CN112734083A - Rice harvester path planning control system based on machine vision - Google Patents
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Abstract
The invention relates to the technical field of machine vision, in particular to a rice harvester path planning control system based on machine vision, which comprises a rice field image acquisition module, a rice field image acquisition module and a control module, wherein the rice field image acquisition module is used for acquiring images of all rice fields to be harvested; the paddy field image processing module identifies the maturity of each paddy field rice from the image; the path planning module plans the harvesting sequence of the rice field according to the maturity from high to low; the rice field image acquisition module acquires rice field images of each rice field; a paddy field image processing module extracts a seedling emergence line from a paddy field image; the path planning module plans rice harvesting paths of all rice fields according to the seedling lines; the sampling planning module plans a harvesting sampling point according to the rice field image and the rice harvesting path; the rice image acquisition module acquires a rice image at a harvesting sampling point; and the rice image analysis module processes the rice image and analyzes the rice quality. The invention can plan the harvesting path according to the maturity of rice and the rice field image, and can plan the irregular harvesting path of the rice field, so that the rice is harvested when the rice is mature.
Description
Technical Field
The invention relates to the technical field of machine vision, in particular to a rice harvester path planning control system based on machine vision.
Background
The rice is one of the main grain crops in China, the planting history of the rice in China is very long, the geographical environment of China is various, and a large area of large-area rice fields are formed in the region with flat terrain. The rice field in large scale is favorable to rice harvester, compares in traditional manual work and cuts rice, and rice harvester has saved manpower resources, and harvesting efficiency also promotes by a wide margin.
However, there are problems with machine harvesting of rice:
1. in a large-area rice field, which rice field is harvested firstly needs to be manually judged, and due to different sowing seasons and different rice field environments in the large-area rice field, the growth vigor of rice is different, the rice is harvested too early, the rice may not turn yellow completely, the dry matter content is incomplete, and the rice quality is poor; the rice is harvested too late, the rice enters a withered period, stems are easy to yellow and dry, the stems are easy to break, grains are easy to fall in the field, and the rice yield is influenced;
2. for rice fields with irregular shapes, the rice harvester cannot automatically plan a harvesting route according to the actual planting condition of the rice field.
Disclosure of Invention
The invention aims to provide a rice harvester path planning control system based on machine vision, which can plan a harvesting path according to rice maturity and rice field images.
The basic scheme provided by the invention is as follows: a rice harvester path planning control system based on machine vision comprises a rice field image acquisition module, a rice field image processing module, a path planning module, a sampling planning module, a rice image acquisition module, a rice image analysis module and a data storage module;
the rice field image acquisition module is used for acquiring all the images of the rice field to be harvested;
the rice field image processing module is used for preprocessing the images and identifying the maturity of rice in each rice field to be harvested from the images;
the path planning module is used for planning the harvesting sequence of all rice fields to be harvested according to the maturity from high to low;
the rice field image acquisition module is also used for acquiring rice field images of each rice field to be harvested according to the harvesting sequence;
the paddy field image processing module is also used for preprocessing the paddy field image and extracting a seedling emergence line from the paddy field image;
the path planning module is also used for planning rice harvesting paths of the rice fields to be harvested according to the seedling lines;
the sampling planning module is used for planning harvesting sampling points according to the rice field image and the rice harvesting path;
the rice image acquisition module is used for acquiring rice images at harvesting sampling points during rice harvesting;
the rice image analysis module is used for processing the rice image and analyzing the rice quality;
the data storage module is used for storing image data, maturity data, harvesting sequence data, rice field image data, seedling line data, rice harvesting path data, sampling point data, rice image data and rice quality data.
Compared with the prior art, the scheme has the advantages that: 1. for multiple rice fields, the maturity of rice in the multiple rice fields in a harvesting season is different due to various reasons, such as seeding time, water demand, topography and the like, the rice is preferably harvested in a yellow maturity stage, premature rice is not completely yellowed, the dry matter content is not complete, late rice enters a withered stage, stems are easy to yellow and dry, and are easy to break, grains are easy to fall in the field, the yield of the rice is influenced, images of all the rice fields to be harvested are collected and preprocessed, information such as pixel distribution, brightness, color and the like is converted into digital signals, the maturity of the rice in each rice field to be harvested is calculated according to the digital signals, the harvesting sequence of all the rice fields to be harvested is planned according to the maturity from high to low, and each rice field is harvested in the yellow maturity stage, so that the quality and the yield of the rice are ensured.
2. The method comprises the steps of collecting rice field images of rice fields to be harvested according to a harvesting sequence, extracting emergence lines from the rice field images, planning rice harvesting paths of the rice fields to be harvested according to seedling lines, harvesting the rice fields according to the rice harvesting paths by a rice harvester, planning the rice harvesting paths of the rice fields to be harvested according to the seedling lines for the rice fields with irregular shapes, and ensuring that the rice harvester can harvest all rice in the rice fields.
3. The harvesting sampling points are planned according to the rice field images and the rice harvesting path, the rice images are collected at the harvesting sampling points when the rice is harvested, the rice images are processed and the rice quality is analyzed, the quality of the rice in the rice field can be reasonably reflected by the rice images collected at the sampling points, and a grower can know the quality of the rice harvested at this time, so that the next planting can be reasonably optimized.
Further, the rice field image processing module comprises a rice field image preprocessing submodule and a seedling line extraction submodule;
the rice field image preprocessing submodule is used for carrying out denoising processing on the rice field image by adopting a median filtering method and carrying out graying processing on the denoised rice field image;
and the seedling line extraction submodule is used for extracting the seedling lines of the rice field image subjected to the graying treatment according to the pixel point density of the grains.
Has the advantages that: and denoising the rice field image by adopting a median filtering method, graying the denoised image, and extracting the seedling line according to the pixel point density of the grains, so that the image quality is improved, and the accuracy of seedling line extraction is improved.
Further, the rice image acquisition module comprises a camera which is arranged at the front end of the rice harvester, and the rice image analysis module comprises a rice image processing submodule and a rice quality analysis submodule;
the rice image processing submodule is used for correcting the distortion of the rice image;
the rice quality analysis submodule is used for extracting the characteristics of rice in the rice image and analyzing the rice quality according to the characteristics of the rice.
Has the advantages that: the camera sets up in the harvester front end, and the rice image of gathering is clear, but because the camera overlooks and shoots, leads to the rice of equidimension on different distances to correspond the pixel point number difference on the rice image, can cause the influence to follow-up analysis calculation, consequently adopts rice image processing submodule piece to correct the distortion of rice image, carries out the extraction of the characteristic of rice in the rice image.
Further, the characteristics of the rice include the type of rice, the number of grains, the size of grains, the integrity of grains, the color of rice, the presence or absence of insect damage and mildew on the surface of rice.
Has the advantages that: the characteristics of the paddy rice in various aspects in the paddy rice picture are extracted, so that the quality of the paddy rice is more comprehensively evaluated.
Further, the rice quality is divided into good quality rice, inferior quality rice and poor quality rice, the data storage module stores a rice characteristic comparison table, and the rice characteristic comparison table comprises characteristic data ranges of various types of rice with various qualities and is used for comparing the characteristic data with the extracted characteristic data of the rice in the rice image and analyzing the rice quality.
Has the advantages that: the characteristics of different types of rice are different, so that the quality of various types of rice is reasonably subdivided according to the rice characteristics corresponding to the rice type comparison, users can sell different prices according to different rice qualities, the income is maximized, the reason is found according to the quality of the rice at this time, and the next rice planting is reasonably optimized.
Furthermore, the sampling planning module extracts a rice field profile according to the rice field image, judges the rice field area, plans the number of harvesting sampling points according to the rice field area, and randomly sets the harvesting sampling points in a rice harvesting path.
Has the advantages that: the method comprises the steps of extracting a rice field outline according to a rice field image, judging the area of the rice field, planning the number of harvesting sampling points according to the area of the rice field, randomly setting the harvesting sampling points in a rice harvesting path, and guaranteeing that a rice picture sampled by the sampling points can reasonably and effectively reflect the condition of rice in the whole rice field.
Further, the rice image analysis module also comprises a rice yield estimation submodule;
and the rice yield estimation submodule is used for counting the rice yield in the rice field in unit area at the sampling point according to the rice image and estimating the rice yield in the whole rice field according to the effective planting area of the whole rice field.
Has the advantages that: estimate the rice output in monoblock paddy field, saved the cost of manpower measurement, not only improved efficiency, also can help the user to know the rice output in paddy field, and then know the income condition.
Further, the rice harvester path planning control system also comprises a rice field image setting module;
and the rice field image setting module is used for directly drawing the rice field image and the seedling line by a user and sending the rice field image and the seedling line to the data storage module.
Has the advantages that: the user directly sets up the module through the paddy field image and draws paddy field image and seedling line to send for the data storage module, can not need the paddy field image acquisition, practice thrift and gather the cost.
Drawings
FIG. 1 is a schematic diagram of a rice harvester path planning control system based on machine vision according to one embodiment;
fig. 2 is a schematic diagram of a rice harvester path planning control system based on machine vision according to a second embodiment.
Detailed Description
Example one
An embodiment substantially as shown in figure 1: a rice harvester path planning control system based on machine vision comprises a rice field image acquisition module, a rice field image processing module, a path planning module, a sampling planning module, a rice image acquisition module, a rice image analysis module and a data storage module.
The rice field image acquisition module is used for acquiring all large-area sliced images of the rice field to be harvested through the unmanned aerial vehicle and sending the images to the data storage module.
The rice field image processing module is used for preprocessing the image, namely denoising the image by adopting a median filtering method, carrying out graying processing, and identifying the maturity of each piece of rice in the rice field to be harvested from the preprocessed image; specifically, a machine vision technology is adopted to convert information such as distribution, brightness, color and the like of pixel points in an image into digital signals, and an image processing module calculates the maturity of rice in each rice field to be harvested according to the digital signals.
And the path planning module is used for planning the harvesting sequence of all the rice fields to be harvested according to the maturity from high to low and sending the harvesting sequence to the data storage module.
The rice field image acquisition module is also used for acquiring rice field images of the rice fields to be harvested according to the harvesting sequence through the unmanned aerial vehicle and sending the rice field images to the data storage module.
The rice field image processing module is also used for preprocessing the rice field image, extracting seedling emergence lines from the rice field image and sending the seedling emergence lines to the data storage module; the rice field image processing module comprises a rice field image preprocessing submodule and a seedling line extraction submodule.
The rice field image preprocessing submodule is used for denoising the rice field image by adopting a median filtering method and carrying out graying processing on the denoised rice field image.
The seedling line extraction submodule is used for extracting grain outlines from the rice field images subjected to graying processing according to color difference, then performing image segmentation on the rice field images, extracting the range of grains, analyzing the number of pixel points contained in the grain range, and performing seedling line extraction according to the pixel point density of the grains, wherein the seedling lines are straight lines formed by a row of rice in a rice field, one rice field image comprises one rice field, and one rice field image comprises a plurality of seedling lines.
The path planning module is also used for planning rice harvesting paths of all blocks of rice fields to be harvested according to the seedling lines, selecting one end of each adjacent seedling line to be connected, connecting all the seedling lines in the rice field image into a rice harvesting path, and planning the rice harvesting paths by adopting a neural network model generated by training a plurality of seedling line data.
The sampling planning module is used for planning harvesting sampling points according to the rice field image and the rice harvesting path; the sampling planning module extracts the paddy field profile according to the paddy field image, extracts the pixel of the paddy field profile, calculates the paddy field area according to pixel, resolution ratio and unmanned aerial vehicle flying height, and the number of the sampling points is reaped according to the paddy field area planning, and the sampling points are randomly arranged in the rice reaping path.
The rice image acquisition module comprises a camera which is arranged at the front end of the rice harvester and is used for acquiring rice images at harvesting sampling points when rice is harvested; the rice image analysis module comprises a rice image processing submodule and a rice quality analysis submodule.
The rice image processing submodule is used for correcting the distortion of the rice image; the camera is arranged at the front end of the harvester, collected rice images are clear, but the number of corresponding pixel points of rice with the same size on the rice images at different distances is different due to overlooking shooting of the camera, so that the subsequent analysis and calculation are influenced, and the distortion of the rice images is corrected by adopting the existing image deformity correction algorithm.
The rice quality analysis submodule is used for extracting the characteristics of rice in a rice image by adopting a machine vision technology, analyzing the rice quality according to the characteristics of the rice, and sending the characteristic data and the rice quality data of the rice to the data storage module; the characteristics of the rice comprise the type of the rice, the number of grains, the size of the grains, the completeness of the grains, the color of the rice and the existence of insect damage and mildew on the surface of the rice; the rice quality is divided into good quality rice, inferior quality rice and poor quality rice, the data storage module stores a rice characteristic comparison table, and the rice characteristic comparison table comprises characteristic data ranges of various types of rice with various qualities and is used for comparing the characteristic data with the extracted characteristic data of the rice in the rice image and analyzing the rice quality.
The rice image analysis module also comprises a rice yield estimation submodule; and the rice yield estimation submodule is used for counting the rice yield in the rice field in unit area at the sampling point according to the rice image and estimating the rice yield in the whole rice field according to the effective planting area of the whole rice field.
The data storage module adopts a cloud storage and is used for storing image data, maturity data, harvesting sequence data, rice field image data, seedling line data, rice harvesting path data, rice field area data, sampling point data, rice image data, rice characteristic data, rice quality data and rice yield data, and storing the corresponding rice field image data, seedling line data, rice harvesting path data, rice field area data, sampling point data, rice image data, rice characteristic data, rice quality data and rice yield data into a group of data.
Example two
The second embodiment is basically as shown in fig. 2, and the difference between the second embodiment and the previous embodiments is that the rice harvester path planning control system of the second embodiment further includes a paddy field image setting module;
the rice field image setting module comprises a drawing program, a user directly draws a rice field image and a seedling line through the drawing program and sends the rice field image and the seedling line to the data storage module, and the path planning module plans a rice harvesting route according to the rice field image and the seedling line drawn by the user and sends the rice harvesting route to the data storage module.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (9)
1. A rice harvester path planning control system based on machine vision is characterized in that: the rice field image processing system comprises a rice field image acquisition module, a rice field image processing module, a path planning module, a sampling planning module, a rice image acquisition module, a rice image analysis module and a data storage module;
the rice field image acquisition module is used for acquiring all the images of the rice field to be harvested;
the rice field image processing module is used for preprocessing the images and identifying the maturity of rice in each rice field to be harvested from the images;
the path planning module is used for planning the harvesting sequence of all rice fields to be harvested according to the maturity from high to low;
the rice field image acquisition module is also used for acquiring rice field images of each rice field to be harvested according to the harvesting sequence;
the paddy field image processing module is also used for preprocessing the paddy field image and extracting a seedling emergence line from the paddy field image;
the path planning module is also used for planning rice harvesting paths of the rice fields to be harvested according to the seedling lines;
the sampling planning module is used for planning harvesting sampling points according to the rice field image and the rice harvesting path;
the rice image acquisition module is used for acquiring rice images at harvesting sampling points during rice harvesting;
the rice image analysis module is used for processing the rice image and analyzing the rice quality;
the data storage module is used for storing image data, maturity data, harvesting sequence data, rice field image data, seedling line data, rice harvesting path data, sampling point data, rice image data and rice quality data.
2. The machine-vision-based rice harvester path planning control system of claim 1, wherein: the rice field image processing module comprises a rice field image preprocessing submodule and a seedling line extraction submodule;
the rice field image preprocessing submodule is used for carrying out denoising processing on the rice field image by adopting a median filtering method and carrying out graying processing on the denoised rice field image;
and the seedling line extraction submodule is used for extracting the seedling lines of the rice field image subjected to the graying treatment according to the pixel point density of the grains.
3. The machine-vision-based rice harvester path planning control system of claim 1, wherein: the rice image acquisition module comprises a camera which is arranged at the front end of the rice harvester, and the rice image analysis module comprises a rice image processing submodule and a rice quality analysis submodule.
4. The machine-vision-based rice harvester path planning control system of claim 1, wherein: the rice image processing submodule is used for correcting the distortion of the rice image;
the rice quality analysis submodule is used for extracting the characteristics of rice in the rice image and analyzing the rice quality according to the characteristics of the rice.
5. The machine-vision-based rice harvester path planning control system of claim 4, wherein: the characteristics of the rice comprise the type of the rice, the number of grains, the size of the grains, the completeness of the grains, the color of the rice and the existence of insect pests and mildew on the surface of the rice.
6. The machine-vision-based rice harvester path planning control system of claim 5, wherein: the rice quality is divided into good quality rice, inferior quality rice and poor quality rice, the data storage module stores a rice characteristic comparison table, and the rice characteristic comparison table comprises characteristic data ranges of various types of rice with various qualities and is used for comparing the characteristic data with the extracted characteristic data of the rice in the rice image and analyzing the rice quality.
7. The machine-vision-based rice harvester path planning control system of claim 1, wherein: the sampling planning module extracts the rice field outline according to the rice field image, judges the rice field area, plans the number of reaping sampling points according to the rice field area, and randomly sets the reaping sampling points in a rice reaping path.
8. The machine-vision-based rice harvester path planning control system of claim 1, wherein: the rice yield estimation submodule is also included;
and the rice yield estimation submodule is used for counting the rice yield in the rice field in unit area at the sampling point according to the rice image and estimating the rice yield in the whole rice field according to the effective planting area of the whole rice field.
9. The machine-vision-based rice harvester path planning control system of claim 1, wherein: the rice field image setting module is also included;
and the rice field image setting module is used for directly drawing the rice field image and the seedling line by a user and sending the rice field image and the seedling line to the data storage module.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114115338A (en) * | 2021-11-11 | 2022-03-01 | 河北英虎农业机械股份有限公司 | Corn harvesting method based on unmanned aerial vehicle cooperation |
| CN114503827A (en) * | 2021-12-21 | 2022-05-17 | 河南福多电力工程有限公司 | Small-sized decentralized operation intelligent management and control system and operation method thereof |
| CN116109268A (en) * | 2023-02-01 | 2023-05-12 | 泰州市衡顺电控科技有限公司 | Intelligent agriculture supervision system and method based on Internet of things |
| CN116777087A (en) * | 2023-08-24 | 2023-09-19 | 南京市农业装备推广中心 | Intelligent agriculture layout method and system |
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2020
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114115338A (en) * | 2021-11-11 | 2022-03-01 | 河北英虎农业机械股份有限公司 | Corn harvesting method based on unmanned aerial vehicle cooperation |
| CN114115338B (en) * | 2021-11-11 | 2023-11-14 | 河北英虎农业机械股份有限公司 | Corn harvesting method based on unmanned aerial vehicle cooperation |
| CN114503827A (en) * | 2021-12-21 | 2022-05-17 | 河南福多电力工程有限公司 | Small-sized decentralized operation intelligent management and control system and operation method thereof |
| CN116109268A (en) * | 2023-02-01 | 2023-05-12 | 泰州市衡顺电控科技有限公司 | Intelligent agriculture supervision system and method based on Internet of things |
| CN116109268B (en) * | 2023-02-01 | 2024-05-17 | 泰州市衡顺电控科技有限公司 | Intelligent agriculture supervision system and method based on Internet of things |
| CN116777087A (en) * | 2023-08-24 | 2023-09-19 | 南京市农业装备推广中心 | Intelligent agriculture layout method and system |
| CN116777087B (en) * | 2023-08-24 | 2023-12-15 | 夏露 | Intelligent agriculture layout method and system |
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