016-12-07 | 淮南市农康生态农业有限公司 | A kind of farmland climatic environment visualizing monitor system |
CN106342663A (en) * | 2016-08-30 | 2017-01-25 | 聂枫 | Park maintenance method |
CN106527140A (en) * | 2016-11-29 | 2017-03-22 | 深圳前海弘稼科技有限公司 | Cloud big data model-based greenhouse control method and device |
CN107367933A (en) * | 2017-07-09 | 2017-11-21 | 长沙善道新材料科技有限公司 | A kind of Intelligent agricultural machinery fertilizing method and device based on Internet of Things |
CN107991888A (en) * | 2018-01-03 | 2018-05-04 | 常州市兰翔电器有限公司 | Agricultural automation embedded integration system and its method of work based on machine learning |
CN108510102A (en) * | 2018-02-07 | 2018-09-07 | 青岛农业大学 | A kind of water-fertilizer integral control method of irrigation using big data calculative strategy |
CN108828519B (en) * | 2018-05-24 | 2022-08-09 | 桂林电子科技大学 | High-precision indoor visible light positioning method based on machine learning |
CN109092206A (en) * | 2018-09-25 | 2018-12-28 | 常州荣创自动化装备股份有限公司 | Based on mobile Internet and the intelligentized fertilizer ratio blending bagging system of cloud service and method |
CN111144168B (en) * | 2018-11-02 | 2023-04-18 | 阿里巴巴集团控股有限公司 | Crop growth cycle identification method, equipment and system |
US11744168B2 (en) * | 2019-11-29 | 2023-09-05 | Soilmetrix, Inc. | Enhanced management zones for precision agriculture |
CN109964611A (en) * | 2019-04-16 | 2019-07-05 | 中国农业大学 | A kind of field crop Tree Precise Fertilization method and system |
CN110400170B (en) * | 2019-07-10 | 2022-08-12 | 北京耕智农业科技有限公司 | Method and device for adjusting crop supply based on user feedback data |
CN112450056A (en) * | 2019-09-09 | 2021-03-09 | 云南天质弘耕科技有限公司 | Water, fertilizer and pesticide integrated intelligent irrigation system based on machine learning algorithm |
CN110906986A (en) * | 2019-12-11 | 2020-03-24 | 内蒙古工业大学 | Grassland ecological monitoring system and method based on Internet of things |
CN111815110A (en) * | 2020-05-30 | 2020-10-23 | 阳江市智慧农业研究院 | Agricultural production planning accurate decision-making method and system based on big data and artificial intelligence |
CN113379188B (en) * | 2021-05-06 | 2022-10-25 | 贵州省烟草公司贵阳市公司 | Tobacco crop rotation planting method and system based on Internet of things |
CN113671154A (en) * | 2021-08-16 | 2021-11-19 | 武汉禾大科技有限公司 | Soil testing formula system |
CN114946360A (en) * | 2022-05-06 | 2022-08-30 | 广西大学 | Prevent blockking up screw fertilization mechanism and control system thereof |
CN115952931A (en) * | 2023-03-14 | 2023-04-11 | 华智生物技术有限公司 | Intelligent rice fertilization method, system, equipment and medium |
CN117322214B (en) * | 2023-11-30 | 2024-02-09 | 余姚市农业技术推广服务总站 | Crop fertilizer accurate application method and system based on neural network |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6058351A (en) * | 1998-09-10 | 2000-05-02 | Case Corporation | Management zones for precision farming |
JP2009246833A (en) * | 2008-03-31 | 2009-10-22 | Dkk-Toa Yamagata Corp | Wireless transmitter/receiver, and transmission and reception method using the same |
CN201781832U (en) * | 2010-06-08 | 2011-04-06 | 宁夏农林科学院 | Precise water-replenishing control device |
CN102524024A (en) * | 2012-02-16 | 2012-07-04 | 四川农业大学 | Crop irrigation system based on computer vision |
CN103092142A (en) * | 2011-11-03 | 2013-05-08 | 中国科学院合肥物质科学研究院 | Farmland fertility data collect and analysis system based on data mining |
CN103149897A (en) * | 2013-02-01 | 2013-06-12 | 中北大学 | Precision agriculture self-adaption monitoring system and method based on agricultural Internet of things |
CN203241793U (en) * | 2013-04-18 | 2013-10-16 | 何惠彬 | Agriculture production monitoring and management system based on internet of things |
CN203302092U (en) * | 2013-06-27 | 2013-11-27 | 华北水利水电大学 | Accurate irrigation data acquisition and control system and accurate irrigation system |
CN103548461A (en) * | 2013-11-05 | 2014-02-05 | 丹阳通宇机械有限公司 | Precise agricultural automatic/manual variable fertilizing machine based on virtual GPS (global position system) |
-
2014
- 2014-02-12 CN CN201410048817.6A patent/CN103823371B/en active Active
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6058351A (en) * | 1998-09-10 | 2000-05-02 | Case Corporation | Management zones for precision farming |
JP2009246833A (en) * | 2008-03-31 | 2009-10-22 | Dkk-Toa Yamagata Corp | Wireless transmitter/receiver, and transmission and reception method using the same |
CN201781832U (en) * | 2010-06-08 | 2011-04-06 | 宁夏农林科学院 | Precise water-replenishing control device |
CN103092142A (en) * | 2011-11-03 | 2013-05-08 | 中国科学院合肥物质科学研究院 | Farmland fertility data collect and analysis system based on data mining |
CN102524024A (en) * | 2012-02-16 | 2012-07-04 | 四川农业大学 | Crop irrigation system based on computer vision |
CN103149897A (en) * | 2013-02-01 | 2013-06-12 | 中北大学 | Precision agriculture self-adaption monitoring system and method based on agricultural Internet of things |
CN203241793U (en) * | 2013-04-18 | 2013-10-16 | 何惠彬 | Agriculture production monitoring and management system based on internet of things |
CN203302092U (en) * | 2013-06-27 | 2013-11-27 | 华北水利水电大学 | Accurate irrigation data acquisition and control system and accurate irrigation system |
CN103548461A (en) * | 2013-11-05 | 2014-02-05 | 丹阳通宇机械有限公司 | Precise agricultural automatic/manual variable fertilizing machine based on virtual GPS (global position system) |
Non-Patent Citations (1)
Title |
---|
一种新的神经网络集成方法及其在精准施肥中的应用;于合龙等;《计算机研究与发展》;20100915;第47卷(第9期);论文第1531-1536页 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2018101848A1 (en) * | 2016-11-29 | 2018-06-07 | Coolfarm S.A. | Predictive dynamic cloud based system for environmental sensing and actuation and respective method of operation |
Also Published As
Publication number | Publication date |
---|---|
CN103823371A (en) | 2014-05-28 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103823371B (en) | Agriculture Tree Precise Fertilization system and fertilizing method based on neural network model | |
Mohamed et al. | Smart farming for improving agricultural management | |
Nabati et al. | GIS-based agro-ecological zoning for crop suitability using fuzzy inference system in semi-arid regions | |
Dobermann et al. | Precision farming: challenges and future directions | |
CN107392376A (en) | A kind of crops Meteorological Output Forecasting Methodology and system | |
CN109757175A (en) | A kind of corn water-fertilizer integral variable fertilization method based on unmanned plane monitoring | |
CN101578936B (en) | Fertilization processing method and system | |
CN105181607A (en) | Soil quick-acting nutrient inversion method and device | |
Tatsumi | Effects of automatic multi-objective optimization of crop models on corn yield reproducibility in the USA | |
Singh | Precision farming | |
Belal et al. | Precision farming technologies to increase soil and crop productivity | |
Jiang et al. | Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand: A case study of maize-grain crop production in the Waikato region | |
Choopan et al. | Estimation of barley yield under irrigation with wastewater using RBF and GFF models of artificial neural network | |
Guo et al. | 14 Data-Driven Precision Agriculture | |
Cheema et al. | Precision Agriculture and ICT: Future Farming | |
Lascano et al. | Precision agriculture: Water and nutrient management | |
Kar et al. | Improving data management and decision-making in precision agriculture | |
CN105432205A (en) | Soil attribute parameter estimation method, fertilizer application program recommended method and fertilizer preparation program recommended apparatus | |
Sarmah et al. | Precision nutrient management for field and horticultural crops | |
Tanaka et al. | Synthetic data generation for validating site-specific crop yield response modelling using WOFOST and gaussian geostatistical simulations | |
McCarthy et al. | Machine vision for camera-based horticulture crop growth monitoring | |
Nourkhah et al. | The role of sensors in smart agriculture | |
Kanthilanka et al. | Economically optimum N fertiliser decisions for rice cultivation in Sri Lanka: Does soil type matter? | |
Sahoo | Geoinformatics for Precision Agriculture | |
Sahoo | Precision farming: concepts, limitations, and opportunities in Indian agriculture |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C14 | Grant of patent or utility model | ||
GR01 | Patent grant |