CN117132891A - Corn seedling condition and seedling vigor acquisition method and system - Google Patents
Corn seedling condition and seedling vigor acquisition method and system Download PDFInfo
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Abstract
The invention relates to a method and a system for acquiring the situation and seedling vigor of corn seedlings, comprising the following steps: acquiring image data of a planting area, identifying a marker through a deep learning model to obtain a target area, detecting a corn plant, and acquiring a plurality of phenotype parameters of the corn plant; dividing a target area according to the identifier and the ridge direction of the cell to obtain each row of cell images and the length of a single cell, identifying each row of cell images, and obtaining coordinates of a single plant image; obtaining the width of a single cell according to the coordinates of the single plant and the number of cells, and obtaining the number of seedlings of each cell; calculating to obtain each seedling interval according to the longitudinal coordinate difference of adjacent plants, and determining the width and length of the plants according to the transverse and longitudinal coordinate difference of single plants, thereby obtaining the plant area and the crown diameter; and carrying out significance analysis on the combined data to obtain a significance analysis excel file, a probability density map and a heat map of the cell seedling number property.
Description
Technical Field
The invention relates to the technical field of agricultural information, in particular to a corn seedling situation and seedling situation acquisition method and system based on unmanned aerial vehicle aerial image machine learning.
Background
The corn seedling period is a key period for determining the corn yield, and the phenotype parameters such as the number of seedlings, the seedling area, miao Jianju and the like are known in time, so that various risks in the planting process can be controlled. Traditional seedling condition and seedling vigor monitoring mainly relies on the manual work, but artificial judgement can have subjectivity, and the accuracy of information can not be guaranteed to the data of gathering, can't check the data, and cost of labor is high moreover, inefficiency, observation scope are limited, have very big influence to follow-up analysis.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a corn seedling situation and seedling situation acquisition method and system based on unmanned aerial vehicle aerial image machine learning, which can acquire data in a large area and realize standardized analysis and management of corn seedling stage phenotype parameters.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a corn seedling condition and seedling vigor obtaining method based on unmanned aerial vehicle aerial image machine learning comprises the following steps: acquiring image data of a planting area, identifying a marker through a deep learning model to obtain a target area, detecting a corn plant, and acquiring a plurality of phenotype parameters of the corn plant; dividing a target area according to the identifier and the ridge direction of the cell to obtain each row of cell images and the length of a single cell, identifying each row of cell images, and obtaining coordinates of a single plant image; obtaining the width of a single cell according to the coordinates of the single plant and the number of cells, and obtaining the number of seedlings of each cell; calculating to obtain each seedling interval according to the longitudinal coordinate difference of adjacent plants, and determining the width and length of the plants according to the transverse and longitudinal coordinate difference of single plants, thereby obtaining the plant area and the crown diameter; and merging the seedling condition and seedling potential data table obtained according to the number of seedlings, the plant area and the crown width diameter with the planting planning table according to the row number and the column number, and performing significance analysis on the merged data to obtain a probability density map and a heat map of the characteristics of the number of seedlings of the excel file and the district of the significance analysis.
Further, obtaining a target area by deep learning the identification marker and detecting the maize plant, comprising:
dividing the markers and the corn plants into two categories, marking, inputting the two categories into a deep learning model for training, and obtaining a trained model;
and processing the image data of the planting area based on the trained model, and respectively identifying the marker and the corn plant to obtain a target area and plants in the target area.
Further, the single cell length is:
and determining a row of cells to be detected based on the coordinates of the markers, and dividing the cells to be detected from the planting area image through the four coordinates, wherein the longitudinal coordinate difference of the divided areas is the length of a single cell.
Further, according to the coordinates of the individual plants and the number of cells, obtaining the width of the individual cells, and obtaining the number of seedlings of each cell, including:
the horizontal coordinate difference of the marker is the width of the area to be detected, and the cell width is as follows according to the planting specification if the area is not a wide or narrow ridge: 4/5× (area width to be measured/cell number); if the ridge is wide and narrow, the cell width is as follows: (width of region to be measured/number of cells) -30, sequentially obtaining all cells according to the ridge direction;
and judging the number of plants in the same cell according to the cell coordinates and the plant coordinates, and obtaining the number of seedlings.
Further, plant area and crown diameter were obtained, including:
the width of the plant is the horizontal coordinate difference w, the length of the plant is the vertical coordinate difference h, and the plant area is: w is h;
the leaf orientation is not fixed, the long side is selected as the coronal diameter: max (w, h).
Further, the seedling condition and seedling vigor data table includes: emergence rate, average seedling spacing, miao Jianju variance, seedling area ratio, average leaf length, variance of leaf length and sowing density.
Further, obtaining a probability density map and a heat map of the cell seedling number property of the significance analysis includes:
the probability density map drawing function is:
sns.kdeplot(data＝data,shade＝True,vertical＝False,cut＝0,color＝'r',alpha＝0.5,linewidth＝0.5,linestyle＝'-')
wherein, data is a parameter in the read seedling condition and seedling situation data table; the shadow is a shadow; vertical is drawn with y axis; cut is the cut bandwidth toward axis limit; color is the drawing color; alpha is the color saturation of the color; linewidth is the line size; linetype is a line; sns.kdepth () is a probability density map drawing function;
the heat map is plotted as:
sns.heatmap(data,annot＝False,cmap＝'coolwarm')
wherein, data is a parameter in the read seedling condition and seedling situation data table; annot is not writing a data value in each thermodynamic diagram cell; cmap is a mapping from data values to color space; sns. hetmap () is a drawing function of a heat map.
A corn seedling condition and seedling vigor acquisition system based on unmanned aerial vehicle aerial image machine learning, comprising: the first processing module is used for acquiring image data of a planting area, identifying a marker through a deep learning model to acquire a target area, detecting a corn plant and acquiring a plurality of phenotype parameters of the corn plant; the second processing module is used for dividing the target area according to the identifier and the ridge direction of the cell to obtain each row of cell images and the length of a single cell, identifying each row of cell images and obtaining the coordinates of the single plant image; the third processing module obtains the width of a single cell according to the coordinates of the single plant and the number of cells and obtains the number of seedlings of each cell; the fourth processing module is used for calculating each seedling interval according to the longitudinal coordinate difference of adjacent plants, determining the width and length of the plants according to the transverse and longitudinal coordinate difference of the single plants, and further obtaining the plant area and the crown diameter; and the saliency analysis module is used for merging the seedling condition and seedling potential data table obtained according to the number of seedlings, the plant area and the crown diameter with the planting planning table according to the row number and the column number, and carrying out saliency analysis on the merged data to obtain a probability density map and a heat map of the saliency analysis excel file and the community seedling number character.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described above.
A computing apparatus, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods described above.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. according to the invention, the unmanned aerial vehicle is carried with the RGB camera to acquire the field seedling stage image, so that a large amount of effective image data can be acquired in a short time, the data can be stored for a long time, and the influence caused by the loss of analysis results is avoided.
2. According to the invention, the image is analyzed through the artificial intelligence analysis software, so that multiple phenotype parameters of the seedling condition and seedling vigor can be obtained in a short time, and the time is saved.
3. The invention reduces the requirements on the professional knowledge and experience of the staff and ensures the operation flow.
4. The invention realizes standardized management of seedling conditions and seedling vigor, and avoids the differentiated influence caused by subjective judgment of manpower.
5. The invention can analyze the seedling conditions and seedling vigor of a plurality of places and has higher flexibility.
6. The invention can analyze a plurality of tasks simultaneously and has high efficiency.
Drawings
FIG. 1 is a schematic diagram of a maize seedling vigor system acquisition and analysis in an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and examples.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The invention provides a corn seedling situation and seedling situation acquisition method and system based on unmanned aerial vehicle aerial image machine learning. Dividing according to the ridge direction of the cells to obtain images of each row of cells and the length of each single cell; identifying each row of cell images to obtain coordinates of a single plant image; obtaining the width of a single cell according to the plant coordinates and the number of cells, and counting the number of seedlings coming out of each cell; each seedling interval can be calculated according to the longitudinal coordinate difference of the adjacent plants; the width and length of the single plant can be determined by the difference of the horizontal and vertical coordinates, so that the plant area is obtained, and the crown diameter is obtained; combining the planting planning table with the analyzed data according to the row numbers and the column numbers, and performing significance analysis on the planting planning table to obtain a probability density map and a heat map of characters such as excel files, cell seedling numbers and the like of the significance analysis. The invention can obtain a plurality of phenotype parameters of corn, analyze and judge the situation and seedling vigor of corn seedlings, can be used in a plurality of places, and has high flux, high aging and low cost.
In one embodiment of the invention, a corn seedling condition and seedling condition acquisition method based on unmanned aerial vehicle aerial image machine learning is provided. In this embodiment, as shown in fig. 1, the method includes the following steps:
1) Acquiring image data of a planting area, identifying a marker through a deep learning model to obtain a target area, detecting a corn plant, and acquiring a plurality of phenotype parameters of the corn plant;
2) Dividing a target area according to the identifier and the ridge direction of the cell to obtain each row of cell images and the length of a single cell, identifying each row of cell images, and obtaining coordinates of a single plant image;
3) Obtaining the width of a single cell according to the coordinates of the single plant and the number of cells, and obtaining the number of seedlings of each cell;
4) Calculating to obtain each seedling interval according to the longitudinal coordinate difference of adjacent plants, and determining the width and length of the plants according to the transverse and longitudinal coordinate difference of single plants, thereby obtaining the plant area and the crown diameter;
5) And merging the seedling condition and seedling potential data table obtained according to the number of seedlings, the plant area and the crown width diameter with the planting planning table according to the row number and the column number, and performing significance analysis on the merged data to obtain a probability density map and a heat map of the characteristics of the number of seedlings of the excel file and the district of the significance analysis.
In this embodiment, the seedling condition and seedling situation data table obtained according to the number of seedlings, the plant area and the crown diameter includes: emergence rate, average seedling spacing, miao Jianju variance, seedling area ratio, average leaf length, variance of leaf length, seeding density, etc.
In the step 1), the image data of the planting area is obtained, specifically: and acquiring an image of the planting area through unmanned aerial vehicle aerial photography.
In the embodiment, an air route is regulated, unmanned plane parameters such as resolution, overlapping degree, shooting interval time and the like are set, shooting is carried out in a corn seedling stage, and the air speed of an aerial shooting environment is smaller than 4-level wind. And transmitting the data through the SD card, and splicing the images shot by the unmanned aerial vehicle to obtain the complete aerial image of the area.
In the step 1), a target area is obtained through deep learning identification markers, and corn plants are detected, and the method comprises the following steps:
1.1 Dividing the markers and the corn plants into two categories, marking, inputting the two categories into a deep learning model for training, and obtaining a trained model;
1.2 And (3) processing the image data of the planting area based on the trained model, and respectively identifying the marker and the corn plant to obtain a target area and plants in the target area.
In the step 2), the length of the single cell is: and determining a row of cells to be detected based on the coordinates of the markers, and dividing the cells to be detected from the planting area image through the four coordinates, wherein the longitudinal coordinate difference of the divided areas is the length of a single cell.
In the step 3), the width of a single cell is obtained according to the coordinates of the single plant and the number of cells, and the number of seedlings of each cell is obtained, comprising the following steps:
3.1 The horizontal coordinate difference of the marker is the width of the area to be detected, and the cell width is as follows according to the planting specification if the area is not a wide or narrow ridge: 4/5× (area width to be measured/cell number); if the ridge is wide and narrow, the cell width is as follows: (width of region to be measured/number of cells) -30, sequentially obtaining all cells according to the ridge direction;
3.2 Judging the number of plants in the same cell according to the cell coordinates and the plant coordinates, and obtaining the number of seedlings.
In the step 4), all plants in the cell are ordered according to coordinates, and the difference of the longitudinal coordinates of the central points of adjacent plants is the seedling spacing.
In the step 4), plant area and crown diameter are obtained, specifically:
4.1 The width of the plant is the horizontal coordinate difference w, the length of the plant is the vertical coordinate difference h, and the plant area is: w is h;
4.2 The leaf orientation is not fixed, the long side is chosen as the coronal diameter: max (w, h).
In the step 5), the planting planning table and the analyzed data are combined according to the row numbers and the column numbers, and a plurality of parameters such as the number of the cell seedlings in the seedling situation and seedling situation data table are respectively read for performing the significance analysis t-test.
In the step 5), a probability density map and a heat map of the cell seedling number property of the significance analysis are obtained, including:
5.1 Probability density map rendering functions are:
sns.kdeplot(data＝data,shade＝True,vertical＝False,cut＝0,color＝'r',alpha＝0.5,linewidth＝0.5,linestyle＝'-')
wherein, data is a parameter in the read seedling condition and seedling situation data table; the shadow is a shadow; vertical is drawn with y axis; cut is the cut bandwidth toward axis limit; color is the drawing color; alpha is the color saturation of the color; linewidth is the line size; linetype is a line; sns.kmap () is a probability density map drawing function.
5.2 A heat map is plotted as:
sns.heatmap(data,annot＝False,cmap＝'coolwarm')
wherein, data is a parameter in the read seedling condition and seedling situation data table; annot is not writing a data value in each thermodynamic diagram cell; cmap is a mapping from data values to color space; sns. hetmap () is a drawing function of a heat map.
In conclusion, when the unmanned aerial vehicle is used, the unmanned aerial vehicle plans a route, adjusts various parameters, shoots corn seedlings, and splices shot images; placing the spliced images at a designated position, starting a seedling condition and seedling situation analysis system, and generating an excel file from the results containing parameters such as the number of seedlings, the spacing of the seedlings and the like, so that the excel file is convenient to view; combining the planting planning table with the analyzed data according to the row numbers and the column numbers, and performing significance analysis on the planting planning table to obtain a probability density map and a heat map of characters such as excel files, cell seedling numbers and the like of the significance analysis.
In one embodiment of the invention, a corn seedling vigor acquisition system based on unmanned aerial vehicle aerial image machine learning is provided, comprising:
the first processing module is used for acquiring image data of a planting area, identifying a marker through a deep learning model to acquire a target area, detecting a corn plant and acquiring a plurality of phenotype parameters of the corn plant;
the second processing module is used for dividing the target area according to the identifier and the ridge direction of the cell to obtain each row of cell images and the length of a single cell, identifying each row of cell images and obtaining the coordinates of the single plant image;
the third processing module obtains the width of a single cell according to the coordinates of the single plant and the number of cells and obtains the number of seedlings of each cell;
the fourth processing module is used for calculating each seedling interval according to the longitudinal coordinate difference of adjacent plants, determining the width and length of the plants according to the transverse and longitudinal coordinate difference of the single plants, and further obtaining the plant area and the crown diameter;
and the saliency analysis module is used for merging the seedling condition and seedling potential data table obtained according to the number of seedlings, the plant area and the crown diameter with the planting planning table according to the row number and the column number, and carrying out saliency analysis on the merged data to obtain a probability density map and a heat map of the saliency analysis excel file and the community seedling number character.
In the above embodiment, in the first processing module, obtaining the target area by deep learning the identification identifier, and detecting the corn plant includes:
dividing the markers and the corn plants into two categories, marking, inputting the two categories into a deep learning model for training, and obtaining a trained model;
and processing the image data of the planting area based on the trained model, and respectively identifying the marker and the corn plant to obtain a target area and plants in the target area.
In the above embodiment, the single cell length is: and determining a row of cells to be detected based on the coordinates of the markers, and dividing the cells to be detected from the planting area image through the four coordinates, wherein the longitudinal coordinate difference of the divided areas is the length of a single cell.
In the above embodiment, obtaining the width of a single cell according to the coordinates of the single plant and the number of cells, and obtaining the number of seedlings of each cell includes:
the horizontal coordinate difference of the marker is the width of the area to be detected, and the cell width is as follows according to the planting specification if the area is not a wide or narrow ridge: 4/5× (area width to be measured/cell number); if the ridge is wide and narrow, the cell width is as follows: (width of region to be measured/number of cells) -30, sequentially obtaining all cells according to the ridge direction;
and judging the number of plants in the same cell according to the cell coordinates and the plant coordinates, and obtaining the number of seedlings.
In the above examples, plant area and crown diameter were obtained, including:
the width of the plant is the horizontal coordinate difference w, the length of the plant is the vertical coordinate difference h, and the plant area is: w is h;
the leaf orientation is not fixed, the long side is selected as the coronal diameter: max (w, h).
In the above embodiment, obtaining a probability density map and a heat map of the cell seedling number property of the significance analysis includes:
the probability density map drawing function is:
sns.kdeplot(data＝data,shade＝True,vertical＝False,cut＝0,color＝'r',alpha＝0.5,linewidth＝0.5,linestyle＝'-')
wherein data is a parameter in the read seedling condition and seedling vigor data table; the shadow is a shadow; vertical is drawn with y axis; cut is the cut bandwidth toward axis limit; color is the drawing color; alpha is the color saturation of the color; linewidth is the line size; linetype is a line; sns.kmap () is a probability density map drawing function.
The heat map is plotted as:
sns.heatmap(data,annot＝False,cmap＝'coolwarm')
wherein, data is a parameter in the read seedling condition and seedling situation data table; annot is not writing a data value in each thermodynamic diagram cell; cmap is a mapping from data values to color space; sns. hetmap () is a drawing function of a heat map.
The system provided in this embodiment is used to execute the above method embodiments, and specific flow and details refer to the above embodiments, which are not described herein.
In one embodiment of the present invention, a computing device structure is provided, which may be a terminal, and may include: a processor (processor), a communication interface (Communications Interface), a memory (memory), a display screen, and an input device. The processor, the communication interface and the memory complete communication with each other through a communication bus. The processor is configured to provide computing and control capabilities. The memory comprises a non-volatile storage medium storing an operating system and a computer program which when executed by the processor implements the method described above; the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a manager network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen, the input device can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computing equipment, and can also be an external keyboard, a touch pad or a mouse and the like. The processor may invoke logic instructions in memory.
Further, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In one embodiment of the present invention, a computer program product is provided, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the method embodiments described above.
In one embodiment of the present invention, a non-transitory computer readable storage medium storing server instructions that cause a computer to perform the methods provided by the above embodiments is provided.
The foregoing embodiment provides a computer readable storage medium, which has similar principles and technical effects to those of the foregoing method embodiment, and will not be described herein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The corn seedling condition and seedling vigor obtaining method is characterized by comprising the following steps:
acquiring image data of a planting area, identifying a marker through a deep learning model to obtain a target area, detecting a corn plant, and acquiring a plurality of phenotype parameters of the corn plant;
dividing a target area according to the identifier and the ridge direction of the cell to obtain each row of cell images and the length of a single cell, identifying each row of cell images, and obtaining coordinates of a single plant image;
obtaining the width of a single cell according to the coordinates of the single plant and the number of cells, and obtaining the number of seedlings of each cell;
calculating to obtain each seedling interval according to the longitudinal coordinate difference of adjacent plants, and determining the width and length of the plants according to the transverse and longitudinal coordinate difference of single plants, thereby obtaining the plant area and the crown diameter;
and merging the seedling condition and seedling potential data table obtained according to the number of seedlings, the plant area and the crown width diameter with the planting planning table according to the row number and the column number, and performing significance analysis on the merged data to obtain a probability density map and a heat map of the characteristics of the number of seedlings of the excel file and the district of the significance analysis.
2. The method for acquiring maize seedling vigor according to claim 1, wherein the target region is obtained by deep learning the identification marker, and the maize plant is detected, comprising:
dividing the markers and the corn plants into two categories, marking, inputting the two categories into a deep learning model for training, and obtaining a trained model;
and processing the image data of the planting area based on the trained model, and respectively identifying the marker and the corn plant to obtain a target area and plants in the target area.
3. The maize seedling vigor acquisition method of claim 1, wherein the individual cell length is:
and determining a row of cells to be detected based on the coordinates of the markers, and dividing the cells to be detected from the planting area image through the four coordinates, wherein the longitudinal coordinate difference of the divided areas is the length of a single cell.
4. The method for obtaining maize seedling vigor according to claim 1, wherein obtaining a single cell width according to the coordinates of individual plants and the number of cells, and obtaining the number of seedlings in each cell comprises:
the horizontal coordinate difference of the marker is the width of the area to be detected, and the cell width is as follows according to the planting specification if the area is not a wide or narrow ridge: 4/5× (area width to be measured/cell number); if the ridge is wide and narrow, the cell width is as follows: (width of region to be measured/number of cells) -30, sequentially obtaining all cells according to the ridge direction;
and judging the number of plants in the same cell according to the cell coordinates and the plant coordinates, and obtaining the number of seedlings.
5. The method for obtaining the vigor of maize seedlings according to claim 1, wherein obtaining plant area and crown diameter comprises:
the width of the plant is the horizontal coordinate difference w, the length of the plant is the vertical coordinate difference h, and the plant area is: w is h;
the leaf orientation is not fixed, the long side is selected as the coronal diameter: max (w, h).
6. The method for acquiring maize seedling vigor as defined in claim 1, wherein the seedling vigor data table comprises: emergence rate, average seedling spacing, miao Jianju variance, seedling area ratio, average leaf length, variance of leaf length and sowing density.
7. The method for acquiring the vigor of corn seedlings according to claim 1, wherein obtaining a probability density map and a heat map of a cell seedling number trait of significance analysis comprises:
the probability density map drawing function is:
sns.kdeplot(data＝data,shade＝True,vertical＝False,cut＝0,color＝'r',alpha＝0.5,linewidth＝0.5,linestyle＝'-')
wherein, data is a parameter in the read seedling condition and seedling situation data table; the shadow is a shadow; vertical is drawn with y axis; cut is the cut bandwidth toward axis limit; color is the drawing color; alpha is the color saturation of the color; linewidth is the line size; linetype is a line; sns.kdepth () is a probability density map drawing function;
the heat map is plotted as:
sns.heatmap(data,annot＝False,cmap＝'coolwarm')
wherein, data is a parameter in the read seedling condition and seedling situation data table; annot is not writing a data value in each thermodynamic diagram cell; cmap is a mapping from data values to color space; sns. hetmap () is a drawing function of a heat map.
8. A maize seedling vigor acquisition system, comprising:
the first processing module is used for acquiring image data of a planting area, identifying a marker through a deep learning model to acquire a target area, detecting a corn plant and acquiring a plurality of phenotype parameters of the corn plant;
the second processing module is used for dividing the target area according to the identifier and the ridge direction of the cell to obtain each row of cell images and the length of a single cell, identifying each row of cell images and obtaining the coordinates of the single plant image;
the third processing module obtains the width of a single cell according to the coordinates of the single plant and the number of cells and obtains the number of seedlings of each cell;
the fourth processing module is used for calculating each seedling interval according to the longitudinal coordinate difference of adjacent plants, determining the width and length of the plants according to the transverse and longitudinal coordinate difference of the single plants, and further obtaining the plant area and the crown diameter;
and the saliency analysis module is used for merging the seedling condition and seedling potential data table obtained according to the number of seedlings, the plant area and the crown diameter with the planting planning table according to the row number and the column number, and carrying out saliency analysis on the merged data to obtain a probability density map and a heat map of the saliency analysis excel file and the community seedling number character.
9. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117911294A (en) * | 2024-03-18 | 2024-04-19 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117911294A (en) * | 2024-03-18 | 2024-04-19 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
| CN117911294B (en) * | 2024-03-18 | 2024-05-31 | 浙江托普云农科技股份有限公司 | Corn ear surface image correction method, system and device based on vision |
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