CN106503741A - Floristic recognition methods, identifying device and server - Google Patents
Floristic recognition methods, identifying device and server Download PDFInfo
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- CN106503741A CN106503741A CN201610932834.5A CN201610932834A CN106503741A CN 106503741 A CN106503741 A CN 106503741A CN 201610932834 A CN201610932834 A CN 201610932834A CN 106503741 A CN106503741 A CN 106503741A
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- G06F18/23—Clustering techniques
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- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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Abstract
The present invention proposes a kind of floristic recognition methods, identifying device and server, and wherein, the floristic recognition methods includes：The characteristic of the every kind of plant in various plants is obtained, according to the characteristic of every kind of plant, floristics identification model is constructed；When identification signal is received, the characteristic of plant to be identified is obtained, according to the floristics identification model, determine the target plant species that mates with the characteristic of the plant to be identified；Using the target plant species as the plant to be identified species.By technical scheme, the accuracy rate of floristics identification can be effectively improved, so that reading intelligent agriculture control is carried out according to the floristics for identifying.
Description
Technical field
The present invention relates to Agricultural Information processing technology field, in particular to a kind of floristic recognition methods, one
Plant floristic identifying device and a kind of server.
Background technology
In the scheme of current floristics identification, by way of mating plant picture, realize floristic knowledge
Not, but, the efficiency and accuracy rate of floristics in correlation technique identification is not high, and the experience of user is bad.
Therefore, the accuracy rate for how improving floristics identification becomes technical problem urgently to be resolved hurrily.
Content of the invention
The present invention is based on the problems referred to above, it is proposed that a kind of new technical scheme, can effectively improve floristics
The accuracy rate of identification.
In view of this, a first aspect of the present invention proposes a kind of floristic recognition methods, including：Obtain plant more
The characteristic of the every kind of plant in thing, according to the characteristic of every kind of plant, constructs floristics identification model；?
When receiving identification signal, obtain the characteristic of plant to be identified, according to the floristics identification model, determine with described
The target plant species of the characteristic coupling of plant to be identified；Using the target plant species as the plant to be identified
Species.
In the technical scheme, by building floristics identification model, plant species can be accurately and efficiently identified
Class, so as to carry out reading intelligent agriculture control according to the floristics for identifying, and then realizes intelligence plantation, intelligent pest and disease damage analysis,
Artificial operation is effectively reduced, cost of labor is reduced.
In above-mentioned technical proposal, it is preferable that the characteristic according to every kind of plant, floristics is constructed
The step of identification model, also include：According to preset rules, the characteristic of every kind of plant is converted into vector data, with
The floristics identification model is constructed according to the vector data.
In the technical scheme, by characteristic is converted into the vector data that computer is capable of identify that, in terms of facilitating
Calculation machine recognizes vector data to build floristics identification model, and the floristics identification model for constructing is also computer
It is capable of identify that.
In any of the above-described technical scheme, it is preferable that the characteristic according to every kind of plant, plant is constructed
The step of category identification model, specifically include：Cluster point is carried out to the characteristic of every kind of plant using Kmeans algorithms
Analysis, to construct the floristics identification model.
In the technical scheme, as Kmeans algorithms have simple, efficiently advantage, therefore, using Kmeans algorithms
Floristics identification model can be rapidly constructed, so as to ensure that the speed of model construction.
In any of the above-described technical scheme, it is preferable that the characteristic of every kind of plant includes one below or multiple
Combination：Leaf color, blade shape, blade dimensions, sepal color, sepal shape, sepal size, petal color, petal
Shape, petal size, the plant height of plant.
In the technical scheme, floristics identification model is constructed according to above characteristic, plant is ensure that
Accuracy during plantation identification.
A second aspect of the present invention proposes a kind of floristic identifying device, including：Construction unit is more for obtaining
The characteristic of the every kind of plant in plant is planted, according to the characteristic of every kind of plant, floristics identification mould is constructed
Type；Determining unit, for when identification signal is received, obtaining the characteristic of plant to be identified, according to the floristics
Identification model, determines the target plant species that mates with the characteristic of the plant to be identified；The determining unit is additionally operable to,
Using the target plant species as the plant to be identified species.
In the technical scheme, by building floristics identification model, plant species can be accurately and efficiently identified
Class, so as to carry out reading intelligent agriculture control according to the floristics for identifying, and then realizes intelligence plantation, intelligent pest and disease damage analysis,
Artificial operation is effectively reduced, cost of labor is reduced.
In above-mentioned technical proposal, it is preferable that the construction unit is additionally operable to, according to preset rules, often plant described
The characteristic of thing is converted into vector data, to construct the floristics identification model according to the vector data.
In the technical scheme, by characteristic is converted into the vector data that computer is capable of identify that, in terms of facilitating
Calculation machine recognizes vector data to build floristics identification model, and the floristics identification model for constructing is also computer
It is capable of identify that.
In any of the above-described technical scheme, it is preferable that the construction unit is specifically for using Kmeans algorithms to described
The characteristic of every kind of plant carries out cluster analysis, to construct the floristics identification model.
In the technical scheme, as Kmeans algorithms have simple, efficiently advantage, therefore, using Kmeans algorithms
Floristics identification model can be rapidly constructed, so as to ensure that the speed of model construction.
In any of the above-described technical scheme, it is preferable that the characteristic of every kind of plant includes one below or multiple
Combination：Leaf color, blade shape, blade dimensions, sepal color, sepal shape, sepal size, petal color, petal
Shape, petal size, the plant height of plant.
In the technical scheme, floristics identification model is constructed according to above characteristic, plant is ensure that
Accuracy during plantation identification.
A third aspect of the present invention proposes a kind of server, including the plant species any one of above-mentioned technical proposal
The identifying device of class, therefore, the server has and the floristic identifying device any one of above-mentioned technical proposal
Identical technique effect, will not be described here.
By technical scheme, the accuracy rate of floristics identification can be effectively improved.
Description of the drawings
Fig. 1 shows the schematic flow sheet of floristic according to an embodiment of the invention recognition methods；
Fig. 2 shows the structural representation of floristic according to an embodiment of the invention identifying device；
Fig. 3 shows the structural representation of server according to an embodiment of the invention.
Specific embodiment
In order to the above objects, features and advantages of the present invention can be more clearly understood that, below in conjunction with the accompanying drawings and concrete real
Apply mode to be further described in detail the present invention.It should be noted that in the case where not conflicting, the enforcement of the application
Feature in example and embodiment can be mutually combined.
A lot of details are elaborated in the following description in order to fully understand the present invention, but, the present invention may be used also
Implemented with being different from other modes described here using other, therefore, protection scope of the present invention is not by described below
Specific embodiment restriction.
Fig. 1 shows the schematic flow sheet of floristic according to an embodiment of the invention recognition methods.
As shown in figure 1, floristic according to an embodiment of the invention recognition methods, including：
Step 102, obtains the characteristic of the every kind of plant in various plants, according to the characteristic of every kind of plant
According to constructing floristics identification model.
Step 104, when identification signal is received, obtains the characteristic of plant to be identified, according to the floristics
Identification model, determines the target plant species that mates with the characteristic of the plant to be identified.
Step 106, using the target plant species as the plant to be identified species.
In the technical scheme, by building floristics identification model, plant species can be accurately and efficiently identified
Class, so as to carry out reading intelligent agriculture control according to the floristics for identifying, and then realizes intelligence plantation, intelligent pest and disease damage analysis,
Artificial operation is effectively reduced, cost of labor is reduced.
In above-mentioned technical proposal, it is preferable that step 102 also includes：According to preset rules, by the spy of every kind of plant
Data conversion is levied into vector data, the floristics identification model is constructed according to the vector data.
In the technical scheme, by characteristic is converted into the vector data that computer is capable of identify that, in terms of facilitating
Calculation machine recognizes vector data to build floristics identification model, and the floristics identification model for constructing is also computer
It is capable of identify that.
For example, (in the present embodiment, characteristic is the characteristic of three kinds of flag flowers of acquisition：The length of sepal and width
Degree, the length and width of petal), the species of three kinds of flag flowers is respectively：A, B and C.The characteristic of three kinds of flag flowers such as table
Shown in 1.
Table 1
| The length of sepal | The width of sepal | The length of petal | The width of petal | Species |
| 5.1 | 3.5 | 1.4 | 0.2 | A |
| 4.9 | 3 | 1.4 | 0.2 | A |
| 4.7 | 3.2 | 1.3 | 0.2 | A |
| 4.6 | 3.1 | 1.5 | 0.2 | A |
| 5 | 3.6 | 1.4 | 0.2 | A |
| 5.4 | 3.9 | 1.7 | 0.4 | A |
| 4.6 | 3.4 | 1.4 | 0.3 | A |
| 7 | 3.2 | 4.7 | 1.4 | B |
| 6.4 | 3.2 | 4.5 | 1.5 | B |
| 6.9 | 3.1 | 4.9 | 1.5 | B |
| 5.5 | 2.3 | 4 | 1.3 | B |
| 6.5 | 2.8 | 4.6 | 1.5 | B |
| 5.7 | 2.8 | 4.5 | 1.3 | B |
| 6.3 | 3.3 | 6 | 2.5 | C |
| 5.8 | 2.7 | 5.1 | 1.9 | C |
| 7.1 | 3 | 5.9 | 2.1 | C |
| 6.3 | 2.9 | 5.6 | 1.8 | C |
| 6.5 | 3 | 5.8 | 2.2 | C |
| 7.6 | 3 | 6.6 | 2.1 | C |
| 4.9 | 2.5 | 4.5 | 1.7 | C |
| 7.3 | 2.9 | 6.3 | 1.8 | C |
According to preset rules, (1 represents：The flag flower of A species, 2 represent：The flag flower of B species, 3 represent：The kite of C species
The decorative pattern at the end of a poem, article, etc. where there is a small blank space, the form of vector data is：The width species of the length petal of the width petal of the length sepal of sepal), often will plant
The characteristic of thing is converted into following vector data.
Above every a line is exactly a vector data, and such as 5.1 3.5 1.4 0.2 1 is exactly a vector data.
In any of the above-described technical scheme, it is preferable that the characteristic according to every kind of plant, plant is constructed
The step of category identification model, specifically include：Cluster point is carried out to the characteristic of every kind of plant using Kmeans algorithms
Analysis, to construct the floristics identification model.
In the technical scheme, as Kmeans algorithms have simple, efficiently advantage, therefore, using Kmeans algorithms
Floristics identification model can be rapidly constructed, so as to ensure that the speed of model construction.
The step of being clustered using Kmeans algorithms is included：First from n data object (in the present embodiment, data
Object refers to characteristic) in arbitrarily select K object as initial cluster center；And for other remaining objects, then root
According to their similarities (distance) with these cluster centres, (cluster centre institute's generation most like with which is assigned these to respectively
Table) cluster；Then the cluster centre (averages of all objects in the cluster) of each obtained new cluster is calculated again；Constantly repeat
This process is till canonical measure function starts convergence.Typically using mean square deviation as canonical measure function.Initial clustering
The characteristics of K object at center has following：Each cluster itself is compact as far as possible, and divides between each cluster as much as possible
Open.
For example, cluster point is carried out to the vector data of the characteristic of three kinds of flag flowers in above-mentioned using Kmeans algorithms
Analysis, the cluster centre of the tri- kinds of flag flowers of A, B, C in the floristics identification model for constructing is data below：
[5.799999999999999,2.716666666666667,4.533333333333333,
1.5333333333333332]
[4.8999999999999995,3.385714285714285,1.4428571428571426,
0.24285714285714283]
[6.874999999999999,3.05,5.725,1.925]
If the characteristic of plant to be identified is 6.7,3.3,5.7,2.1, according to above floristics identification model, can
To determine the flag flower of the species of the plant to be identified as C species.
In any of the above-described technical scheme, it is preferable that the characteristic of every kind of plant includes one below or multiple
Combination：Leaf color, blade shape, blade dimensions, sepal color, sepal shape, sepal size, petal color, petal
Shape, petal size, the plant height of plant.
In the technical scheme, floristics identification model is constructed according to above characteristic, plant is ensure that
Accuracy during plantation identification.
Fig. 2 shows the structural representation of floristic according to an embodiment of the invention identifying device.
As shown in Fig. 2 floristic according to an embodiment of the invention identifying device 200, including：Construction unit 202
With determining unit 204.
Construction unit 202, for obtaining the characteristic of the every kind of plant in various plants, according to every kind of plant
Characteristic, constructs floristics identification model；Determining unit 204, for when identification signal is received, obtaining to be identified
The characteristic of plant, according to the floristics identification model, determination is mated with the characteristic of the plant to be identified
Target plant species；The determining unit 204 is additionally operable to, using the target plant species as the plant to be identified kind
Class.
In the technical scheme, by building floristics identification model, plant species can be accurately and efficiently identified
Class, so as to carry out reading intelligent agriculture control according to the floristics for identifying, and then realizes intelligence plantation, intelligent pest and disease damage analysis,
Artificial operation is effectively reduced, cost of labor is reduced.
In above-mentioned technical proposal, it is preferable that the construction unit 202 is additionally operable to, according to preset rules, will be described every kind of
The characteristic of plant is converted into vector data, to construct the floristics identification model according to the vector data.
In the technical scheme, by characteristic is converted into the vector data that computer is capable of identify that, in terms of facilitating
Calculation machine recognizes vector data to build floristics identification model, and the floristics identification model for constructing is also computer
It is capable of identify that.
In any of the above-described technical scheme, it is preferable that the construction unit 202 is specifically for using Kmeans algorithms pair
The characteristic of every kind of plant carries out cluster analysis, to construct the floristics identification model.
In the technical scheme, as Kmeans algorithms have simple, efficiently advantage, therefore, using Kmeans algorithms
Floristics identification model can be rapidly constructed, so as to ensure that the speed of model construction.
In any of the above-described technical scheme, it is preferable that the characteristic of every kind of plant includes one below or multiple
Combination：Leaf color, blade shape, blade dimensions, sepal color, sepal shape, sepal size, petal color, petal
Shape, petal size, the plant height of plant.
In the technical scheme, floristics identification model is constructed according to above characteristic, plant is ensure that
Accuracy during plantation identification.
Fig. 3 shows the structural representation of server according to an embodiment of the invention.
As shown in figure 3, server 300 according to an embodiment of the invention, including any one of above-mentioned technical proposal
Floristic identifying device 200, therefore, the server 300 has and plant any one of above-mentioned technical proposal
The 200 identical technique effect of identifying device of species, will not be described here.
Technical scheme is described in detail above in association with accompanying drawing, by technical scheme, can be effective
Ground improves the accuracy rate of floristics identification, so as to carry out reading intelligent agriculture control according to the floristics for identifying.
In the present invention, term " first ", " second " are only used for the purpose for describing, and it is not intended that indicating or hint phase
To importance.For the ordinary skill in the art, above-mentioned term can be understood as the case may be in the present invention
Concrete meaning.
The preferred embodiments of the present invention are the foregoing is only, the present invention is not limited to, for the skill of this area
For art personnel, the present invention can have various modifications and variations.All within the spirit and principles in the present invention, made any repair
Change, equivalent, improvement etc., should be included within the scope of the present invention.
Claims (9)
1. a kind of floristic recognition methods, it is characterised in that include：
The characteristic of the every kind of plant in various plants is obtained, according to the characteristic of every kind of plant, plant is constructed
Category identification model；
When identification signal is received, the characteristic of plant to be identified is obtained, according to the floristics identification model, determined
The target plant species that mates with the characteristic of the plant to be identified；
Using the target plant species as the plant to be identified species.
2. floristic recognition methods according to claim 1, it is characterised in that described according to every kind of plant
Characteristic, the step of construct floristics identification model, also includes：
According to preset rules, the characteristic of every kind of plant is converted into vector data, with according to the vector data structure
Build out the floristics identification model.
3. floristic recognition methods according to claim 1, it is characterised in that described according to every kind of plant
Characteristic, the step of construct floristics identification model, specifically includes：
Cluster analysis is carried out to the characteristic of every kind of plant using Kmeans algorithms, is known with constructing the floristics
Other model.
4. floristic recognition methods according to any one of claim 1 to 3, it is characterised in that
The characteristic of every kind of plant includes one below or multiple combinations：Leaf color, blade shape, blade chi
Very little, sepal color, sepal shape, sepal size, petal color, petal shape, petal size, the plant height of plant.
5. a kind of floristic identifying device, it is characterised in that include：
Construction unit, for obtaining the characteristic of the every kind of plant in various plants, according to the characteristic of every kind of plant
According to constructing floristics identification model；
Determining unit, for when identification signal is received, obtaining the characteristic of plant to be identified, according to the floristics
Identification model, determines the target plant species that mates with the characteristic of the plant to be identified；
The determining unit is additionally operable to, using the target plant species as the plant to be identified species.
6. floristic identifying device according to claim 5, it is characterised in that the construction unit is additionally operable to,
According to preset rules, the characteristic of every kind of plant is converted into vector data, with according to the vector data structure
Build out the floristics identification model.
7. floristic identifying device according to claim 5, it is characterised in that the construction unit specifically for,
Cluster analysis is carried out to the characteristic of every kind of plant using Kmeans algorithms, is known with constructing the floristics
Other model.
8. the floristic identifying device according to any one of claim 5 to 7, it is characterised in that
The characteristic of every kind of plant includes one below or multiple combinations：Leaf color, blade shape, blade chi
Very little, sepal color, sepal shape, sepal size, petal color, petal shape, petal size, the plant height of plant.
9. a kind of server, it is characterised in that include：Floristic identification dress as any one of claim 5 to 8
Put.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610932834.5A CN106503741A (en) | 2016-10-31 | 2016-10-31 | Floristic recognition methods, identifying device and server |
| PCT/CN2017/106913 WO2018077111A1 (en) | 2016-10-31 | 2017-10-19 | Plant type recognition method, recognition apparatus and server |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610932834.5A CN106503741A (en) | 2016-10-31 | 2016-10-31 | Floristic recognition methods, identifying device and server |
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| CN106503741A true CN106503741A (en) | 2017-03-15 |
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| CN201610932834.5A Pending CN106503741A (en) | 2016-10-31 | 2016-10-31 | Floristic recognition methods, identifying device and server |
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| WO (1) | WO2018077111A1 (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909072A (en) * | 2017-09-29 | 2018-04-13 | 广东数相智能科技有限公司 | A kind of vegetation type recognition methods, electronic equipment, storage medium and device |
| WO2018077111A1 (en) * | 2016-10-31 | 2018-05-03 | 深圳前海弘稼科技有限公司 | Plant type recognition method, recognition apparatus and server |
| CN108256568A (en) * | 2018-01-12 | 2018-07-06 | 宁夏智启连山科技有限公司 | A kind of plant species identification method and device |
| CN109255338A (en) * | 2018-09-30 | 2019-01-22 | 南京林业大学 | The method of discrimination of Malus spectabilis kind and kind, device, storage medium and electronic equipment |
| CN110598583A (en) * | 2019-08-26 | 2019-12-20 | 恒大智慧科技有限公司 | Intelligent greening management method, intelligent greening control system and storage medium |
| CN111639750A (en) * | 2020-05-26 | 2020-09-08 | 珠海格力电器股份有限公司 | Control method and device of intelligent flowerpot, intelligent flowerpot and storage medium |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110347721A (en) * | 2019-07-08 | 2019-10-18 | 紫光云技术有限公司 | A kind of floristic analysing method of flag flower |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102741882A (en) * | 2010-11-29 | 2012-10-17 | 松下电器产业株式会社 | Image classification device, image classification method, program, recording media, integrated circuit, and model creation device |
| CN103617417A (en) * | 2013-11-25 | 2014-03-05 | 中国科学院深圳先进技术研究院 | Automatic plant identification method and system |
| CN104331704A (en) * | 2014-10-27 | 2015-02-04 | 合肥星服信息科技有限责任公司 | Plant identification method based on Haar characteristics |
| CN104346630A (en) * | 2014-10-27 | 2015-02-11 | 华南理工大学 | Cloud flower identifying method based on heterogeneous feature fusion |
| CN104361348A (en) * | 2014-10-27 | 2015-02-18 | 华南理工大学 | Flower and plant recognition method on intelligent terminal |
| CN106033435A (en) * | 2015-03-13 | 2016-10-19 | 北京贝虎机器人技术有限公司 | Article identification method and apparatus, and indoor map generation method and apparatus |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106503741A (en) * | 2016-10-31 | 2017-03-15 | 深圳前海弘稼科技有限公司 | Floristic recognition methods, identifying device and server |
-
2016
- 2016-10-31 CN CN201610932834.5A patent/CN106503741A/en active Pending
-
2017
- 2017-10-19 WO PCT/CN2017/106913 patent/WO2018077111A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102741882A (en) * | 2010-11-29 | 2012-10-17 | 松下电器产业株式会社 | Image classification device, image classification method, program, recording media, integrated circuit, and model creation device |
| CN103617417A (en) * | 2013-11-25 | 2014-03-05 | 中国科学院深圳先进技术研究院 | Automatic plant identification method and system |
| CN104331704A (en) * | 2014-10-27 | 2015-02-04 | 合肥星服信息科技有限责任公司 | Plant identification method based on Haar characteristics |
| CN104346630A (en) * | 2014-10-27 | 2015-02-11 | 华南理工大学 | Cloud flower identifying method based on heterogeneous feature fusion |
| CN104361348A (en) * | 2014-10-27 | 2015-02-18 | 华南理工大学 | Flower and plant recognition method on intelligent terminal |
| CN106033435A (en) * | 2015-03-13 | 2016-10-19 | 北京贝虎机器人技术有限公司 | Article identification method and apparatus, and indoor map generation method and apparatus |
Non-Patent Citations (2)
| Title |
|---|
| 朱颢东 等: "基于余弦定理和K-means的植物叶片识别方法", 《华中师范大学学报（自然科学版）》 * |
| 祁亨年 等: "基于叶片特征的计算机辅助植物识别模型", 《浙江林学院学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018077111A1 (en) * | 2016-10-31 | 2018-05-03 | 深圳前海弘稼科技有限公司 | Plant type recognition method, recognition apparatus and server |
| CN107909072A (en) * | 2017-09-29 | 2018-04-13 | 广东数相智能科技有限公司 | A kind of vegetation type recognition methods, electronic equipment, storage medium and device |
| CN108256568A (en) * | 2018-01-12 | 2018-07-06 | 宁夏智启连山科技有限公司 | A kind of plant species identification method and device |
| CN109255338A (en) * | 2018-09-30 | 2019-01-22 | 南京林业大学 | The method of discrimination of Malus spectabilis kind and kind, device, storage medium and electronic equipment |
| CN109255338B (en) * | 2018-09-30 | 2021-01-12 | 南京林业大学 | Method and device for distinguishing varieties and varieties of Chinese flowering crabapples, storage medium and electronic equipment |
| CN110598583A (en) * | 2019-08-26 | 2019-12-20 | 恒大智慧科技有限公司 | Intelligent greening management method, intelligent greening control system and storage medium |
| CN111639750A (en) * | 2020-05-26 | 2020-09-08 | 珠海格力电器股份有限公司 | Control method and device of intelligent flowerpot, intelligent flowerpot and storage medium |
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| Publication number | Publication date |
|---|---|
| WO2018077111A1 (en) | 2018-05-03 |
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