CN109191074A - Wisdom orchard planting management system - Google Patents
Wisdom orchard planting management system Download PDFInfo
- Publication number
- CN109191074A CN109191074A CN201810983208.8A CN201810983208A CN109191074A CN 109191074 A CN109191074 A CN 109191074A CN 201810983208 A CN201810983208 A CN 201810983208A CN 109191074 A CN109191074 A CN 109191074A
- Authority
- CN
- China
- Prior art keywords
- fruit tree
- module
- information
- image
- training
- 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.)
- Pending
Links
- 239000002420 orchard Substances 0.000 title claims abstract description 40
- 235000013399 edible fruits Nutrition 0.000 claims abstract description 161
- 230000012010 growth Effects 0.000 claims abstract description 43
- 238000007726 management method Methods 0.000 claims abstract description 32
- 238000003973 irrigation Methods 0.000 claims abstract description 20
- 230000002262 irrigation Effects 0.000 claims abstract description 20
- 238000005516 engineering process Methods 0.000 claims abstract description 18
- 244000045947 parasite Species 0.000 claims abstract description 9
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 8
- 230000007613 environmental effect Effects 0.000 claims abstract description 8
- 238000004519 manufacturing process Methods 0.000 claims abstract description 8
- 230000008859 change Effects 0.000 claims abstract description 4
- 238000007418 data mining Methods 0.000 claims abstract description 4
- 238000012544 monitoring process Methods 0.000 claims abstract description 4
- 238000012549 training Methods 0.000 claims description 53
- 238000012545 processing Methods 0.000 claims description 35
- 241000196324 Embryophyta Species 0.000 claims description 20
- 239000003337 fertilizer Substances 0.000 claims description 20
- 201000010099 disease Diseases 0.000 claims description 18
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 18
- 241000607479 Yersinia pestis Species 0.000 claims description 17
- 238000013528 artificial neural network Methods 0.000 claims description 14
- 238000003745 diagnosis Methods 0.000 claims description 14
- 238000009313 farming Methods 0.000 claims description 14
- 230000003862 health status Effects 0.000 claims description 13
- 230000003902 lesion Effects 0.000 claims description 13
- 239000002689 soil Substances 0.000 claims description 13
- 230000036541 health Effects 0.000 claims description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000003062 neural network model Methods 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 7
- 230000001537 neural effect Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 5
- 238000003967 crop rotation Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 5
- 239000007921 spray Substances 0.000 claims description 5
- 239000000284 extract Substances 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000012271 agricultural production Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 230000004720 fertilization Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 241001269238 Data Species 0.000 claims description 2
- 244000085025 Pandanus tectorius Species 0.000 claims description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 2
- 238000013480 data collection Methods 0.000 claims description 2
- 229910052760 oxygen Inorganic materials 0.000 claims description 2
- 239000001301 oxygen Substances 0.000 claims description 2
- 230000008635 plant growth Effects 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims description 2
- 230000005094 fruit set Effects 0.000 claims 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims 1
- 241000790917 Dioxys <bee> Species 0.000 claims 1
- 229910052799 carbon Inorganic materials 0.000 claims 1
- 238000013135 deep learning Methods 0.000 abstract description 6
- 239000005871 repellent Substances 0.000 abstract description 3
- 230000002940 repellent Effects 0.000 abstract description 3
- 230000008901 benefit Effects 0.000 description 6
- 210000003608 fece Anatomy 0.000 description 5
- 239000007788 liquid Substances 0.000 description 5
- 239000010871 livestock manure Substances 0.000 description 5
- 238000000034 method Methods 0.000 description 5
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 238000013138 pruning Methods 0.000 description 4
- 238000009333 weeding Methods 0.000 description 4
- 239000000575 pesticide Substances 0.000 description 3
- 206010018498 Goitre Diseases 0.000 description 2
- 241000238631 Hexapoda Species 0.000 description 2
- 239000001569 carbon dioxide Substances 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000035784 germination Effects 0.000 description 2
- 201000003872 goiter Diseases 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 210000005036 nerve Anatomy 0.000 description 2
- 210000004218 nerve net Anatomy 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- UPLPHRJJTCUQAY-WIRWPRASSA-N 2,3-thioepoxy madol Chemical compound C([C@@H]1CC2)[C@@H]3S[C@@H]3C[C@]1(C)[C@@H]1[C@@H]2[C@@H]2CC[C@](C)(O)[C@@]2(C)CC1 UPLPHRJJTCUQAY-WIRWPRASSA-N 0.000 description 1
- 241001124076 Aphididae Species 0.000 description 1
- 241000221785 Erysiphales Species 0.000 description 1
- 241000406668 Loxodonta cyclotis Species 0.000 description 1
- 244000241838 Lycium barbarum Species 0.000 description 1
- 235000015459 Lycium barbarum Nutrition 0.000 description 1
- 235000015468 Lycium chinense Nutrition 0.000 description 1
- 241001414989 Thysanoptera Species 0.000 description 1
- JNVCSEDACVAATK-UHFFFAOYSA-L [Ca+2].[S-]SSS[S-] Chemical compound [Ca+2].[S-]SSS[S-] JNVCSEDACVAATK-UHFFFAOYSA-L 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000012272 crop production Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 239000002917 insecticide Substances 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 239000003895 organic fertilizer Substances 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 238000003892 spreading Methods 0.000 description 1
- 238000009966 trimming Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Health & Medical Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Primary Health Care (AREA)
- Animal Husbandry (AREA)
- Mining & Mineral Resources (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Marine Sciences & Fisheries (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of wisdom orchard planting management systems, the present invention obtains fruit tree growth environmental information according to wireless network, it realizes quick, multidimensional, multiple dimensioned orchard information real-time monitoring, it is that every fruit tree constructs growth model based on deep learning, use VR/AR three-dimensional model, the mode of chart and curve is intuitively dynamically shown to user, system carries out various sound-light alarms when environmental data exception, utilize big data, cloud computing, data mining, artificial intelligence, audio and video technology makes agricultural planting move towards precision, it is irrigated according to the feedback of information above garden is provided, the decision supports such as fertilising, realize machine intelligence, such as intelligent irrigation, intelligence fertilising, infrasound bird repellent expelling parasite etc. automatically controls, remote control production process, fixed-point operation, there is provided a set of scientific system accurate agricultural intelligence for plant personnel Change production management system to solve the above problems.
Description
Technical field
The present invention relates to virtual reality emulation fields, and in particular to a kind of wisdom orchard planting management system.
Background technique
The production process of traditional planting needs the whole nurse of plant personnel, and relies on the plantation experience pair of administrative staff
The different upgrowth situations of crops take the measures such as watering, fertilizing weeding expelling parasite, but exist and water-fertilizer-pesticide occur because technology is not up to standard
The not high phenomenon of utilization rate, causes the waste of resource, and What is more, also result in crop foods safety it is not up to standard and to citizen
Health of human body damages.In recent years, haze is wreaked havoc, river is contaminated, there is different degrees of pollution in the soil in China 80%, money
Source and ecological environment have contacted red line, in face of severe planting environment, only with traditional plantation experience of administrative staff without
Method reply.Information asymmetry phenomenon in market for farm products transaction is fairly obvious, directly results in the growing surface of agricultural product type
Product, yield and price big ups and downs cause agricultural product high yield not had a good harvest.
The wisdom agricultural of China at this stage is to construct network system by wireless sensor on the basis of technology of Internet of things
System, monitors the growing environment information of crop, upgrowth situation in real time, instructs fertigation；Utilize " 3S " Technique dynamic
Detect crop yield；Realize the remote automatic controls such as automatic irrigation, automatic fertilization.But above-mentioned technology can not achieve and be directed to
The differentiated of specific Current Situation of Crop Production, targetedly operation instruction do not accomplish real wisdom agricultural, precisely plantation.
In view of above-mentioned, the designer is actively subject to research and innovation, to found a kind of wisdom orchard planting management system,
Make it with more the utility value in industry.
Summary of the invention
In order to solve the above technical problems, the object of the present invention is to provide a kind of intelligentized kind of offer scientific system scale
Plant the wisdom orchard planting management system of guidance.
Wisdom orchard planting management system of the present invention, comprising:
Garden Planning module obtains the market information of campus environment data and fruit tree over the years, according to campus environment data
And the market information output garden of fruit tree over the years raises fruit trees the alternative of the selection of kind and the division of planting area；
Fruit tree profile module constructs fruit tree growth model, is that every fruit tree creates health account and record operation log, note
The upgrowth situation for recording and predicting fruit tree carries out differentiated processing, guidance according to meteorological data and the actual conditions of plant growth
Scientific crop rotation；
Precisely plantation module realizes that the long-distance intelligent control designated equipment for accurately distinguishing is managed to fruit tree, including
Drip irrigation and spray irrigation, water-fertilizer integral, pinpoint spray, sound wave expelling parasite plantation from robot walking Tree Precise Fertilization/deinsectization, unmanned plane at slight irrigation
Operation.
Further, the Garden Planning module includes: garden information acquisition unit, agricultural data acquisition unit, intelligence
Analytical unit and editing equipment management unit, wherein garden information acquisition unit, including carry out campus environment real time information sampling
Multiple wireless sensors, the wireless sensor include Temperature Humidity Sensor, temperature sensor, air quality sensor,
Soil temperature-moisture sensor, soil moisture sensor, carbon dioxide sensor, light intensity sensor, oxygen sensor；Agriculture number
According to acquisition unit, the market information of fruit tree over the years is obtained；
The intellectual analysis unit, according to the market information of campus environment data and fruit tree, planning of science activities garden, rationally choosing
With fruit variety.
The editing equipment management unit provides decision according to intellectual analysis unit, and the long-range designated equipment that controls is filled
The farming operations such as water, fertilising, expelling parasite.
Further, the fruit tree profile module, including garden information acquisition unit, pomology information acquisition unit, weather
Information acquisition unit, Fruit tree model unit and analytical unit, wherein
Fruit tree model module is neural network model to be constructed, by fruit tree growth sample based on fruit tree growing way sample set
Image in collection is analyzed, and fruit tree growth model is obtained, and judges growth cycle locating for fruit tree and the circannian rank of production
Section, wherein growing way sample set is one group of fruit tree growing way picture set, for training growth model, can use the growth model later
Judge fruit tree growing way and health status；
Garden information collection obtains the real time environmental data of garden by wireless sensor；
Pomology information acquisition unit, by wireless sensor, Image Acquisition, obtain every plant of fruit tree upgrowth situation information and
Health and fitness information, wherein one group can be inputted by information, fruit tree growth models such as fruit tree plant height, stem thickness, acquisition picture, soil datas
The information of fruit tree, to the upgrowth situation and health status judged；
Climatic information acquisition unit obtains garden location weather, history day using data collection, data mining technology
Gas information and the following relevant weather information；
Fruit tree model unit, upgrowth situation and ecological environment to fruit tree carry out real-time monitoring, found health account, record
Device therefor operation note monitors fruit tree growing way situation, prediction output of the fruit tree in real time according to collected information, and according to fruit
It sets yield and health status predicts ecological crop rotation；
There is abnormal and fruit tree health status when something goes wrong in campus environment data, utilizes artificial intelligence in analytical unit
Technology provides reasonable treatment measures and proposes, selects for plant personnel, realizes the specific aim plantation of differentiated, wherein fruit tree strain
Height, stem thickness, acquisition picture, soil data information, fruit tree growth model can input the information of one group of fruit tree, to the growth judged
Situation and health status.
Further, the fruit tree profile module further includes correction subelement, corrects fruit tree shelves for agricultural production personnel
The farming of case module instructs as a result, and will correct neural metwork training module of the data feedback to fruit tree growth model, nerve net
Network model training module carries out the optimization of Neural Network Diagnosis model based on amendment data.
Further, the neural network model training module of fruit tree includes sample acquisition module, image processing module, training
Module, optimization module, sample acquisition module, for obtaining the training image inside fruit tree growing way figure sample set；
Image processing module obtains normalized images for carrying out standardization processing to training image；
Training module for analyzing obtained normalized images, and combines the agriculture in fruit tree growing way figure sample set
Thing data carry out continual analysis training, obtain Neural Network Diagnosis model；
Correction module, user when for by Fruit tree model guidance plantation and farming operations correct data feedback to nerve net
Network training module makes neural metwork training module carry out the optimization of Neural Network Diagnosis model based on amendment data.
Further, Fruit tree model diagnostic module includes diagnosis object acquisition module, image processing module, diagnostic module,
Object acquisition module is diagnosed, for obtaining the diagnostic image with diagnosis object, wisdom orchard system is believed by image
Cease the upgrowth situation information image that acquisition technique obtains fruit tree；
Image processing module obtains normalized images for carrying out standardization processing to diagnostic image；
Standardization processing includes being labeled to the twig of acquisition image and leaf lesion point, has obtained trained figure to described
As progress batch processing, including unified format, equalization and denoising, then extract candidate frame and pre-training.The twig and
The normalized images that training image obtains that are labeled as of leaf lesion point carry out twig and diseased region feature mark, form twig
And diseased region label, label information include period and diseased region locating for lesion；
Mark further includes the upper left angle point and bottom right angle point of label information and twig and lesion point in the normalized images
Coordinate, label information refers to whether target belongs to the classification information of pest and disease damage and pest and disease damage developing stage；The mark is logical
It crosses K mean algorithm and clustering is carried out to the initial candidate frame chosen by hand in self-control data set, find the statistics rule of candidate frame
Rule is anchor several to cluster number k, and the wide high parameter of k cluster centre box of being subject to corrects anchor, obtains and advises
The most similar initial candidate frame parameter of lesion shape in generalized image.
Further, training module uses convolutional neural networks, is instructed in advance on fruit tree growth situation sample set first
Practice, small parameter perturbations are then carried out on training sample set, obtains the image advanced features of brothers fruit tree image, be output to next layer
Neural metwork training module；Diagnostic module is used to combine orchard ecology data, analyzes normalized images, and according to nerve
Network model is analyzed to obtain the progress farming guidance of fruit tree growth situation；Diagnostic module utilizes multilayer neural network, by from specification
Change in image and extracts candidate region；Twig and pest and disease damage position and classification information are predicted using entire image feature, are directly learned
Practise the global information of image；The object detection method of candidate frame, the comprehensive score by screening candidate frame embodies, by each candidate
The classification information that the confidence level of frame selects frame to predict with after is multiplied, and obtains comprehensive score, then carry out non-maxima suppression processing, with
Continuous iteration progress, the uninterrupted prediction block of parameter, gradually close to true frame, the true frame location information of final output and classification letter
Breath；Image processing module carries out format discriminance to training image or diagnostic image, and is required to carry out format according to normalized images
Conversion, while calculating the resolution ratio of training image or diagnostic image, and to resolution ratio lower than given threshold standardization figure into
Row reacquires.
Further, the accurate plantation module, including central processing, equipment management and log feedback, wherein
Central processing module, the specified equipment of long-range control is irrigated specified fruit tree, is applied depending on the user's operation
The operation such as fertilizer；
Device management module is linked into all types of equipment of orchard drip irrigation and spray irrigation, water-fertilizer integral, intelligent robot,
Long-range control mechanical equipment；
The usage log of equipment is fed back to wisdom orchard system, and the health account of abundant fruit tree by log feedback module.
According to the above aspect of the present invention, wisdom orchard planting management system of the present invention, has at least the following advantages:
The present invention combines the fruit tree market information system over the years to plan garden by the environmental information data in acquisition orchard, rationally
Fruit variety is matched, realizes that planting benefit maximizes；Growth model is constructed in conjunction with deep learning, establishes health account for each tree
The upgrowth situation change information of fruit tree and the operation log to every fruit tree are recorded, the health status and growth ring of each tree are monitored
Border data carry out science reply behave and recommend to select for plant personnel when occurring abnormal；Plant personnel is recommended in the operation of system
Lower remote control equipment (as sprinkling irrigation, water-fertilizer integral, unmanned plane are sprayed insecticide) realizes the accurate plantation to target fruit tree.Wisdom
Orchard system provides comprehensive informatization resolve scheme for plant personnel, helps user to raise the management level, improves efficiency, reduces
Cost increases income.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention,
And can be implemented in accordance with the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention and the accompanying drawings.
Detailed description of the invention
Fig. 1 is a kind of fruit tree management flow chart of wisdom orchard system in the embodiment of the present invention；
Fig. 2 is a kind of fruit tree growth model work flow diagram in the embodiment of the present invention；
Fig. 3 is a kind of fruit tree life cycle management flow chart of wisdom orchard system in the embodiment of the present invention；
Fig. 4 is a kind of accurate Cultivate administration flow chart of fruit tree of wisdom orchard system in the embodiment of the present invention.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
In recent years, deep learning has become new frontline technology, is widely used in image analysis, video analysis, automatic
The fields such as driving and unmanned plane, and achieve significant effect.Deep learning, which is applied to agricultural planting, may be implemented wisdom agriculture
Industry, wisdom plantation, " artificial intelligence+agricultural " technology refer to the artificial intelligence model based on deep learning, are formed by initialization
Model parameter is trained the data by mark, adjusts model parameter after there is error, then assists leading to agricultural knowledge
The accurately agricultural diagnostics model largely formed after training is crossed, realizes the guidance plant to agricultural production and to water-fertilizer-pesticide
Scientific management, bring increasing substantially for production efficiency and resource utilization.Since agricultural planting is a kind of partially empirical
Field, and artificial intelligence can from mass data quick learning sample feature, artificial intelligence application is referred in agricultural planting
It leads, agricultural planting personnel can be made to free from the heavy labor of duplicate complexity.
Wisdom orchard is based on technology of Internet of things, obtains fruit tree growth environmental information according to wireless network, realizes quick, more
Dimension, multiple dimensioned orchard information real-time monitoring are that every fruit tree constructs growth model based on deep learning, use VR/AR solid
The mode of model, chart and curve is intuitively dynamically shown to user, and system carries out various sound-light alarms when environmental data exception,
Agricultural planting is set to move towards precision using big data, cloud computing, data mining, artificial intelligence, audio and video technology, according to the above letter
The feedback of breath the decision supports such as is irrigated, is applied fertilizer to garden is provided, and realizes machine intelligence, as intelligent irrigation, intelligence fertilising,
Infrasound bird repellent expelling parasite etc. automatically controls, and remote control production process, fixed-point operation provides a set of scientific system for plant personnel
Accurate intelligent agriculture production management system.
Embodiment
As shown in Figures 1 to 4, an a kind of preferred embodiment of wisdom orchard planting management system of the present invention, comprising:
A kind of wisdom orchard system includes Garden Planning module, fruit tree profile module and precisely plants module.
After user selectes the planting area of fructus lycii garden, by essential information (such as geographical location, the size of wolfberry orchard
Deng) it is uploaded to wisdom orchard system.Campus environment information acquisition module includes Temperature Humidity Sensor, temperature sensor, air matter
The real time information of acquisition is passed to Garden Planning mould by quantity sensor, soil temperature-moisture sensor, carbon dioxide sensor etc.
Block.The information such as the fruit tree relevant information market supply and demand over the years being collected into are passed to Garden Planning by agricultural, commodities market information collection module
Module.The garden information and fruit tree market information that Garden Planning module will acquire combine, and carry out garden planning of science activities, provide rationally
Fruit tree matingplan.
Fruit tree profile module is that every fructus lycii tree constructs growth model, foundes health account, records the plantation position of fructus lycii tree
It sets, kind, the age of tree, set the data such as height, upgrowth situation, yield over the years, economic benefit over the years.This module is by every fruit of real-time detection
The upgrowth situation of tree provides the plantation guidance of scientific system differentiated.In the life cycle of fructus lycii tree, the basis since field planting
Fructus lycii tree kind, orchard environmental data provide technical operation guidance, such as be colonized when Planting Row Distance, spacing in the rows, depth, diameter,
Organic fertilizer is poured water, and provides 2 water, the irrigation opportunity prompt of 3 water and finger of pouring water according to soil moisture and recent meteorological data
It leads；Fructus lycii tree health status and twig upgrowth situation are monitored according to image acquisition technology, in conjunction with the age of tree and locating period annual period
Pruning guidance is carried out, the twig for needing to wipe out and retain is judged, fixed dry strong dry strategy such as is implemented to annual tree, it is biennial
Tree by pinching except the permanent base's tree crown major branch of tiller culture (summer removes useless sprout tillers cutting back transitory branch in time, carry out bud picking and
Branch is stayed in secondary bud picking, caving and dormant pruning), 3 years raw trees consolidate base's branch group spreading crown (selected and remain when summer pruning " 1,
2 grades of side shoots ", when dormant period, select and remain tiltedly raw or side shoot all one's life), 4 years raw tree culture permanent 2 layers of tree crown skeletons of tree body (repair by summer
Cultrate into and stablize tree crown major branch skeleton basis, dormant period selects and remain side shoot), economic effect is carried out every year at the fructus lycii tree of age to being in
Benefit assessment, when anticipation standard is not achieved in economic benefit or input and output are not consistent, system prompt replaces fruit tree guidance ecology
Crop rotation carries out the guidance of economic benefit good fruit tree (to consolidate that substantial semicircle is tree-like, and summer, which wipes out, sprouts at the trimming work of age tree
Bud consolidates tree-like, and dormant period selects and remain fruit branch).
Fruit tree profile module is every year according to weather information, the health status and growing environment data of fruit tree, guidance plantation people
Member carries out corresponding farming operations, provides scientific intelligent decision making guidance, realizes precisely plantation, wisdom plantation.This module is to every
Fruit tree carries out real-time yield and estimates, this operation can be estimated after carrying out relevant farming operations every time to the yield of fruit tree
It influences.Annual spring late March is to early April, and fruit tree profile module is according to the growth status of recent meteorological data and the fruit tree
The instructions such as hoe up weeds of raking the soil level of pouring water are provided user remotely to be controlled after user agrees to and executes the operation by precisely planting module
Designated equipment processed, such as water-fertilizer integrated intelligent irrigation equipment, intelligent weed-eradicating robot, carry out the intelligent farming operations of science.May
This module of part instructs user to carry out twig according to the branch germination status that image acquisition technology understands current fruit tree in conjunction with the age of tree
Select and remain and add required supporting arch, and the rush branch fertilizer accordingly matched, this step are applied according to the practical upgrowth situation of every fruit tree
It can be remotely operated by water-fertilizer integrated intelligent irrigation system.Fructus lycii tree in June enters germination period, this module is according to fruit tree
Twig upgrowth situation carries out pruning guidance, and controls intelligent weed-eradicating robot weeding using accurate Cultivate administration system remote
Rush fertilizers for potted flowers is used with water-fertilizer integrated intelligent irrigation system.The florescence of fructus lycii tree is the high-incidence of the pest and disease damages such as powdery mildew, goitre mite
Season, this module carry out prevention and control of plant diseases, pest control work according to fruit tree growth status, targetedly apply different fertilizer to each tree,
And the fructus lycii tree to the age of tree in 2~3 years executes top dressing operation.Fruit tree enters fruiting period, this module automatic prompt applies leaf to fruit tree
Leaven, to 2~3 years top dressing fruit trees.Different fertilisings is selected for the existing upgrowth situation of every fruit tree and locating annual period
Operation and liquid manure proportion.June~November, fruit tree entered the collecting period, this module judges fructus lycii maturation shape according to image acquisition technology
Condition prompts user to pick in time, and carries out prevention and control of plant diseases, pest control work, avoids the occurrence of the disease pests such as aphid, goitre mite, black fruit and thrips
Evil causes economic loss, is carried out that corresponding fertilizer and liquid manure is selected to match according to fruit tree individual instances, remote by precisely planting module
Process control designated equipment is irrigated.After fruit tree enters dormant period, this module according to the fruit tree age of tree prompt pour water operation opportunity and
Frequency pours water on a small quantity to 1~3 year tree, and the fruit tree of 4 years or more the age of trees pour water for 6~7 times, carries out 5~6 times at age tree
It pours water.This module judges envelope garden opportunity according to meteorological data, and scientific method is and guided to carry out envelope garden, such as uses the clear garden of lime sulfur
Reduce overwintering insect egg and germ.This last module waits second year to open garden again, then carries out new 1 year fructus lycii plantation and refer to
It leads.
Fruit tree growth model module in fruit tree profile module is divided into fruit tree growth model construction module and fruit tree growth shape
Condition diagnostic module.Fruit tree growth model training module is based on fruit tree growing way sample set, convolutional neural networks model is constructed, by right
Image in fruit tree growing way sample set is analyzed, and the Neural Network Diagnosis model of fruit tree growing way is obtained.Fruit tree growth situation is examined
Disconnected image of the module based on input, is judged, water-fertilizer-pesticide needed for obtaining fruit tree growing way and disease using Neural Network Diagnosis model
The diagnostic result of insect pest.Fruit tree growth model module further includes rectification module, is examined for plant personnel amendment fruit tree growth situation
It is disconnected as a result, and data feedback will be corrected to neural network model training module, neural network model training module be based on data into
The optimization of row Neural Network Diagnosis model.Wherein fruit tree growth model construction module includes that sample data acquisition obtains module, figure
As processing module and training module.Sample data acquisition module is used to obtain the training figure inside fructus lycii tree growing way figure sample set
Picture.Image processing module is used to training image progress standardization processing obtaining normalized images.Training module will be for that will obtain
Normalized images analyzed, and combine fruit tree growing way figure sample set year in farming data carry out lasting analyzing and training,
Obtain Neural Network Diagnosis model.Fruit tree growth State Diagnosis module is used to analyze normalized images, and according to nerve
Network model analyzes the diagnostic result for obtaining fruit tree growing way situation and pest and disease damage.Fruit tree growth State Diagnosis module is divided into diagnostic graph
As obtaining module, image processing module and diagnostic module.Diagnostic image, which obtains module and is based on technology of Internet of things, to be obtained wait diagnose pair
The image of elephant, wisdom orchard system obtain the growing way image of fruit tree based on technology of Internet of things.Image processing module is used for follow-up
Disconnected image carries out standardization processing and obtains normalized images.Diagnostic module is used to analyze normalized images, and according to mind
The diagnostic result of fruit tree growing way situation and disease pest is obtained through network model analysis.Correction module divides diagnostic result, plant personnel
Revised fruit tree growing way diagnostic result, diagnostic image are analysed as amendment data feedback to the neural network model training stage.Rule
Generalized processing includes being labeled to fruit tree growing way twig and pest and disease damage point, carries out batch processing to training image has been obtained,
Including unified format, equalization and denoising, then extract candidate frame and pre-training；The mark of twig and pest and disease damage point is to instruction
Practice the normalized images that image obtains and carry out focus characteristic mark, forming lesion information label includes newborn twig situation and disease pest
Evil locating period and diseased region, mark should also include the upper left corner of label information and target in the normalized images
The coordinate of point and bottom right angle point, label information refer to whether target belongs to the classification information of lesion and lesion developing stage.Instruction
Practice module and pre-training is carried out on fruit tree growing way sample set based on neural network, it is micro- that parameter is then carried out on training sample set
It adjusts, obtains the advanced features of sample set image, be input to next layer network training module.Diagnostic module is mentioned from normalized images
Candidate region is taken, position and the classification information of prediction twig and pest and disease damage are carried out using amplitude characteristics of image, directly study image
Global information；The object detection method of candidate frame, the comprehensive score by screening candidate frame is realized, by setting for each candidate frame
Reliability is multiplied with the classification information that candidate frame is predicted, obtains comprehensive score, then carry out non-maxima suppression processing, with constantly repeatedly
Generation progress, the uninterrupted prediction block of parameter, closest to true frame, the true frame location information of final output and classification information.At image
It manages module and format discriminance is carried out to training image or diagnostic image, and required to carry out format conversion according to normalized images, simultaneously
The resolution ratio of training image or diagnostic image is calculated, and the standardization figure to resolution ratio lower than given threshold is obtained again
It takes.
The smart machine that precisely plantation module can remotely manage orchard carries out specified farming operations to target fruit tree, is fruit tree
Profile module generates the implementation section of decision, and records operation log and carry out result estimate, and the utilization rate of resource can be improved
Avoid manpower consumption.The liquid manure decision guidance that water-fertilizer integrated intelligent irrigation equipment is provided according to fruit tree health account, by user
Corresponding scheme is selected in the system of wisdom orchard and is executed, and water-fertilizer integrated intelligent irrigation system can choose different fertilizer
Type, and corresponding liquid manure proportion is carried out, the specified equipment of long-range control carries out operation to target fruit tree and realizes precisely science kind
Planting management, such as pour water, apply agricultural.Unmanned plane fertilising specifies unmanned plane after user setting fertilizer type and liquid manure proportion
Device intelligence planning sprinkling route, carries out the fertilising work of scientific and efficient rate.The sound of different-waveband can be set in infrasound expelling parasite
Wave carries out corresponding expelling parasite bird repellent, assigned work duration and opening of device work, accomplishes that fining is intelligent.Intelligent weeding machine
People can carry out weeding work, and the management time of specified garden in the designated area after long-range starting.Automation equipment
Every time execute operation after, can all be recorded in operation log, and can prejudge influence situation of the operation to target fruit tree and
Fructus lycii yield is estimated, real-time recording unit aging conditions, and recommendation apparatus updates replacement.
Wisdom orchard system for improve crop yield, instruct scientific system plantation, increase peasant income have it is important
Function and significance.
In the present invention, in conjunction with the climatic information of garden location, soil regime, market information over the years is planted
Planning provides alternative, and specific implementation is selected by peasant household.
The above is only a preferred embodiment of the present invention, it is not intended to restrict the invention, it is noted that for this skill
For the those of ordinary skill in art field, without departing from the technical principles of the invention, can also make it is several improvement and
Modification, these improvements and modifications also should be regarded as protection scope of the present invention.
Claims (8)
1. a kind of wisdom orchard planting management system characterized by comprising
Garden Planning module obtains the market information of campus environment data and fruit tree over the years, according to campus environment data and
The market information output garden of fruit tree over the years raises fruit trees the alternative of the selection of kind and the division of planting area；
Fruit tree profile module constructs fruit tree growth model, is that every fruit tree creates health account and record operation log, record is simultaneously
The upgrowth situation for predicting fruit tree carries out differentiated processing, guidance science according to meteorological data and the actual conditions of plant growth
Crop rotation；
Precisely plantation module realizes that the long-distance intelligent control designated equipment for accurately distinguishing is managed to fruit tree, including trickle irrigation,
Sprinkling irrigation, water-fertilizer integral, pinpoints spray, sound wave expelling parasite plantation operation from robot walking Tree Precise Fertilization/deinsectization, unmanned plane at slight irrigation.
2. wisdom orchard planting management system according to claim 1, which is characterized in that the Garden Planning module packet
It includes: garden information acquisition unit, agricultural data acquisition unit, intellectual analysis unit and editing equipment management unit, wherein garden
Information acquisition unit, multiple wireless sensors including carrying out campus environment real time information sampling, the wireless sensor packet
Include Temperature Humidity Sensor, temperature sensor, air quality sensor, soil temperature-moisture sensor, soil moisture sensor, dioxy
Change carbon sensor, light intensity sensor, oxygen sensor；Agricultural data acquisition unit obtains the market information of fruit tree over the years；
The intellectual analysis unit, according to the market information of campus environment data and fruit tree, planning of science activities garden, reasonable selection fruit
Set kind.
The editing equipment management unit provides decision according to intellectual analysis unit, and the long-range designated equipment that controls is poured water, applied
The farming operations such as fertilizer, expelling parasite.
3. wisdom orchard planting management system according to claim 1, which is characterized in that the fruit tree profile module, packet
Garden information acquisition unit, pomology information acquisition unit, climatic information acquisition unit, Fruit tree model unit and analytical unit are included,
Wherein,
Fruit tree model module is neural network model to be constructed, by fruit tree growth sample set based on fruit tree growing way sample set
Image analyzed, obtain fruit tree growth model, judge growth cycle locating for fruit tree and production circannian stage,
In, growing way sample set is one group of fruit tree growing way picture set, for training growth model, can judge fruit with the growth model later
Set growing way and health status；
Garden information collection obtains the real time environmental data of garden by wireless sensor；
Pomology information acquisition unit obtains the upgrowth situation information and health of every plant of fruit tree by wireless sensor, Image Acquisition
Information, wherein one group of fruit tree can be inputted by information, fruit tree growth models such as fruit tree plant height, stem thickness, acquisition picture, soil datas
Information, to the upgrowth situation and health status judged；
Climatic information acquisition unit obtains garden location weather using data collection, data mining technology, weather history is believed
Breath and the following relevant weather information；
Fruit tree model unit, upgrowth situation to fruit tree and ecological environment carry out real-time monitoring, found health account, used in record
Equipment operation record monitors fruit tree growing way situation, prediction output of the fruit tree in real time according to collected information, and is produced according to fruit tree
Amount and health status predict ecological crop rotation；
There is abnormal and fruit tree health status when something goes wrong in campus environment data, utilizes artificial intelligence technology in analytical unit
It provides reasonable treatment measures to propose, be selected for plant personnel, realize the specific aim plantation of differentiated, wherein fruit tree plant height, stem
Slightly, picture, soil data information are acquired, fruit tree growth model can input the information of one group of fruit tree, to the upgrowth situation judged
And health status.
4. wisdom orchard planting management system according to claim 3, which is characterized in that the fruit tree profile module is also wrapped
Correction subelement is included, the farming for correcting fruit tree profile module for agricultural production personnel instructs as a result, and will correct data feedback
To the neural metwork training module of fruit tree growth model, neural network model training module is based on amendment data and carries out neural network
The optimization of diagnostic model.
5. wisdom orchard planting management system according to claim 4, which is characterized in that the neural network model of fruit tree is instructed
Practicing module includes sample acquisition module, image processing module, training module, optimization module, sample acquisition module, for obtaining fruit
Set the training image inside growing way figure sample set；
Image processing module obtains normalized images for carrying out standardization processing to training image；
Training module for analyzing obtained normalized images, and combines the farming number in fruit tree growing way figure sample set
According to continual analysis training is carried out, Neural Network Diagnosis model is obtained；
Correction module, user when for by Fruit tree model guidance plantation and farming operations correct data feedback to neural network and instruct
Practice module, neural metwork training module is made to carry out the optimization of Neural Network Diagnosis model based on amendment data.
6. wisdom orchard planting management system according to claim 5, which is characterized in that Fruit tree model diagnostic module includes
Object acquisition module, image processing module, diagnostic module are diagnosed,
Object acquisition module is diagnosed, for obtaining the diagnostic image with diagnosis object, wisdom orchard system is adopted by image information
The upgrowth situation information image of collection technology acquisition fruit tree；
Image processing module obtains normalized images for carrying out standardization processing to diagnostic image；
Standardization processing include to acquisition image twig and leaf lesion point be labeled, to it is described obtained training image into
Row batch processing, including unified format, equalization and denoising, then extract candidate frame and pre-training.The twig and leaf
The normalized images that training image obtains that are labeled as of lesion point carry out twig and diseased region feature mark, form twig and disease
Become position label, label information includes period and diseased region locating for lesion；
Mark further includes the seat of the upper left angle point and bottom right angle point of label information and twig and lesion point in the normalized images
Mark, label information refer to whether target belongs to the classification information of pest and disease damage and pest and disease damage developing stage；The mark is equal by K
Value-based algorithm carries out clustering to the initial candidate frame chosen by hand in self-control data set, finds the statistical law of candidate frame, with
Clustering number k is anchor several, and the wide high parameter of k cluster centre box of being subject to corrects anchor, is obtained and standardization figure
The most similar initial candidate frame parameter of lesion shape as in.
7. wisdom orchard planting management system according to claim 6, which is characterized in that training module uses convolutional Neural
Network carries out pre-training on fruit tree growth situation sample set first, and small parameter perturbations are then carried out on training sample set, obtains
The image advanced features of brothers' fruit tree image are output to next layer of neural metwork training module；Diagnostic module is used to combine orchard
Ecological data analyzes normalized images, and is analyzed to obtain fruit tree growth situation progress farming according to neural network model
Guidance；Diagnostic module utilizes multilayer neural network, by extracting candidate region from normalized images；Utilize entire image feature
To predict twig and pest and disease damage position and classification information, the directly global information of study image；The object detection method of candidate frame,
Comprehensive score by screening candidate frame embodies, and the classification information that the confidence level of each candidate frame selects frame to predict with after is multiplied,
Comprehensive score is obtained, then carries out non-maxima suppression processing, as continuous iteration is in progress, the uninterrupted prediction block of parameter is gradually connect
Nearly true frame, the true frame location information of final output and classification information；Image processing module to training image or diagnostic image into
Row format differentiates, and is required to carry out format conversion according to normalized images, while calculating the resolution of training image or diagnostic image
Rate, and the standardization figure to resolution ratio lower than given threshold reacquires.
8. wisdom orchard planting management system according to claim 1, which is characterized in that the accurate plantation module, packet
Include central processing, equipment management and log feedback, wherein
Central processing module, the specified equipment of long-range control irrigates specified fruit tree, is applied fertilizer depending on the user's operation
Operation；
Device management module is linked into all types of equipment of orchard drip irrigation and spray irrigation, water-fertilizer integral, intelligent robot, remotely
Control mechanical equipment；
The usage log of equipment is fed back to wisdom orchard system, and the health account of abundant fruit tree by log feedback module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810983208.8A CN109191074A (en) | 2018-08-27 | 2018-08-27 | Wisdom orchard planting management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810983208.8A CN109191074A (en) | 2018-08-27 | 2018-08-27 | Wisdom orchard planting management system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109191074A true CN109191074A (en) | 2019-01-11 |
Family
ID=64916310
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810983208.8A Pending CN109191074A (en) | 2018-08-27 | 2018-08-27 | Wisdom orchard planting management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109191074A (en) |
Cited By (53)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109766957A (en) * | 2019-03-19 | 2019-05-17 | 广东省农业科学院农业生物基因研究中心 | A kind of method that wisdom efficiently monitors Germplasm Resources of Farm Crop breeding time |
| CN109816267A (en) * | 2019-01-31 | 2019-05-28 | 中国农业科学院农业信息研究所 | A kind of intelligence Soybean production management method and system |
| CN109886259A (en) * | 2019-02-22 | 2019-06-14 | 潍坊科技学院 | A kind of tomato disease based on computer vision identification method for early warning and device |
| CN109874584A (en) * | 2019-03-19 | 2019-06-14 | 广州辰轩农业科技有限公司 | A kind of fruit tree growing way monitoring system based on deep learning convolutional neural networks |
| CN109948535A (en) * | 2019-03-19 | 2019-06-28 | 广东省农业科学院农业生物基因研究中心 | A kind of wisdom monitoring method of crop seed production primary growth stage fertilizer and water condition |
| CN109961035A (en) * | 2019-03-19 | 2019-07-02 | 广东省农业科学院水稻研究所 | A kind of rice plant of tillering stage growing way wisdom monitoring adjusting method |
| CN109991911A (en) * | 2019-05-05 | 2019-07-09 | 西安邮电大学 | A kind of orchard comprehensive monitoring system based on Internet of Things |
| CN110100557A (en) * | 2019-06-19 | 2019-08-09 | 南京农业大学 | A kind of Cold region apple fruit tree based on AR determines cave-applied fertilizer teleoperation method |
| CN110472557A (en) * | 2019-08-13 | 2019-11-19 | 深圳市睿海智电子科技有限公司 | A kind of method and device of tomato growth monitoring |
| CN110579987A (en) * | 2019-09-10 | 2019-12-17 | 西京学院 | intelligent orchard information control system and method based on LORA communication |
| CN110619062A (en) * | 2019-09-18 | 2019-12-27 | 宁夏大学 | Intelligent agricultural production data monitoring and early warning control system and method |
| CN110659860A (en) * | 2019-09-21 | 2020-01-07 | 深圳市创新盈科技有限公司 | Intelligent agricultural system |
| CN110771442A (en) * | 2019-09-30 | 2020-02-11 | 大气候物联网科技(广州)有限公司 | Agricultural planting method and system based on Internet of things |
| CN110839519A (en) * | 2019-12-02 | 2020-02-28 | 成都信息工程大学 | Internet of things intelligent agricultural irrigation device and method based on deep learning |
| CN110915470A (en) * | 2019-12-11 | 2020-03-27 | 常德鑫芙蓉环保有限公司 | Tobacco leaf pest control system |
| CN111207789A (en) * | 2020-01-08 | 2020-05-29 | 四川轻化工大学 | Wireless data acquisition terminal and method suitable for fruit and vegetable planting environment |
| CN111508060A (en) * | 2020-03-24 | 2020-08-07 | 深圳市天天学农网络科技有限公司 | Crop fertilization and pesticide application demonstration method, device and system based on 3D model |
| CN111680927A (en) * | 2020-06-13 | 2020-09-18 | 南京柯姆威科技有限公司 | AI artificial intelligence training device and training method thereof |
| CN111722561A (en) * | 2020-06-03 | 2020-09-29 | 湖南杨氏鲜果有限公司 | Intelligent garden management and control system |
| CN111998891A (en) * | 2020-07-22 | 2020-11-27 | 宁夏农林科学院农业经济与信息技术研究所（宁夏农业科技图书馆） | Wolfberry growth information acquisition system and method based on mobile phone APP |
| CN112042449A (en) * | 2020-09-17 | 2020-12-08 | 山西农业大学 | Method for controlling aphids in apple orchard based on Chinese rice lacewing |
| CN112327687A (en) * | 2020-10-27 | 2021-02-05 | 贵州贵谷农业股份有限公司 | Orchard integrated intelligent monitoring system |
| CN112330149A (en) * | 2020-11-04 | 2021-02-05 | 北京博创联动科技有限公司 | Crop management system, method and device and electronic equipment |
| CN112418473A (en) * | 2019-08-20 | 2021-02-26 | 阿里巴巴集团控股有限公司 | Crop information processing method, device, equipment and computer storage medium |
| 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 |
| CN112508042A (en) * | 2020-12-23 | 2021-03-16 | 安徽理工大学 | Orchard management system based on fuzzy recognition technology and image recognition technology |
| CN112562074A (en) * | 2021-02-25 | 2021-03-26 | 中国建筑西南设计研究院有限公司 | Intelligent green land health judgment method and maintenance management method |
| CN112561367A (en) * | 2020-12-22 | 2021-03-26 | 湖北良顷农业科技有限公司 | Intelligent planting decision control system |
| CN112711900A (en) * | 2020-12-02 | 2021-04-27 | 济南浪潮高新科技投资发展有限公司 | Crop digital twin modeling method |
| CN112836623A (en) * | 2021-01-29 | 2021-05-25 | 北京农业智能装备技术研究中心 | Facility tomato farming decision auxiliary method and device |
| CN112836678A (en) * | 2021-03-02 | 2021-05-25 | 成都亚讯星科科技股份有限公司 | Intelligent planning method for intelligent agricultural park |
| CN113077452A (en) * | 2021-04-09 | 2021-07-06 | 电子科技大学成都学院 | Apple tree pest and disease detection method based on DNN network and spot detection algorithm |
| CN113221723A (en) * | 2021-05-08 | 2021-08-06 | 余治梅 | Traceable self-feedback learning urban plant factory |
| CN113379188A (en) * | 2021-05-06 | 2021-09-10 | 贵州省烟草公司贵阳市公司 | Tobacco crop rotation planting method and system based on Internet of things |
| CN113608551A (en) * | 2021-08-11 | 2021-11-05 | 成都信息工程大学 | Unmanned agricultural machinery group cooperation system and application method thereof |
| CN113748389A (en) * | 2019-02-26 | 2021-12-03 | Wago管理有限责任公司 | Method and device for monitoring industrial process steps |
| CN113778110A (en) * | 2021-11-11 | 2021-12-10 | 山东中天宇信信息技术有限公司 | Intelligent agricultural machine control method and system based on machine learning |
| CN114442705A (en) * | 2021-12-31 | 2022-05-06 | 浙江优控云科技有限公司 | Intelligent agricultural system based on Internet of things and control method |
| CN114488921A (en) * | 2022-04-14 | 2022-05-13 | 山东省农业机械科学研究院 | Internet of things management and control system for intelligent orchard |
| CN114581783A (en) * | 2022-05-06 | 2022-06-03 | 中国计量大学 | Intelligent vehicle and intelligent agricultural management system |
| CN114584358A (en) * | 2022-02-25 | 2022-06-03 | 安捷光通科技成都有限公司 | Intelligent network security system, device and storage medium based on Bayesian regularization |
| CN114750166A (en) * | 2022-05-31 | 2022-07-15 | 南通理工学院 | Orchard intelligent management robot control system and method based on wireless sensor network |
| CN114766333A (en) * | 2022-05-09 | 2022-07-22 | 邢台市农业科学研究院 | Fruit tree plant networking regulation and control system |
| CN115004994A (en) * | 2022-06-16 | 2022-09-06 | 广西鸣鸣水果有限公司 | Fruit tree pest control system based on digital map |
| CN115245122A (en) * | 2021-12-21 | 2022-10-28 | 重庆三峡学院 | Accurate cultivation system suitable for thunberg fritillary bulb is planted |
| CN115294518A (en) * | 2022-07-18 | 2022-11-04 | 广东省农业科学院环境园艺研究所 | Intelligent monitoring method and system for accurate cultivation of horticultural plants in greenhouse |
| CN115599028A (en) * | 2022-12-14 | 2023-01-13 | 山东捷瑞数字科技股份有限公司（Cn） | Intelligent rural area system based on digital twinning technology and regulation and control method |
| CN116649123A (en) * | 2023-06-02 | 2023-08-29 | 上海华维可控农业科技集团股份有限公司 | Full-period automatic vegetable planting equipment and system |
| CN116843164A (en) * | 2023-08-29 | 2023-10-03 | 北京博创联动科技有限公司 | Agricultural machinery intelligent control system based on image analysis |
| CN117036954A (en) * | 2023-08-22 | 2023-11-10 | 生态环境部南京环境科学研究所 | Plant area growth condition identification method and system |
| CN117197595A (en) * | 2023-11-08 | 2023-12-08 | 四川省农业科学院农业信息与农村经济研究所 | Fruit tree growth period identification method, device and management platform based on edge calculation |
| CN117178862A (en) * | 2023-11-08 | 2023-12-08 | 四川嘉陵江桐子壕航电开发有限公司 | Garden watering information acquisition and monitoring method |
| CN117280290A (en) * | 2021-01-04 | 2023-12-22 | 心田农业技术有限公司 | Determining drive system anomalies based on power and/or current changes in irrigation systems |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105183054A (en) * | 2015-09-30 | 2015-12-23 | 江苏农林职业技术学院 | Remote precise irrigation and fertilization system and method for orchard |
| US20160217228A1 (en) * | 2015-01-23 | 2016-07-28 | Iteris, Inc. | Modeling of crop growth for desired moisture content of targeted livestock feedstuff for determination of harvest windows using field-level diagnosis and forecasting of weather conditions and observations and user input of harvest condition states |
| CN106097131A (en) * | 2016-06-23 | 2016-11-09 | 桂林电子科技大学 | Agriculturals based on big data precisely implantation methods |
| CN106778888A (en) * | 2016-12-27 | 2017-05-31 | 浙江大学 | A kind of orchard pest and disease damage survey system and method based on unmanned aerial vehicle remote sensing |
| CN106777683A (en) * | 2016-12-14 | 2017-05-31 | 天津市农业技术推广站 | A kind of crop growth of cereal crop seedlings monitoring system and method |
-
2018
- 2018-08-27 CN CN201810983208.8A patent/CN109191074A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160217228A1 (en) * | 2015-01-23 | 2016-07-28 | Iteris, Inc. | Modeling of crop growth for desired moisture content of targeted livestock feedstuff for determination of harvest windows using field-level diagnosis and forecasting of weather conditions and observations and user input of harvest condition states |
| CN105183054A (en) * | 2015-09-30 | 2015-12-23 | 江苏农林职业技术学院 | Remote precise irrigation and fertilization system and method for orchard |
| CN106097131A (en) * | 2016-06-23 | 2016-11-09 | 桂林电子科技大学 | Agriculturals based on big data precisely implantation methods |
| CN106777683A (en) * | 2016-12-14 | 2017-05-31 | 天津市农业技术推广站 | A kind of crop growth of cereal crop seedlings monitoring system and method |
| CN106778888A (en) * | 2016-12-27 | 2017-05-31 | 浙江大学 | A kind of orchard pest and disease damage survey system and method based on unmanned aerial vehicle remote sensing |
Cited By (66)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109816267A (en) * | 2019-01-31 | 2019-05-28 | 中国农业科学院农业信息研究所 | A kind of intelligence Soybean production management method and system |
| CN109886259A (en) * | 2019-02-22 | 2019-06-14 | 潍坊科技学院 | A kind of tomato disease based on computer vision identification method for early warning and device |
| CN113748389A (en) * | 2019-02-26 | 2021-12-03 | Wago管理有限责任公司 | Method and device for monitoring industrial process steps |
| CN109766957A (en) * | 2019-03-19 | 2019-05-17 | 广东省农业科学院农业生物基因研究中心 | A kind of method that wisdom efficiently monitors Germplasm Resources of Farm Crop breeding time |
| CN109874584A (en) * | 2019-03-19 | 2019-06-14 | 广州辰轩农业科技有限公司 | A kind of fruit tree growing way monitoring system based on deep learning convolutional neural networks |
| CN109948535A (en) * | 2019-03-19 | 2019-06-28 | 广东省农业科学院农业生物基因研究中心 | A kind of wisdom monitoring method of crop seed production primary growth stage fertilizer and water condition |
| CN109961035A (en) * | 2019-03-19 | 2019-07-02 | 广东省农业科学院水稻研究所 | A kind of rice plant of tillering stage growing way wisdom monitoring adjusting method |
| CN109991911A (en) * | 2019-05-05 | 2019-07-09 | 西安邮电大学 | A kind of orchard comprehensive monitoring system based on Internet of Things |
| CN110100557A (en) * | 2019-06-19 | 2019-08-09 | 南京农业大学 | A kind of Cold region apple fruit tree based on AR determines cave-applied fertilizer teleoperation method |
| CN110472557A (en) * | 2019-08-13 | 2019-11-19 | 深圳市睿海智电子科技有限公司 | A kind of method and device of tomato growth monitoring |
| CN112418473A (en) * | 2019-08-20 | 2021-02-26 | 阿里巴巴集团控股有限公司 | Crop information processing method, device, equipment and computer storage medium |
| 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 |
| CN110579987A (en) * | 2019-09-10 | 2019-12-17 | 西京学院 | intelligent orchard information control system and method based on LORA communication |
| CN110619062A (en) * | 2019-09-18 | 2019-12-27 | 宁夏大学 | Intelligent agricultural production data monitoring and early warning control system and method |
| CN110659860A (en) * | 2019-09-21 | 2020-01-07 | 深圳市创新盈科技有限公司 | Intelligent agricultural system |
| CN110771442A (en) * | 2019-09-30 | 2020-02-11 | 大气候物联网科技(广州)有限公司 | Agricultural planting method and system based on Internet of things |
| CN110839519A (en) * | 2019-12-02 | 2020-02-28 | 成都信息工程大学 | Internet of things intelligent agricultural irrigation device and method based on deep learning |
| CN110915470A (en) * | 2019-12-11 | 2020-03-27 | 常德鑫芙蓉环保有限公司 | Tobacco leaf pest control system |
| CN111207789A (en) * | 2020-01-08 | 2020-05-29 | 四川轻化工大学 | Wireless data acquisition terminal and method suitable for fruit and vegetable planting environment |
| CN111508060A (en) * | 2020-03-24 | 2020-08-07 | 深圳市天天学农网络科技有限公司 | Crop fertilization and pesticide application demonstration method, device and system based on 3D model |
| CN111722561A (en) * | 2020-06-03 | 2020-09-29 | 湖南杨氏鲜果有限公司 | Intelligent garden management and control system |
| CN111680927A (en) * | 2020-06-13 | 2020-09-18 | 南京柯姆威科技有限公司 | AI artificial intelligence training device and training method thereof |
| CN111998891A (en) * | 2020-07-22 | 2020-11-27 | 宁夏农林科学院农业经济与信息技术研究所（宁夏农业科技图书馆） | Wolfberry growth information acquisition system and method based on mobile phone APP |
| CN112042449A (en) * | 2020-09-17 | 2020-12-08 | 山西农业大学 | Method for controlling aphids in apple orchard based on Chinese rice lacewing |
| CN112327687A (en) * | 2020-10-27 | 2021-02-05 | 贵州贵谷农业股份有限公司 | Orchard integrated intelligent monitoring system |
| CN112330149A (en) * | 2020-11-04 | 2021-02-05 | 北京博创联动科技有限公司 | Crop management system, method and device and electronic equipment |
| CN112711900A (en) * | 2020-12-02 | 2021-04-27 | 济南浪潮高新科技投资发展有限公司 | Crop digital twin modeling method |
| CN112561367A (en) * | 2020-12-22 | 2021-03-26 | 湖北良顷农业科技有限公司 | Intelligent planting decision control system |
| CN112508042A (en) * | 2020-12-23 | 2021-03-16 | 安徽理工大学 | Orchard management system based on fuzzy recognition technology and image recognition technology |
| CN117280290A (en) * | 2021-01-04 | 2023-12-22 | 心田农业技术有限公司 | Determining drive system anomalies based on power and/or current changes in irrigation systems |
| CN117280290B (en) * | 2021-01-04 | 2024-03-29 | 心田农业技术有限公司 | Determining drive system anomalies based on power and/or current changes in irrigation systems |
| CN112836623B (en) * | 2021-01-29 | 2024-04-16 | 北京农业智能装备技术研究中心 | Auxiliary method and device for agricultural decision of facility tomatoes |
| CN112836623A (en) * | 2021-01-29 | 2021-05-25 | 北京农业智能装备技术研究中心 | Facility tomato farming decision auxiliary method and device |
| CN112562074B (en) * | 2021-02-25 | 2021-05-04 | 中国建筑西南设计研究院有限公司 | Intelligent green land health judgment method and maintenance management method |
| CN112562074A (en) * | 2021-02-25 | 2021-03-26 | 中国建筑西南设计研究院有限公司 | Intelligent green land health judgment method and maintenance management method |
| CN112836678B (en) * | 2021-03-02 | 2022-11-22 | 成都亚讯星科科技股份有限公司 | Intelligent planning method for intelligent agricultural park |
| CN112836678A (en) * | 2021-03-02 | 2021-05-25 | 成都亚讯星科科技股份有限公司 | Intelligent planning method for intelligent agricultural park |
| CN113077452A (en) * | 2021-04-09 | 2021-07-06 | 电子科技大学成都学院 | Apple tree pest and disease detection method based on DNN network and spot detection algorithm |
| CN113077452B (en) * | 2021-04-09 | 2022-07-15 | 电子科技大学成都学院 | Apple tree pest and disease detection method based on DNN network and spot detection algorithm |
| CN113379188A (en) * | 2021-05-06 | 2021-09-10 | 贵州省烟草公司贵阳市公司 | Tobacco crop rotation planting method and system based on Internet of things |
| CN113221723A (en) * | 2021-05-08 | 2021-08-06 | 余治梅 | Traceable self-feedback learning urban plant factory |
| CN113221723B (en) * | 2021-05-08 | 2024-04-09 | 余治梅 | Traceable self-feedback learning urban plant factory |
| CN113608551A (en) * | 2021-08-11 | 2021-11-05 | 成都信息工程大学 | Unmanned agricultural machinery group cooperation system and application method thereof |
| CN113778110A (en) * | 2021-11-11 | 2021-12-10 | 山东中天宇信信息技术有限公司 | Intelligent agricultural machine control method and system based on machine learning |
| CN115245122A (en) * | 2021-12-21 | 2022-10-28 | 重庆三峡学院 | Accurate cultivation system suitable for thunberg fritillary bulb is planted |
| CN114442705A (en) * | 2021-12-31 | 2022-05-06 | 浙江优控云科技有限公司 | Intelligent agricultural system based on Internet of things and control method |
| CN114584358A (en) * | 2022-02-25 | 2022-06-03 | 安捷光通科技成都有限公司 | Intelligent network security system, device and storage medium based on Bayesian regularization |
| CN114584358B (en) * | 2022-02-25 | 2023-10-13 | 安捷光通科技成都有限公司 | Intelligent network security system, device and storage medium based on Bayesian regularization |
| CN114488921B (en) * | 2022-04-14 | 2022-08-16 | 山东省农业机械科学研究院 | Internet of things management and control system for intelligent orchard |
| CN114488921A (en) * | 2022-04-14 | 2022-05-13 | 山东省农业机械科学研究院 | Internet of things management and control system for intelligent orchard |
| US11889793B2 (en) | 2022-04-14 | 2024-02-06 | Shandong Academy Of Agricultural Machinery Sciences | Internet-of-things management and control system for intelligent orchard |
| CN114581783A (en) * | 2022-05-06 | 2022-06-03 | 中国计量大学 | Intelligent vehicle and intelligent agricultural management system |
| CN114766333A (en) * | 2022-05-09 | 2022-07-22 | 邢台市农业科学研究院 | Fruit tree plant networking regulation and control system |
| CN114750166A (en) * | 2022-05-31 | 2022-07-15 | 南通理工学院 | Orchard intelligent management robot control system and method based on wireless sensor network |
| CN115004994A (en) * | 2022-06-16 | 2022-09-06 | 广西鸣鸣水果有限公司 | Fruit tree pest control system based on digital map |
| CN115004994B (en) * | 2022-06-16 | 2023-12-08 | 广西鸣鸣水果有限公司 | Fruit tree plant diseases and insect pests prevention and control system based on digital map |
| CN115294518A (en) * | 2022-07-18 | 2022-11-04 | 广东省农业科学院环境园艺研究所 | Intelligent monitoring method and system for accurate cultivation of horticultural plants in greenhouse |
| CN115599028A (en) * | 2022-12-14 | 2023-01-13 | 山东捷瑞数字科技股份有限公司（Cn） | Intelligent rural area system based on digital twinning technology and regulation and control method |
| CN116649123B (en) * | 2023-06-02 | 2023-11-17 | 上海华维可控农业科技集团股份有限公司 | Full-period automatic vegetable planting equipment and system |
| CN116649123A (en) * | 2023-06-02 | 2023-08-29 | 上海华维可控农业科技集团股份有限公司 | Full-period automatic vegetable planting equipment and system |
| CN117036954A (en) * | 2023-08-22 | 2023-11-10 | 生态环境部南京环境科学研究所 | Plant area growth condition identification method and system |
| CN117036954B (en) * | 2023-08-22 | 2024-04-26 | 生态环境部南京环境科学研究所 | Plant area growth condition identification method and system |
| CN116843164A (en) * | 2023-08-29 | 2023-10-03 | 北京博创联动科技有限公司 | Agricultural machinery intelligent control system based on image analysis |
| CN117197595A (en) * | 2023-11-08 | 2023-12-08 | 四川省农业科学院农业信息与农村经济研究所 | Fruit tree growth period identification method, device and management platform based on edge calculation |
| CN117178862A (en) * | 2023-11-08 | 2023-12-08 | 四川嘉陵江桐子壕航电开发有限公司 | Garden watering information acquisition and monitoring method |
| CN117178862B (en) * | 2023-11-08 | 2024-02-02 | 四川嘉陵江桐子壕航电开发有限公司 | Garden watering information acquisition and monitoring method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109191074A (en) | Wisdom orchard planting management system | |
| RU2688234C1 (en) | Smart growing control method and smart device for growing | |
| Krishnan et al. | Robotics, IoT, and AI in the automation of agricultural industry: a review | |
| CN110347127A (en) | Crop planting mandatory system and method based on cloud service | |
| CN110579987A (en) | intelligent orchard information control system and method based on LORA communication | |
| CN108781926B (en) | Greenhouse irrigation system and method based on neural network prediction | |
| CN110771442A (en) | Agricultural planting method and system based on Internet of things | |
| KR20200029657A (en) | Farming automation system using crop image big data | |
| CN112450056A (en) | Water, fertilizer and pesticide integrated intelligent irrigation system based on machine learning algorithm | |
| CN113657751A (en) | Wisdom agricultural is produced and is melted comprehensive service platform | |
| Muniasamy | Machine learning for smart farming: a focus on desert agriculture | |
| Khanh et al. | Future of Smart Agriculture Techniques and Applications | |
| Avigal et al. | Learning seed placements and automation policies for polyculture farming with companion plants | |
| Singla et al. | IoT based monitoring for the growth of basil using machine learning | |
| CN110119901A (en) | A kind of planting asparagus comprehensive management platform and its management method | |
| Jain et al. | Ubiquitous sensor based intelligent system for net houses | |
| Tiwari et al. | Precision agriculture applications in horticulture. | |
| CN114779862A (en) | Intelligent grape planting environment monitoring system | |
| Kunhare et al. | Role of Artificial Intelligence in the Agricultural System | |
| Sharma et al. | Artificial Intelligence in Agriculture: A Literature Survey | |
| Ali et al. | Smart farming using ai towards bangladeshi agriculture | |
| Abul-Soud et al. | Smart Agriculture in Orchard Farms | |
| Wasatkar et al. | IoT and AI-based Smart Farm: Optimizing Crop Yield and Sustainability | |
| Chen et al. | Intelligent strawberry field management and control system | |
| CN114467380A (en) | Remotely and autonomously controlled tilling, irrigating and picking device and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190111 |