KR20170056731A - System for diagnosing growth state by image data to unit crop organ - Google Patents
System for diagnosing growth state by image data to unit crop organ Download PDFInfo
- Publication number
- KR20170056731A KR20170056731A KR1020150159358A KR20150159358A KR20170056731A KR 20170056731 A KR20170056731 A KR 20170056731A KR 1020150159358 A KR1020150159358 A KR 1020150159358A KR 20150159358 A KR20150159358 A KR 20150159358A KR 20170056731 A KR20170056731 A KR 20170056731A
- Authority
- KR
- South Korea
- Prior art keywords
- growth
- range
- crop
- data
- leaf
- Prior art date
Links
- 230000012010 growth Effects 0.000 title claims abstract description 187
- 210000000056 organ Anatomy 0.000 title description 21
- 235000016709 nutrition Nutrition 0.000 claims abstract description 30
- 230000001850 reproductive effect Effects 0.000 claims abstract description 19
- 235000015097 nutrients Nutrition 0.000 claims abstract description 18
- 238000004458 analytical method Methods 0.000 claims abstract description 17
- 230000035764 nutrition Effects 0.000 claims abstract description 15
- 238000003745 diagnosis Methods 0.000 claims abstract description 11
- 238000000034 method Methods 0.000 claims description 29
- 238000003306 harvesting Methods 0.000 claims description 25
- 238000004891 communication Methods 0.000 claims description 13
- 238000013500 data storage Methods 0.000 claims description 13
- 244000038559 crop plants Species 0.000 claims description 9
- 230000003601 intercostal effect Effects 0.000 claims description 4
- 210000004185 liver Anatomy 0.000 claims description 2
- 230000008929 regeneration Effects 0.000 claims description 2
- 238000011069 regeneration method Methods 0.000 claims description 2
- 230000008635 plant growth Effects 0.000 claims 2
- 238000010191 image analysis Methods 0.000 abstract description 3
- 238000009313 farming Methods 0.000 abstract description 2
- 241000196324 Embryophyta Species 0.000 description 24
- 210000003462 vein Anatomy 0.000 description 10
- 238000010586 diagram Methods 0.000 description 7
- 235000010469 Glycine max Nutrition 0.000 description 6
- 244000068988 Glycine max Species 0.000 description 6
- 230000001066 destructive effect Effects 0.000 description 5
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 230000007613 environmental effect Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 235000013555 soy sauce Nutrition 0.000 description 4
- 238000012790 confirmation Methods 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 238000013480 data collection Methods 0.000 description 3
- 230000035784 germination Effects 0.000 description 3
- 230000008520 organization Effects 0.000 description 3
- 230000000737 periodic effect Effects 0.000 description 3
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 241000227653 Lycopersicon Species 0.000 description 2
- 235000007688 Lycopersicon esculentum Nutrition 0.000 description 2
- 241000607479 Yersinia pestis Species 0.000 description 2
- 230000001154 acute effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 239000001569 carbon dioxide Substances 0.000 description 2
- 230000010339 dilation Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 235000013312 flour Nutrition 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000029553 photosynthesis Effects 0.000 description 2
- 238000010672 photosynthesis Methods 0.000 description 2
- 235000014347 soups Nutrition 0.000 description 2
- 208000005156 Dehydration Diseases 0.000 description 1
- 230000003698 anagen phase Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 238000010899 nucleation Methods 0.000 description 1
- 230000000384 rearing effect Effects 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 210000004994 reproductive system Anatomy 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000007226 seed germination Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
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
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0098—Plants or trees
-
- H04N5/232—
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Chemical & Material Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Botany (AREA)
- Biochemistry (AREA)
- Medicinal Chemistry (AREA)
- Food Science & Technology (AREA)
- Immunology (AREA)
- Wood Science & Technology (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Marine Sciences & Fisheries (AREA)
- Mining & Mineral Resources (AREA)
- Analytical Chemistry (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to an image analysis module for extracting an image object for each crop structure from the transmitted crop image and calculating leaf length and leaf width data based on the extracted image object for each crop structure, , A nutrient growth range, a reproductive growth range, a stress range, and a normal growth range are set and registered based on difference values between the leaf width data and the current last previous leaf length and leaf width data, A growth analysis module for analyzing a range of difference values between the leaf area and leaf width data of the crop and the leaf area and the leaf width data before the end of the current data and storing the growth state of the difference value range corresponding to the analyzed result as the growth state of the current crop, Nutrition based on the interfold length of the crop A growth range in which a length range of a length range corresponding to the analyzed result is stored as a growth status of a current crop, And a control unit for controlling each of the units and providing result information of the growth analysis module and the growth analysis module. The present invention relates to a growth diagnosis / analysis system based on image data of each plant organism, Using the actual data of the grower, the status of the crop growth is diagnosed and the influence of the stress is analyzed to stabilize and improve the growth and productivity of the crop. The measured data is stored periodically and the data is graphically analyzed To identify changes in growth characteristics and to enable scientific farming.
Description
The present invention relates to a growth diagnosis / analysis system based on image data per crop organ.
The way to investigate the characteristics of each crop organism to investigate the status of the crop growth is the periodical in the experiential, non-periodic, destructive, non-scientific, and conventional methods of surveying and visual confirmation by growers. By monitoring the cultivation condition of the crops by sensing, the productivity is stabilized and improved. To prevent pests.
Particularly, the destructive survey method causes a problem in the continuity of productivity, and an error occurs in the investigation of the growth from the subject's subjective viewpoint. In addition, the data from the investigator's records may cause problems in ongoing management and are a difficulty in the analysis of scientific growth characteristics
Then, the cultivator judges crop growth characteristics, nutritional growth or reproduction growth by utilizing image data obtained through visual observation or visual observation, image data per crop organ collected by various online methods, and image data collected through a camera or the like , And the method of analyzing the change of stressful growth and the change of the growth amount should be algorithmically stored, recorded, and utilized as scientific data.
For reference, it is necessary to manage the above-ground environment and rhizosphere environment in order to grow the crops. There are differences in the growth characteristics of each plant depending on the environment, and also the productivity is affected. In the present technology, crop growth is managed only considering the environmental impact of the crops, and the growth characteristics of the crops are investigated and analyzed through non-periodic surveys or surveys on the occurrence of pests.
In addition, research methods related to the growth of crops have been used in a non-destructive manner, either by actual measurements using growers or researchers, or by using measurement and measurement devices in destructive ways.
The present invention has been developed in order to solve the above problems. In order to investigate and analyze the growth characteristics of crops, the present invention provides a method of estimating the growth data of the crops using the images of the crops collected through various methods, This study analyzes the growth status of each organ by analyzing the changes of the growth characteristics and growth conditions according to the influence of the environment on the basis of them, and solves the need to manage the crops by statistical data management And to provide a system for diagnosis / analysis of growth based on image data of each plant, which can be utilized as a standard for measuring the growth state of each plant by utilizing non-destructive growth investigation and image-based scientific image.
According to an aspect of the present invention, there is provided a growth diagnosis /
A data storage unit for storing the crop growth information; and a control unit for controlling each of the units, wherein the control unit is configured to control the vegetation growth, the reproduction growth, the normal growth range And determines the growth state of the inter-leaf span length range corresponding to the analyzed result as the growth state of the current target crop to the data storage unit .
Preferably, the nutritional growth range is subdivided into a river nutrition and a nutrient growth based on the length of the harvesting space of the plant, and the reproductive growth range is set based on the length of the growing season, And the growth state of the crop is divided into the river growth, the nutritional growth, the river growth, the mature growth, and the normal growth, and the growth state of the crop is analyzed.
The range of the growth of the crop is as follows: the range of growth of the river nutrient when the length of the growing space is 31 cm or more, the range of the nutrient growth when the length is 26 cm to 30 cm, the range of the river germination when the width is 10 cm or less, , And a range of normal growth conditions in the range of 16 cm to 25 cm.
Preferably, the control unit sets and registers the nutritional growth range, the reproductive growth range, the stress range, and the normal growth range based on the difference between the current leaf position and leaf width data of the crop and the current last previous leaf spot and leaf width data, The current leaf position and leaf width data of the target crop from the crop image is calculated and the range of the difference between the current leaf position and leaf width data of the calculated target crop and the leaf spot and leaf width data before the end of the current data is analyzed, And the growth state of the range of leaf width and leaf width difference value is stored in the data storage unit as the growth state of the present crop.
The control unit controls nutrient growth, reproductive growth, stress, and the like based on the change in the growth rate between the present foliage soy sauce, gyeongyang, fowl number, harvest and number data, , A normal state range is set and registered, and the current soybean soup diameter, germination number, fugitive number, harvest number and number of the target crop are calculated on the basis of the crop image, and the current soybean soup, , The harvesting and number data, and the range of the growth change between the harvesting and number data before the end of the current data, and the growth variation of the range of the growth change corresponding to the result of the analysis, And the change amount is stored.
The present invention relates to a growth diagnosis / analysis system based on image data of a plant organs. The system diagnoses the status of crop growth and analyzes the influence of stress using various image data of each plant or plant and actual data of growers, And the measured data is stored periodically and the graphical analysis is performed to confirm the change in the growth characteristics, thereby enabling scientific farming.
1 is a view showing a configuration of a growth diagnosis / analysis system based on image data per crop plant according to the present invention;
2 is a diagram illustrating a data gathering method according to the present invention in order;
FIG. 3 is a diagram showing a method of analyzing data per crop plant according to the present invention.
4 is a view illustrating a method of diagnosing a growth condition of a crop according to the present invention.
5 is a view showing a method for analyzing a change in crop growth amount according to the present invention
FIG. 6 is a graph showing the length (DMFC) from the growth point of the tomatoes grown under the medium water stress conditions according to the present invention to the flower buds blooming
Fig. 7 is a graph showing crop productivity in accordance with the present invention; Fig.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a view showing a configuration of a growth diagnosis / analysis system based on image data per crop plant according to the present invention; FIG.
1, the system according to the present invention includes a wired /
The wired / wireless communication unit 101 (or the "wired / wireless communication unit") receives the image of the target crop. The wired /
The
The
The user interface (UI)
The ground / root zone cultivation
2 is a diagram showing a data gathering method according to the present invention in order.
As shown in FIG. 2, the data collection method according to the present invention, that is, the method of collecting the crop information includes the image of the crop, the actual data, and the method of collection includes the wired / wireless communication method through the network, And the collected image is collected through the input / output device. The extracted image is extracted for each image of the collected image, converted into numerical data, and stored in the data storage unit. Further, the information input by using the input / output unit of the actual data of the crop grower is stored in the data storage unit for each plant organ.
At this time, the angle of 1, 2, and 3 leaves in the leaves from the growth point to the 7th leaf is measured from the designated time after sunrise to the designated time before sunset, and the wilt of the leaves and the leaf color and leaf temperature of the leaves are measured, (Reference point) with the data (image) of the designated time and the angle of 90 ° or more of the stem (reference point), and the amount of change in the leaf color of the whole leaf which can be identified among the leaves secured in the data (image) The data is stored when the difference of the leaf angle of the measured value is greater than -15 ° C, the difference of the leaf color occurs, and the value of the leaf temperature is not different by the time. The moisture stress preliminary alarm and the moisture stress damage alarm are displayed through the input / output unit.
The specific data collection method is as follows.
First, the cropped image of the transferred data (S201) is sensed (S202), and image objects of each plant organ (leaf, stem, growth point, fruits, And stores the data for each plant organ (S203 to S205).
For example, it is assumed that the current background of a leaf is monochrome. First, a leaf area is searched using color clustering in the HSL color space.
Then apply the morphology dilation several times to remove the nodal component and leave only the leaflets.
Then, the lobes are counted by the connection component extraction method, and the width, height, area, and number of leaflets are extracted by the eigenvalue calculation method for the leaf area.
The shape and characteristics of leaves are as follows.
- Feather-like leaflets attached to petiole from stem. Petiole is in the stem and within + - 30 degrees from 90 degrees. - Leaves of lobules are serrated acute and the ends of lobules are pointed. - The lobules have the main veins and side veins, and the side veins are deeply embedded in the main veins. - Leaves have slightly white and gray green veins.
In the case where the crop image data is not collected, that is, when the crop image can not be collected, the actual data for each crop plant by the grower is inputted (S206) and the data is stored as data for each crop plant to be grown (S205) (S207). In the case where no actual data for each crop plant is input, the growth environment data is stored (S207).
FIG. 3 is a diagram illustrating a data analysis method according to a plant organ according to the present invention.
As shown in FIG. 3, the present invention analyzes a change amount per organ by utilizing a data analysis algorithm for each plant organ, compares it with stored electricity data, and classifies the steady state, nutritional growth, reproductive growth, .
Regarding the image analysis of each plant organ, periodic leaf lengths and leaf widths of up to 17 leaves are measured at a designated time, and data on the number of crops are obtained by securing data of about 5 points in the rearing place. And the leaf length and leaf width were estimated. The leaves and leaf width were estimated by inverting the leaf index of leaves. 5 Repeated leaf length and leaf width data The average value is calculated for three or more images. If less than 3 leaf points are used, the leaf length and leaf width are used as the closest point and the leaf width of the lower leaf is averaged.
The leaf area is calculated by multiplying leaf length and leaf width by leaves of up to 17 leaves per individual. At this time, the number of times of planting is multiplied to convert into leaf area per area, and the measured leaf length, leaf width, and leaf area are stored.
At present, the difference is compared with the measured value in the analysis and the difference value is stored. When the value of leaf / leaf width / leaf area is +, 0, - value, it is used as data of nutrition, reproductive growth diagnosis, stress diagnosis, photosynthesis / growth / quantity / quality prediction data.
Table 1 shows the criterion for the rate of change of leaf length and leaf width for early diagnosis using measurement data of each plant organ.
The method of data analysis according to specific plant organ is as follows.
First, the nutrient growth range, the reproductive growth range, the stress range, and the normal growth range are set and registered based on the difference value between the current foliage length and leaf width data and the last previous foliar length and leaf width data for each plant organ.
Then, the range of the difference between the leaf area and leaf width data of the current target crop and the leaf area and the leaf width data before the end of the current data are analyzed (S01 to S302), and a difference value range corresponding to the analyzed result The growing state is stored as the current growing state of the target crop (S303 to S306).
For example, when the difference between the leaf area and leaf width data of the current target crop and the leaf area and leaf width data of the current target crop per the calculated plant organ belongs to the nutrition growth range of N, . If the difference between the leaf area and the leaf width data of the current crop and the leaf area and the leaf width data before the end of the current data belong to the stress range of K, the current growing state of the target crop is stored as a stress state.
At this time, the user notifies the registered user terminal of the growing state of the crop in the stressed state, and notifies the user that the growing state of the current cropping object is the stressed state.
4 is a diagram illustrating a method of diagnosing a growth condition of a crop according to the present invention.
As shown in FIG. 4, the method for diagnosing a growth condition of a crop according to the present invention analyzes the length of a storage space of a crop, and analyzes the data according to the steady state, nutritional growth, strong nutritional growth, reproductive growth, And stores it in the storage unit.
Specifically, it is as follows.
First, the method for diagnosing a growth condition of a crop according to the present invention sets and registers a nutritional growth, a reproductive growth, and a normal growth range based on a span length of a crop sprout, and analyzes the range of the span between the current sprouts of the target crop (S401- S402), and stores the growth state of the length range corresponding to the analyzed result as the current state of the current crop (S403 to S405).
In a more specific embodiment, the method for diagnosing the growth of a crop according to the present invention is characterized in that the nutritional growth range is subdivided into a river nutrition and a nutritional nutrition based on the length of the intercostal space, It is subdivided into regenerative growth and medicinal growth based on the interspecific length of the interspecies, and the regeneration status of the crop is subdivided into river nutrition, medicinal nutrition, river reproductive, Analyze the growth of the crop.
At this time, the range of growth of the crop according to the present invention is in the range of river nutrition growth range of 26 cm to 30 cm, the nutritional growth range of 10 cm or less, the range of 11 to 15 cm And the range of normal growth condition is 16cm ~ 25cm.
5 is a diagram illustrating a method for analyzing a change in crop growth rate according to the present invention.
As shown in FIG. 5, the method for analyzing the change in the crop growth rate according to the present invention analyzes the data of the crop root length, the diameter, the foliage number, the number of crops, and the number of harvests and numbers to analyze the nutrient growth, reproductive growth, And stores it in the data storage unit.
Specifically, it is as follows.
First, the method for analyzing the change in the crop growth rate according to the present invention is based on the changes in the growth rate between the current Hanban soy sauce, light soybean flour, fruiting number, harvesting number data and the last previous soybean soy sauce, light soybean flour, Growth, reproductive growth, stress, and steady-state range are set and registered.
Then, the range of growth variation between the foliage length, the diameter, the number of foliage, the number of foliage, the number of harvests and the number of foliage of the current crop by the cropping unit, and the foliage and leaf width data before the end of the current data are analyzed (S501 to S502) , And the amount of change in the amount of change corresponding to the analyzed result is stored as the amount of change in the current target crop (S503 to S506).
For example, if the change in growth between the current harvesting date, germination, fugitive number, harvest and number data of the current target crop according to the calculated plant organ, m, the amount of change in the growth of the current crop is stored as the amount of nutrient growth change. Then, the change amount of growth between the current harvesting plant length, gyeonggyeong, fowling number, harvesting and number data of the current target crop according to the above-mentioned plant organ, and the harvest and number data of the last previous fowl liver, In the case of belonging to the stress variation range, the change amount of the current crop is stored as a stress state.
At this time, the amount of change in the growth of the crop in the stressed state is notified to the registered user terminal, and the user is informed that the change amount of the current crop is the stressed state.
FIG. 6 is a view showing the length (DMFC) from the growth point of tomatoes grown under medium moisture stress conditions according to the present invention to the flower buds under flowering, and FIG. 7 is a diagram showing crop productivity according to the present invention.
As shown in FIGS. 6 and 7, the present invention collects five pieces of data (images or actual values) in a crop growing area periodically, extracts a cropping number in an image in the case of a cropping image, Calculate the distance to the first flower room in flowering, and calculate and store the average of the measured distance values for 5 repetitions. (For reference, the horizontal axis represents water content in growing media (%).
Periodically, the average value data is stored by measuring the five kinds of crop plants in the plant from the bottom to the top of the plant, the diameter, the number of trees, the number of trees, the number of harvests and the number of harvests.
In addition, the growth, growth and productivity of crops are represented by the S curve, and the growth of crops is rooted by seed germination, followed by gradually increasing the size and weight of photosynthesis and nitrogen assimilation. And there is a difference in productivity depending on the difference in growth.
Description of the Related Art [0002]
101: wired / wireless communication unit 102: data storage unit
103: control unit 104: ground / rhizosphere cultivation environment acquisition module
105: User Interface (UI) module
106: Input / output unit
Claims (5)
A data storage unit for storing the crop growth information; And
A control unit for controlling each of the units;
Lt; / RTI >
The control unit sets and registers the nutrient growth, reproductive growth, and normal growth range on the basis of the span of the intercostal space between crops, analyzes the span length range between the foliage from the transferred target crop image, Wherein the growth state of the liver span length range is stored in the data storage unit as the current state of the target crop.
The control unit
The nutrient growth range is subdivided into a river nutrition and a nutrient growth based on the length of the intercostal space of the crop, and the reproductive growth range is defined as a river reproductive growth and a medicinal growth And the plant growth state is subdivided into a plant growth state, a nutrient growth regime, a germplasm growth regime, a germplasm growth regime, and a normal growth regeneration state, and the growth state of the crop is analyzed. Diagnosis / Analysis System by.
The growth state range of the crop is
The range of growth of the river nutrient is 31cm or more, the growth range of the nutrient is 26cm ~ 30cm, the growth range of the river is 10cm or less, the range of the growth is about 11cm ~ 15cm, and the growth is 16cm ~ 25cm. Wherein said image data includes a plurality of image data of a plurality of crop plants.
The control unit
A nutrient growth range, a reproductive growth range, a stress range, and a normal growth range are set and registered based on the difference value between the current leaf area and leaf width data of the crop and the current last previous leaf area and leaf width data, And the difference value between the current leaf position and leaf width data of the target crop and the last leaf position and leaf width data before the end of the current data are analyzed to calculate the leaf spot and leaf width difference value corresponding to the analyzed result Wherein the growth state of the range is stored in the data storage section as the growth state of the current target crop.
The control unit
Based on the changes in the growth rate between the present vegetation and the date of harvest, number of shoots, number of shoots, number of harvests, number of harvests, number of shoots, number of shoots, number of harvests, number of shoots, And the number and the number of harvests and the number of harvests of the target crop are calculated based on the crop image, and the current harvesting date, And a range of growth change between harvesting and number data before the end of the current data, and storing the growth change in the range of the growth change amount corresponding to the analyzed result as the growth change amount of the current target crop Growth diagnosis / analysis system based on image data of each plant organism.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020150159358A KR101763841B1 (en) | 2015-11-13 | 2015-11-13 | System for diagnosing growth state by image data to unit crop organ |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020150159358A KR101763841B1 (en) | 2015-11-13 | 2015-11-13 | System for diagnosing growth state by image data to unit crop organ |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| KR20170056731A true KR20170056731A (en) | 2017-05-24 |
| KR101763841B1 KR101763841B1 (en) | 2017-08-16 |
Family
ID=59051681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020150159358A KR101763841B1 (en) | 2015-11-13 | 2015-11-13 | System for diagnosing growth state by image data to unit crop organ |
Country Status (1)
| Country | Link |
|---|---|
| KR (1) | KR101763841B1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345039A (en) * | 2018-11-13 | 2019-02-15 | 中国水利水电科学研究院 | A kind of crop production forecast method comprehensively considering water and saline stress |
| KR20190069648A (en) * | 2017-12-05 | 2019-06-20 | 농업회사법인 원스베리 주식회사 | Method for measuring growth amount by image analyzing ginseng |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102204832B1 (en) | 2018-05-30 | 2021-01-19 | 주식회사 공간정보 | Construction System for Growth Information of Farm Crops |
| KR20190138523A (en) | 2018-06-05 | 2019-12-13 | 한국전자통신연구원 | Apparatus and method for obtaining crop growth data for diagnosis of pests in a greenhouse |
| KR102706281B1 (en) | 2021-09-01 | 2024-09-19 | 아이티컨버젼스 주식회사 | Crop Growth Measurement System Through Cloud Based Video Image |
| KR102598012B1 (en) | 2022-10-17 | 2023-11-06 | 순천대학교 산학협력단 | A System for Determining the Growth Stage of Fruit Tree Using a Deep Learning-Based Object Detection Model |
| KR20240093374A (en) | 2022-12-15 | 2024-06-24 | 강원대학교산학협력단 | Discrimination method and discrimination system for shape factor of tubers for establishing optimal conditions in the maturation process of tubers |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5842444B2 (en) * | 2011-08-02 | 2016-01-13 | 株式会社ニコン | Sales processing system, sales processing method and program |
-
2015
- 2015-11-13 KR KR1020150159358A patent/KR101763841B1/en active IP Right Grant
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190069648A (en) * | 2017-12-05 | 2019-06-20 | 농업회사법인 원스베리 주식회사 | Method for measuring growth amount by image analyzing ginseng |
| CN109345039A (en) * | 2018-11-13 | 2019-02-15 | 中国水利水电科学研究院 | A kind of crop production forecast method comprehensively considering water and saline stress |
| CN109345039B (en) * | 2018-11-13 | 2021-02-23 | 中国水利水电科学研究院 | Crop yield prediction method comprehensively considering water and salt stress |
Also Published As
| Publication number | Publication date |
|---|---|
| KR101763841B1 (en) | 2017-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101763841B1 (en) | System for diagnosing growth state by image data to unit crop organ | |
| Padilla-Díaz et al. | Scheduling regulated deficit irrigation in a hedgerow olive orchard from leaf turgor pressure related measurements | |
| Giese et al. | Complete vineyard floor cover crops favorably limit grapevine vegetative growth | |
| CN105045321A (en) | Internet-of-things application design-based cloud platform integrated management method | |
| KR20170056728A (en) | System for measuring growth amount and plant length using lindenmayer system and image and beam criterion | |
| JP7137426B2 (en) | Harvest prediction system for greenhouse-grown fruits | |
| JP2016154510A (en) | Information processor, growth state determination method, and program | |
| Adams et al. | The impact of changing light levels and fruit load on the pattern of tomato yields | |
| CN110119901A (en) | A kind of planting asparagus comprehensive management platform and its management method | |
| Campbell et al. | Developing a castor (Ricinus communis L.) production system in Florida, US: evaluating crop phenology and response to management | |
| KR20220082952A (en) | Farming automation system using crop image big data | |
| KR101810901B1 (en) | Apparatus for simulation for growth state crop organ | |
| Poni et al. | Laser scanning estimation of relative light interception by canopy components in different grapevine training systems | |
| Avigal et al. | Learning seed placements and automation policies for polyculture farming with companion plants | |
| CN109325630B (en) | Morphological parameter-based rice yield prediction method under high-temperature stress | |
| KR20200056520A (en) | Method for diagnosing growth and predicting productivity of tomato empolying cloud | |
| JP2022136058A (en) | Method of generating prediction model for predicting crop production performance, generation apparatus, and generation program | |
| CN112837267A (en) | Digital detection method and system for predicting drug resistance of transgenic corn | |
| CN104285627A (en) | Flue-cured tobacco wide-narrow row high-low ridge planting method | |
| CN109673414B (en) | Strawberry vine hanging three-dimensional cultivation method | |
| Kaiser et al. | High tunnel strawberries | |
| Fujii et al. | Relation between stem growth processes and internode length patterns in sorghum cultivar ‘Kazetachi’ | |
| JP2019219704A (en) | Farm management support system | |
| KR101993761B1 (en) | Method for tracking crops of agriculture | |
| JP2022136056A (en) | Prediction method for predicting crop production performance, prediction apparatus, and prediction program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A201 | Request for examination | ||
| E902 | Notification of reason for refusal | ||
| GRNT | Written decision to grant |