CN107969297A - A kind of method of intelligence plantation strawberry - Google Patents
A kind of method of intelligence plantation strawberry Download PDFInfo
- Publication number
- CN107969297A CN107969297A CN201711400427.0A CN201711400427A CN107969297A CN 107969297 A CN107969297 A CN 107969297A CN 201711400427 A CN201711400427 A CN 201711400427A CN 107969297 A CN107969297 A CN 107969297A
- Authority
- CN
- China
- Prior art keywords
- strawberry
- pictures
- sub
- images
- training set
- 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.)
- Withdrawn
Links
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/14—Greenhouses
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
- A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
- A01G9/246—Air-conditioning systems
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/25—Greenhouse technology, e.g. cooling systems therefor
Landscapes
- Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Environmental Sciences (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Cultivation Of Plants (AREA)
Abstract
A kind of method of intelligence plantation strawberry of disclosure of the invention, this method include：（1）Strawberry picture concerned is obtained, classification rower of going forward side by side draws；（2）The training set of images according to type classified closes；（3）Closed to obtain neural network model according to training set of images；（4）Video identification plants the stage of strawberry；（5）The stage self adaptive control environment obtained according to judgement.A kind of method of intelligence plantation strawberry provided by the invention, by the way that mistake will not be produced, the machine recognition of subjective consciousness will not be possessed to judge the growth phase of strawberry, so as to timely control the growing environment of strawberry to adapt to the growth phase of strawberry, so that strawberry can survive in the environment of optimum always, its yield and quality is finally greatly enhanced.
Description
Technical field
The present invention relates to a kind of method for planting strawberry, especially a kind of method of intelligence plantation strawberry.
Background technology
Strawberry is the food that people like eating.In order to meet it is increasingly increased to strawberry the needs of, it is necessary to largely plant
Strawberry.And to turn out strawberry just needs to pay attention to the growing environment of strawberry constantly.The environment of surrounding is only allowed to adapt to strawberry not
Requirement of the same time to environment could improve the probability of outcome of strawberry.
The process of existing plantation strawberry is all artificially to observe, and is judged according to the subjectivity of people so as to fulfill to environment
Random change.However, since the energy of people is limited, the growth phase of strawberry usually can not be timely judged.And even if
In face of same situation, different people can also make different judgements to the growing state of strawberry.Growth so to strawberry is
Extremely disadvantageous.
The content of the invention
Therefore, in view of the above-mentioned problems, the present invention provides a kind of method of intelligence plantation strawberry, by the way that mistake will not be produced
By mistake, the machine recognition of subjective consciousness will not be possessed to judge the growth phase of strawberry, so as to timely control the growth of strawberry
Environment adapts to the growth phase of strawberry, so that strawberry can survive in the environment of optimum always, finally greatly
Improve its yield and quality.
In order to achieve the above object, the present invention proposes a kind of method of intelligence plantation strawberry, it is characterised in that this method
Including：
(1) strawberry picture concerned is obtained, classification rower of going forward side by side draws；
(2) training set of images according to type classified closes；
(3) closed to obtain neural network model according to training set of images；
(4) stage of video identification plantation strawberry；
(5) the stage self adaptive control environment obtained according to judgement.
This intelligently plants the method for strawberry, it also meets condition, and step (1) specifically includes：Search and grass from internet
The relevant plurality of pictures of the certain kind of berries, and according to the contents of every pictures to showing that different types of part is split respectively in the picture
Sectional drawing forms multiple sub-pictures, and the species includes strawberry bud, strawberry, strawberry fruit, and carries out species to every sub-pictures
Mark, i.e., what the sub-pictures showed is any in strawberry bud, strawberry, strawberry fruit.
This intelligently plants the method for strawberry, it also meets condition, and step (2) specifically includes：Search is labeled as strawberry bud
All sub-pictures, form strawberry bud training set of images and close；Search is labeled as all sub-pictures of strawberry, forms strawberry floral diagram picture
Training set；Search is labeled as all sub-pictures of strawberry fruit, forms strawberry fruit training set of images and closes.
This intelligently plants the method for strawberry, it also meets condition, and step (3) specifically includes：By strawberry bud training set of images
Each sub-pictures in conjunction resolve into the subgraph overlapped according to certain tactic some, and each subgraph is defeated
Enter into a nervelet network, the output of the nervelet network be stored in a small ordered series of numbers, by all small ordered series of numbers according to
Certain order arrangement obtains a big ordered series of numbers, which is input in one big neutral net, and then obtain energy
Enough first nerves network models that strawberry bud is judged whether according to image；
Each sub-pictures during strawberry training set of images is closed are resolved into according to certain tactic some
The subgraph of coincidence, each subgraph is input in a nervelet network, and the output of the nervelet network is stored in one
In a small ordered series of numbers, all small ordered series of numbers are obtained into a big ordered series of numbers according to certain order arrangement, which is input to
In one big neutral net, and then obtain to judge whether the nervus opticus network model of strawberry according to image；
Each sub-pictures during strawberry fruit training set of images is closed are resolved into according to certain tactic multiple portions
Divide the subgraph overlapped, each subgraph is input in a nervelet network, the output of the nervelet network is stored in
In one small ordered series of numbers, all small ordered series of numbers are obtained into a big ordered series of numbers according to certain order arrangement, which is inputted
Into one big neutral net, and then obtain to judge whether the third nerve network mould of strawberry fruit according to image
Type.
This intelligently plants the method for strawberry, it also meets condition, and step (4) specifically includes：Shot and planted by camera
All sub-pictures by the picture segmentation into several partly overlapping sub-pictures, are input to by the panoramic pictures in strawberry base
One neural network model, counts if first nerves network model judges that a certain sub-pictures belong to strawberry bud and adds one, until complete
The judgement of paired all sub-pictures, output first count；All sub-pictures are input to nervus opticus network model, if second
Neural network model judges that a certain sub-pictures belong to strawberry and then count plus one, until the judgement to all sub-pictures is completed, it is defeated
Go out the second counting；All sub-pictures are input to third nerve network model, if third nerve network model judges a certain son
Picture belongs to strawberry fruit and then counts plus one, until completing the judgement to all sub-pictures, output the 3rd counts；If the first meter
The ratio that number accounts for the sum of first, second, third counting is more than a default value, then illustrates that strawberry is in the budding stage；If the
The ratio that two countings account for the sum of first, second, third counting is more than a default value, then illustrates that strawberry is in the flowering phase；Such as
The ratio that the counting of fruit the 3rd accounts for the sum of first, second, third counting is more than a default value, then illustrates that strawberry is in result rank
Section.
This intelligently plants the method for strawberry, it also meets condition, and step (5) specifically includes：When strawberry is in the budding stage
When, it is 26~28 DEG C to control plantation strawberry base daylight environment temperature, and nighttime ambient temperature is more than 8 DEG C；Opened when strawberry is in
When spending the stage, it is 20~25 DEG C to control plantation strawberry base daylight environment temperature, and nighttime ambient temperature is 10~12 DEG C；Work as strawberry
During in the result stage, it is 18~25 DEG C to control plantation strawberry base daylight environment temperature, and nighttime ambient temperature is 8 DEG C.
Embodiment
A kind of method of intelligence plantation strawberry, it is characterised in that this method includes：
(1) strawberry picture concerned is obtained, classification rower of going forward side by side draws；
Search and the relevant plurality of pictures of strawberry from internet, and according to the contents of every pictures to being shown in the picture
Different types of part carries out segmentation sectional drawing and forms multiple sub-pictures respectively, and the species includes strawberry bud, strawberry, strawberry fruit
It is real, and species mark is carried out to every sub-pictures, i.e., which in strawberry bud, strawberry, strawberry fruit what the sub-pictures showed is
It is a kind of；
The quantity of picture is enough, could make it that the neural network model that training obtains is more accurate；
(2) training set of images according to type classified closes；
Search is labeled as all sub-pictures of strawberry bud, forms strawberry bud training set of images and closes；Search is labeled as strawberry
All sub-pictures, form the conjunction of strawberry training set of images；Search is labeled as all sub-pictures of strawberry fruit, forms strawberry fruit
Real training set of images closes；
(3) closed to obtain neural network model according to training set of images；
Each sub-pictures during strawberry bud training set of images is closed are resolved into according to certain tactic some
The subgraph of coincidence, each subgraph is input in a nervelet network, and the output of the nervelet network is stored in one
In a small ordered series of numbers, all small ordered series of numbers are obtained into a big ordered series of numbers according to certain order arrangement, which is input to
In one big neutral net, and then obtain to judge whether the first nerves network model of strawberry bud according to image；
Each sub-pictures during strawberry training set of images is closed are resolved into according to certain tactic some
The subgraph of coincidence, each subgraph is input in a nervelet network, and the output of the nervelet network is stored in one
In a small ordered series of numbers, all small ordered series of numbers are obtained into a big ordered series of numbers according to certain order arrangement, which is input to
In one big neutral net, and then obtain to judge whether the nervus opticus network model of strawberry according to image；
Each sub-pictures during strawberry fruit training set of images is closed are resolved into according to certain tactic multiple portions
Divide the subgraph overlapped, each subgraph is input in a nervelet network, the output of the nervelet network is stored in
In one small ordered series of numbers, all small ordered series of numbers are obtained into a big ordered series of numbers according to certain order arrangement, which is inputted
Into one big neutral net, and then obtain to judge whether the third nerve network mould of strawberry fruit according to image
Type；
(4) stage of video identification plantation strawberry；
The panoramic pictures in plantation strawberry base are shot by camera, by the picture segmentation into several partly overlapping sons
All sub-pictures are input to first nerves network model by picture, if first nerves network model judges a certain sub-pictures category
Then counted in strawberry bud and add one, until completing the judgement to all sub-pictures, output first counts；All sub-pictures are input to
Nervus opticus network model, counts if nervus opticus network model judges that a certain sub-pictures belong to strawberry and adds one, until
The judgement to all sub-pictures is completed, output second counts；All sub-pictures are input to third nerve network model, if the
Three neural network models judge that a certain sub-pictures belong to strawberry fruit and then count plus one, until completing to sentence all sub-pictures
Disconnected, output the 3rd counts；If the ratio that the first counting accounts for the sum of first, second, third counting is more than a default value, say
Bright strawberry is in the budding stage；If the ratio that the second counting accounts for the sum of first, second, third counting is more than a default value,
Then illustrate that strawberry is in the flowering phase；If the ratio that the 3rd counting accounts for the sum of first, second, third counting is more than a present count
Value, then illustrate that strawberry is in the result stage；
Camera is preferably high-definition camera or super clear camera；Panoramic pictures are required to show plantation strawberry base
Ground all with the relevant details of strawberry, if plantation the larger whole capsules so that a panoramic pictures are had no idea in strawberry base
Include, then can shoot multiple independent pictures to replace panoramic pictures；In view of having advance germination, blooming, the part of result
Strawberry, therefore, there is provided default value and then avoids because the strawberry that part is developed in advance causes to make the stage residing for entirety
The judgement of mistake；Denominator need be first, second, third counting and, and and not all sub-pictures quantity, as it is likely that
What sub-pictures showed be in plantation strawberry base with the incoherent content of strawberry；
(5) the stage self adaptive control environment obtained according to judgement；
When strawberry is in the budding stage, it is 26~28 DEG C to control plantation strawberry base daylight environment temperature, night-environment
Temperature is more than 8 DEG C；When strawberry is in the flowering phase, it is 20~25 DEG C to control plantation strawberry base daylight environment temperature, night
Between environment temperature be 10~12 DEG C；When strawberry is in the result stage, control plantation strawberry base daylight environment temperature for 18~
25 DEG C, nighttime ambient temperature is 8 DEG C；
Be provided with different environmental parameters for the different phase residing for strawberry, enable to rudiment, bloom, result it is suitable
The progress of profit, improves the yield and quality of strawberry.
It should be noted that above content is that to combine specific embodiment made for the present invention further specifically
It is bright, it is impossible to assert that the embodiment of the present invention is only limitted to this, under the above-mentioned guidance of the present invention, those skilled in the art can
To carry out various improvement and deformation on the basis of above-described embodiment, and these are improved or deformation falls in protection model of the invention
In enclosing.
Claims (6)
- A kind of 1. method of intelligence plantation strawberry, it is characterised in that this method includes：（1）Strawberry picture concerned is obtained, classification rower of going forward side by side draws；（2）The training set of images according to type classified closes；（3）Closed to obtain neural network model according to training set of images；（4）Video identification plants the stage of strawberry；（5）The stage self adaptive control environment obtained according to judgement.
- 2. the method for intelligence plantation strawberry according to claim 2, it is characterised in that step（1）Specifically include：From interconnection Online search and the relevant plurality of pictures of strawberry, and according to the contents of every pictures to showing different types of part in the picture Segmentation sectional drawing is carried out respectively forms multiple sub-pictures, the species includes strawberry bud, strawberry, strawberry fruit, and to every son Picture carries out species mark, i.e., what the sub-pictures showed is any in strawberry bud, strawberry, strawberry fruit.
- 3. the method for intelligence plantation strawberry according to claim 2, it is characterised in that step（2）Specifically include：Search mark All sub-pictures for strawberry bud are noted, strawberry bud training set of images is formed and closes；Search is labeled as all sub-pictures of strawberry, shape Closed into strawberry training set of images；Search is labeled as all sub-pictures of strawberry fruit, forms strawberry fruit training set of images and closes.
- 4. the method for intelligence plantation strawberry according to claim 3, it is characterised in that step（3）Specifically include：By strawberry Each sub-pictures during bud training set of images closes resolve into the subgraph overlapped according to certain tactic some, will Each subgraph is input in a nervelet network, the output of the nervelet network is stored in a small ordered series of numbers, by institute There is small ordered series of numbers to obtain a big ordered series of numbers according to certain order arrangement, which is input to a big neutral net In, and then obtain to judge whether the first nerves network model of strawberry bud according to image；Each sub-pictures during strawberry training set of images is closed are resolved into be overlapped according to certain tactic some Subgraph, each subgraph is input in a nervelet network, the output of the nervelet network is stored in one small In ordered series of numbers, all small ordered series of numbers are obtained into a big ordered series of numbers according to certain order arrangement, which is input to one In big neutral net, and then obtain to judge whether the nervus opticus network model of strawberry according to image；Each sub-pictures during strawberry fruit training set of images is closed are resolved into according to certain tactic some weights The subgraph of conjunction, each subgraph is input in a nervelet network, and the output of the nervelet network is stored in one In small ordered series of numbers, all small ordered series of numbers are obtained into a big ordered series of numbers according to certain order arrangement, which is input to one In a big neutral net, and then obtain to judge whether the third nerve network model of strawberry fruit according to image.
- 5. the method for intelligence plantation strawberry according to claim 4, it is characterised in that step（4）Specifically include：By taking the photograph As the panoramic pictures in head shooting plantation strawberry base, by the picture segmentation into several partly overlapping sub-pictures, by all sons Picture is input to first nerves network model, is counted if first nerves network model judges that a certain sub-pictures belong to strawberry bud Add one, until completing the judgement to all sub-pictures, output first counts；All sub-pictures are input to nervus opticus network mould Type, counts if nervus opticus network model judges that a certain sub-pictures belong to strawberry and adds one, until completing to all subgraphs The judgement of piece, output second count；All sub-pictures are input to third nerve network model, if third nerve network model Judge that a certain sub-pictures belong to strawberry fruit and then count plus one, until completing the judgement to all sub-pictures, output the 3rd counts； If the ratio that the first counting accounts for the sum of first, second, third counting is more than a default value, illustrate that strawberry is in rudiment rank Section；If the ratio that the second counting accounts for the sum of first, second, third counting is more than a default value, illustrates that strawberry is in and open Spend the stage；If the ratio that the 3rd counting accounts for the sum of first, second, third counting is more than a default value, illustrate at strawberry In the result stage.
- 6. the method for intelligence plantation strawberry according to claim 5, it is characterised in that step（5）Specifically include：Work as strawberry During in the budding stage, it is 26~28 DEG C to control plantation strawberry base daylight environment temperature, and nighttime ambient temperature is more than 8 DEG C； When strawberry is in the flowering phase, it is 20~25 DEG C to control plantation strawberry base daylight environment temperature, nighttime ambient temperature 10 ~12 DEG C；When strawberry is in the result stage, it is 18~25 DEG C to control plantation strawberry base daylight environment temperature, night-environment temperature Spend for 8 DEG C.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711400427.0A CN107969297A (en) | 2017-12-20 | 2017-12-20 | A kind of method of intelligence plantation strawberry |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711400427.0A CN107969297A (en) | 2017-12-20 | 2017-12-20 | A kind of method of intelligence plantation strawberry |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107969297A true CN107969297A (en) | 2018-05-01 |
Family
ID=62007436
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711400427.0A Withdrawn CN107969297A (en) | 2017-12-20 | 2017-12-20 | A kind of method of intelligence plantation strawberry |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107969297A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110199845A (en) * | 2019-06-28 | 2019-09-06 | 西南林业大学 | A kind of plant conservation system based on Internet of Things and artificial intelligence |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001083385A2 (en) * | 2000-04-28 | 2001-11-08 | Atlantium Ltd. | Devices and method for production of traceable immunizing drinking water, other liquids and gas |
| CN101916382A (en) * | 2010-07-30 | 2010-12-15 | 广州中医药大学 | Method for recognizing image of plant leaf |
| CN103093247A (en) * | 2013-01-05 | 2013-05-08 | 山东华戎信息产业有限公司 | Automatic classification method for plant images |
| CN107357935A (en) * | 2017-08-15 | 2017-11-17 | 杭州萌橙科技有限公司 | Intelligent planting equipment control method based on program cloud |
| CN107980504A (en) * | 2017-12-18 | 2018-05-04 | 济宁百果生态农业科技有限公司 | A kind of method of intelligence plantation strawberry |
-
2017
- 2017-12-20 CN CN201711400427.0A patent/CN107969297A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001083385A2 (en) * | 2000-04-28 | 2001-11-08 | Atlantium Ltd. | Devices and method for production of traceable immunizing drinking water, other liquids and gas |
| CN101916382A (en) * | 2010-07-30 | 2010-12-15 | 广州中医药大学 | Method for recognizing image of plant leaf |
| CN103093247A (en) * | 2013-01-05 | 2013-05-08 | 山东华戎信息产业有限公司 | Automatic classification method for plant images |
| CN107357935A (en) * | 2017-08-15 | 2017-11-17 | 杭州萌橙科技有限公司 | Intelligent planting equipment control method based on program cloud |
| CN107980504A (en) * | 2017-12-18 | 2018-05-04 | 济宁百果生态农业科技有限公司 | A kind of method of intelligence plantation strawberry |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110199845A (en) * | 2019-06-28 | 2019-09-06 | 西南林业大学 | A kind of plant conservation system based on Internet of Things and artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kang et al. | From parallel plants to smart plants: intelligent control and management for plant growth | |
| Guo et al. | Aerial imagery analysis–quantifying appearance and number of sorghum heads for applications in breeding and agronomy | |
| US10796150B2 (en) | Automated diagnosis and treatment of crop infestations | |
| CN110009043A (en) | A kind of pest and disease damage detection method based on depth convolutional neural networks | |
| CN106406403A (en) | Agriculture management and control system based on augmented reality | |
| WO2020078396A1 (en) | Method for determining distribution information, and control method and device for unmanned aerial vehicle | |
| US20180157974A1 (en) | Data-driven ghosting using deep imitation learning | |
| US20230049158A1 (en) | Crop scouting information systems and resource management | |
| CN111507967B (en) | Mango high-precision detection method in natural orchard scene | |
| CN106600444A (en) | Variety selection method and variety selection device based on neural network algorithm and portfolio theory | |
| CN107980504A (en) | A kind of method of intelligence plantation strawberry | |
| CN107969297A (en) | A kind of method of intelligence plantation strawberry | |
| CN114179104B (en) | Picking robot control method and system based on visual identification | |
| CN108364235A (en) | Method and system for determining fruit pre-picking plan and fruit picking system | |
| Tubau Comas et al. | Automatic apple tree blossom estimation from UAV RGB imagery | |
| James et al. | High‐throughput phenotyping for breeding targets—Current status and future directions of strawberry trait automation | |
| Zacarias et al. | Simulation of an Automated Tahitian lemon grading system based on computer vision | |
| Meena et al. | Detection of Varieties of Diseases in Rice Plants using Deep Learning Techniques | |
| Gunarathna et al. | Experimental determination of CNN hyper-parameters for tomato disease detection using leaf images | |
| CN108174719A (en) | The greenhouse that a kind of strawberry is independently planted | |
| Ahn et al. | An overview of perception methods for horticultural robots: From pollination to harvest | |
| CN107133561A (en) | Event-handling method and device | |
| JP6994212B1 (en) | Artificial intelligence (AI) learning device, fruit picking object estimation device, estimation system, and program | |
| JP6768767B2 (en) | Crop growth judgment system server device, crop growth judgment device, crop growth learning device, crop growth judgment method and program | |
| CN115690778A (en) | Method for detecting, tracking and counting mature fruits based on deep neural network |
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 | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20180501 |