CN113996557B - Online monitoring system and method for trellis grape - Google Patents
Online monitoring system and method for trellis grape Download PDFInfo
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- CN113996557B CN113996557B CN202111280726.1A CN202111280726A CN113996557B CN 113996557 B CN113996557 B CN 113996557B CN 202111280726 A CN202111280726 A CN 202111280726A CN 113996557 B CN113996557 B CN 113996557B
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- 235000014787 Vitis vinifera Nutrition 0.000 title claims abstract description 50
- 235000009754 Vitis X bourquina Nutrition 0.000 title claims abstract description 49
- 235000012333 Vitis X labruscana Nutrition 0.000 title claims abstract description 49
- 238000012544 monitoring process Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 title claims abstract description 21
- 240000006365 Vitis vinifera Species 0.000 title description 2
- 241000219095 Vitis Species 0.000 claims abstract description 48
- 241000219094 Vitaceae Species 0.000 claims abstract description 31
- 235000021021 grapes Nutrition 0.000 claims abstract description 31
- 238000000605 extraction Methods 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 230000005540 biological transmission Effects 0.000 claims abstract description 7
- 238000003860 storage Methods 0.000 claims abstract description 4
- 238000012545 processing Methods 0.000 claims description 6
- 238000010008 shearing Methods 0.000 claims description 5
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- 240000000560 Citrus x paradisi Species 0.000 description 1
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- 235000013399 edible fruits Nutrition 0.000 description 1
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- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/04—Sorting according to size
- B07C5/10—Sorting according to size measured by light-responsive means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
- B07C2501/009—Sorting of fruit
Abstract
The invention discloses a trellis grape on-line monitoring system and an on-line monitoring method applied to the trellis grape on-line monitoring system. The system comprises a growth monitoring module, an image recognition module, a feature extraction module, a data temporary storage module, a remote transmission module and an alarm module; the growth monitoring module comprises a camera system and a track system, wherein the camera system further comprises a CCD camera and an infrared thermal image monitor, and the track system further comprises a plurality of vertical lifting columns arranged in the circumferential direction and at least one liftable annular track erected on the vertical lifting columns. On one hand, the invention can improve the data acquisition precision by constructing a camera system and a track system which are suitable for the shed frame, and realize the accurate analysis of grape growth vigor; on the other hand, the grapes can be scientifically classified according to the accurate analysis of the grape vigor, so that proper picking means can be adopted according to different grades.
Description
Technical Field
The invention belongs to the technical field of agricultural planting automation, and particularly relates to an online monitoring system and method based on the Internet of things technology, which are applied to trellis grapes.
Background
Grape is rich in vitamins, minerals, anticancer microelements and antioxidants, and as a result, the grape is more and more favored by people along with the increasingly improved living conditions, and the planting scale is enlarged year by year. The monitoring of grape growth is particularly important when the agricultural planting automation and mechanization are popularized. However, due to the large number of growth parameters that need to be monitored for effective monitoring of grapes, the scale range is also very large, and the need for modern production has not been met far enough by manual observation and analysis alone.
Various automated grape growth monitoring systems and methods based on image recognition have been proposed in the prior art, however, they still have the following problems: firstly, the existing image acquisition equipment has single function and insufficient data acquisition precision, for grapes, the fruit grains are overlapped, the grapes and grape leaves are mutually covered, and different spikes are also subjected to staggered interaction; secondly, due to insufficient acquisition precision, the acquired growth parameters related to grape growth vigor are not complete or accurate, so that the grape growth vigor cannot be accurately monitored; thirdly, the existing picking mode is single, and grapes are not classified according to the growth vigor of the grapes, so that proper picking means are adopted according to different grades.
Disclosure of Invention
The invention provides a canopy frame grape on-line monitoring system and a monitoring method aiming at the defects in the prior art.
The invention provides an on-line monitoring system for trellis grapes, which comprises a growth monitoring module, an image recognition module, a characteristic extraction module, a data temporary storage module, a remote transmission module and an alarm module;
the growth monitoring module comprises a camera system and a track system, wherein the camera system further comprises a CCD camera and an infrared thermal image monitor, and the track system further comprises a plurality of vertical lifting columns arranged in the circumferential direction and at least one liftable annular track erected on the plurality of vertical lifting columns;
the CCD camera is arranged on the annular track in a sliding mode, and the infrared thermal image monitor is arranged on the vertical lifting column in a sliding mode.
Preferably, the number of the annular tracks is two, each annular track is provided with a coordinate scale and a CCD camera, and each CCD camera can perform angle adjustment of-30 to 45 degrees in the vertical direction and can perform 2 to 10 times of optical zooming; preferably, one of the two annular rails located below further comprises a telescopic rod, one end of the telescopic rod is slidably arranged on the annular rail, and the other end of the telescopic rod is fixedly connected with the CCD camera.
Preferably, the pattern recognition module is used for performing cross processing on data collected by the CCD camera and the infrared thermal image monitor.
Preferably, the feature extraction module performs image restoration and feature extraction on the degraded image based on a Bayesian classifier.
Preferably, the remote transmission module comprises one or more of wifi, 5g, optical fiber, bluetooth, infrared and other communication means.
Meanwhile, the invention also provides an online monitoring method applied to the trellis grape online monitoring system, which comprises the following steps:
s1: starting an infrared thermal image monitor positioned on the vertical lifting column, adjusting the height of the infrared thermal image monitor, acquiring panoramic images of grapes and grape leaves, converting the panoramic images into gray images, and acquiring spectral information of the grapes and the grape leaves;
s2: determining and adjusting the height of the annular track according to the panoramic image;
s3: and starting a CCD camera positioned on the annular track, positioning a region to be monitored according to the panoramic image acquired by the infrared thermal image monitor, moving the CCD camera, and adjusting the shooting angle and zoom multiple of the CCD camera to acquire a plurality of close-up images of the grape and the grape leaf in the region to be monitored.
Preferably, the method further comprises the following steps:
s4: and carrying out cross processing on the data acquired by the CCD camera and the infrared thermal image monitor.
Preferably, the method further comprises the following steps:
s5: and performing image restoration and feature extraction on the degraded image based on the Bayesian classifier.
Preferably, the feature extraction specifically comprises parameters such as leaf color, leaf texture, leaf shape, coloring rate, maximum grape grain diameter, cluster spike length, cluster maximum transverse diameter, maximum leaf/cluster projection area ratio and the like.
Preferably, the method further comprises the following steps:
s6: analyzing the parameters so as to determine a first threshold condition and a second threshold condition, and driving the picking robot to pick the grapes in a grabbing manner when the analysis result meets the first threshold condition; when the analysis result does not meet the first threshold condition and meets the second threshold condition, driving the picking robot to pick the grapes in a shearing mode; and when the analysis result does not meet the second threshold condition, driving the picking robot to pick the grapes in a sucking mode.
Compared with the prior art, on one hand, the invention can improve the data acquisition precision by constructing a track system suitable for the shed frame and arranging the CCD camera and the infrared thermal image monitor thereon and by the cooperation of the CCD camera and the infrared thermal image monitor, thereby realizing the accurate analysis of the grape growth vigor; on the other hand, the grapes can be scientifically classified according to the accurate analysis of the grape vigor, so that proper picking means can be adopted according to different grades.
Drawings
FIG. 1 is a schematic diagram of an on-line monitoring system for trellis grapes applied to agricultural planting;
FIG. 2 is a flow chart of an on-line monitoring method applied to a trellis grape on-line monitoring system.
Detailed Description
The invention is further described below with reference to the drawings and examples.
Embodiment one:
as shown in FIG. 1, the invention provides an on-line monitoring system for trellis grapes, which comprises a growth monitoring module, an image recognition module, a feature extraction module, a data temporary storage module, a remote transmission module and an alarm module;
the growth monitoring module comprises a camera system and a track system, wherein the camera system further comprises a CCD camera and an infrared thermal image monitor, and the track system further comprises a plurality of vertical lifting columns arranged in the circumferential direction and at least one liftable annular track erected on the plurality of vertical lifting columns;
the CCD camera is arranged on the annular track in a sliding mode, and the infrared thermal image monitor is arranged on the vertical lifting column in a sliding mode.
The number of the annular tracks is two, each annular track is provided with a coordinate scale and a CCD camera, and each CCD camera can be subjected to angle adjustment of-30-45 degrees in the vertical direction and can be subjected to 2-10 times of optical zooming; preferably, one of the two annular rails located below further comprises a telescopic rod, one end of the telescopic rod is slidably arranged on the annular rail, and the other end of the telescopic rod is fixedly connected with the CCD camera.
The pattern recognition module is used for carrying out cross processing on the data acquired by the CCD camera and the infrared thermal image monitor.
The feature extraction module performs image restoration and feature extraction on the degraded image based on a Bayesian classifier.
The remote transmission module comprises one or more of wifi, 5g, optical fiber, bluetooth, infrared and other communication means.
The infrared thermal image monitors are arranged on the vertical lifting columns in a sliding manner, so that on one hand, the monitoring and anti-theft effects can be achieved, on the other hand, the infrared thermal image monitors are arranged on the plurality of vertical lifting columns arranged in the circumferential direction, the stereoscopic panoramic imaging of grape vine on the shed frame is achieved, and a basis is provided for the running position of the CCD camera; in addition, the information such as spectrum and the like acquired by the infrared thermal image monitor can also be used as the basis for further cross analysis of the grape.
Considering that there is overlapping among the grape, grape and grape leaf mask each other, and the difference is interacted between different ears, consequently, set up two liftable annular tracks, all set up the CCD camera on every annular track, adjust the shooting angle and the zoom multiple of two CCD cameras to form the different cross coverage area of depth of field, improved the image acquisition precision.
Furthermore, the CCD camera on the annular track below can extend to the direction of the object to be monitored through the telescopic rod, samples at different horizontal positions, and samples at different vertical positions in a more matched mode with the height adjustment of the annular track above, so that three-point sampling or multi-point sampling of the object to be monitored from different spatial positions is realized, and the real image of the trellis grape can be better constructed by carrying out image processing and algorithmic combination on sampling results of the CCD camera at a plurality of different spatial positions.
The invention provides an omnibearing image monitoring system with variable track, variable focus, variable distance and adjustable angle, which is particularly suitable for monitoring and analyzing specific crops such as trellis grapes.
Embodiment two:
as shown in fig. 2, the invention also provides an on-line monitoring method applied to the on-line monitoring system of the trellis grape, comprising the following steps:
s1: starting an infrared thermal image monitor positioned on the vertical lifting column, adjusting the height of the infrared thermal image monitor, acquiring panoramic images of grapes and grape leaves, converting the panoramic images into gray images, and acquiring spectral information of the grapes and the grape leaves;
s2: determining and adjusting the height of the annular track according to the panoramic image;
s3: and starting a CCD camera positioned on the annular track, positioning a region to be monitored according to the panoramic image acquired by the infrared thermal image monitor, moving the CCD camera, and adjusting the shooting angle and zoom multiple of the CCD camera to acquire a plurality of close-up images of the grape and the grape leaf in the region to be monitored.
S4: and carrying out cross processing on the data acquired by the CCD camera and the infrared thermal image monitor.
S5: and performing image restoration and feature extraction on the degraded image based on the Bayesian classifier.
The characteristic extraction comprises parameters such as leaf color, leaf texture, leaf shape, coloring rate, maximum grape grain diameter, grape cluster spike length, grape cluster transverse diameter, maximum leaf/grape cluster projection area ratio and the like.
The calculation of parameters such as the grape size based on the machine vision technique in combination with means such as the chessboard calibration reference board or the laser ranging, etc. belongs to the conventional technique in the art and will not be described in detail here. The invention omits the extra configuration of the reference object by arranging the coordinate scale on the annular track.
S6: analyzing the parameters so as to determine a first threshold condition and a second threshold condition, and driving the picking robot to pick the grapes in a grabbing manner when the analysis result meets the first threshold condition; when the analysis result does not meet the first threshold condition and meets the second threshold condition, driving the picking robot to pick the grapes in a shearing mode; and when the analysis result does not meet the second threshold condition, driving the picking robot to pick the grapes in a sucking mode.
Specifically, parameters such as the maximum grape grain diameter, the cluster spike length, the cluster maximum transverse diameter, the maximum blade/cluster projection area ratio and the like are always visual representations of the grape grain grade, so that the automatic grading of the grapes can be realized by fully considering the parameters and setting reasonable threshold conditions.
Table 1 exemplarily shows a setting of the first threshold condition and the second threshold condition.
TABLE 1
The setting of the first threshold condition and/or the second threshold condition may be a related numerical range that satisfies the four parameters at the same time, or may be a related numerical range that satisfies only a few of the four parameters.
The setting of the threshold condition needs to fully consider factors such as grape variety, greenhouse temperature, sunlight duration and the like, and a practitioner in the field can set the threshold condition according to specific situations, and the setting is not repeated here.
Furthermore, considering grapes of different grades and different growing conditions, the volume, the weight, the stem and stem connection strength and the like of the grapes are different, the existing picking mode is single and is not targeted, on one hand, the realization of large-scale automatic picking is not facilitated, and on the other hand, the grape fruits can be damaged to a certain extent by the improper picking mode. For example, for grape strings with big individual heads and good growth vigor, the falling caused by shearing and picking can damage the pulp, and the situation can be well avoided by a mechanical arm grabbing mode; for small bunches of grapes which are small in size, poor in growth vigor and expected to be incapable of continuing to grow, grape branches and leaves are often covered, grabbing and shearing are inconvenient, adjacent branches can be possibly damaged, the situation can be well avoided through a vacuum tube suction mode, and fixed-point cleaning can be achieved.
While the invention has been described in detail in the foregoing general description and with reference to specific embodiments thereof, certain modifications and improvements may be made thereto. The above description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, but other variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention, and various combinations of the embodiments are still included in the scope of the invention.
Claims (6)
1. An online monitoring method of a trellis grape online monitoring system comprises a growth monitoring module, an image recognition module, a feature extraction module, a data temporary storage module, a remote transmission module and an alarm module;
the growth monitoring module comprises a camera system and a track system, wherein the camera system further comprises a CCD camera and an infrared thermal image monitor, and the track system further comprises a plurality of vertical lifting columns arranged in the circumferential direction and at least one liftable annular track erected on the plurality of vertical lifting columns;
the CCD camera is arranged on the annular track in a sliding manner, and the infrared thermal image monitor is arranged on the vertical lifting column in a sliding manner;
characterized in that the method comprises the following steps:
s1: starting an infrared thermal image monitor positioned on the vertical lifting column, adjusting the height of the infrared thermal image monitor, acquiring panoramic images of grapes and grape leaves, converting the panoramic images into gray images, and acquiring spectral information of the grapes and the grape leaves;
s2: determining and adjusting the height of the annular track according to the panoramic image;
s3: starting a CCD camera positioned on the annular track, positioning a region to be monitored according to the panoramic image acquired by the infrared thermal image monitor, moving the CCD camera, and adjusting the shooting angle and zoom multiple of the CCD camera to acquire a plurality of close-up images of the grape and grape leaves in the region to be monitored;
s4: the data collected by the CCD camera and the infrared thermal image monitor are subjected to cross processing;
s5: performing image restoration and feature extraction on the degraded image based on a Bayesian classifier;
s6: analyzing the feature extraction result so as to determine a first threshold condition and a second threshold condition, and driving the picking robot to pick the grapes in a grabbing manner when the analysis result meets the first threshold condition; when the analysis result does not meet the first threshold condition and meets the second threshold condition, driving the picking robot to pick the grapes in a shearing mode; and when the analysis result does not meet the second threshold condition, driving the picking robot to pick the grapes in a sucking mode.
2. The on-line monitoring method according to claim 1, wherein the number of the annular tracks is two, each annular track is provided with a coordinate scale and a CCD camera, and each CCD camera can perform an angle adjustment of-30 to 45 degrees in the vertical direction and can perform 2 to 10 times of optical zooming; one of the two annular tracks, which is positioned below, further comprises a telescopic rod, one end of the telescopic rod is arranged on the annular track in a sliding manner, and the other end of the telescopic rod is fixedly connected with the CCD camera.
3. The on-line monitoring method according to claim 2, wherein the image recognition module is configured to cross-process data collected by the CCD camera and the infrared thermal image monitor.
4. The online monitoring method of claim 3, wherein the feature extraction module performs image restoration and feature extraction on the degraded image based on a bayesian classifier.
5. The on-line monitoring method of claim 4, wherein the remote transmission module comprises one or more of wifi, 5g, fiber optics, bluetooth, infrared.
6. The on-line monitoring method according to claim 1, wherein the feature extraction specifically comprises leaf color, leaf texture, leaf shape, coloring rate, maximum grape grain diameter, cluster spike length, cluster maximum transverse diameter, maximum leaf/cluster projected area ratio.
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| CN202111280726.1A CN113996557B (en) | 2021-11-01 | 2021-11-01 | Online monitoring system and method for trellis grape |
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| CN202111280726.1A CN113996557B (en) | 2021-11-01 | 2021-11-01 | Online monitoring system and method for trellis grape |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103699095A (en) * | 2013-12-25 | 2014-04-02 | 北京交通大学 | Greenhouse plant growth posture monitoring system based on binocular stereo vision and greenhouse plant growth posture monitoring method based on binocular stereo vision |
| CN104764533A (en) * | 2015-03-31 | 2015-07-08 | 梁伟 | Intelligent agricultural system based on unmanned aerial vehicle image collecting and thermal infrared imager |
| US9462749B1 (en) * | 2015-04-24 | 2016-10-11 | Harvest Moon Automation Inc. | Selectively harvesting fruits |
| CN109781729A (en) * | 2019-01-17 | 2019-05-21 | 广西慧云信息技术有限公司 | A kind of grape physiological conditions online monitoring system |
| CN111666883A (en) * | 2020-06-08 | 2020-09-15 | 佛山科学技术学院 | Grape picking robot target identification and fruit stalk clamping and cutting point positioning method |
| AU2020101843A4 (en) * | 2020-08-15 | 2020-09-24 | Ananth, Christo DR | A system monitoring for harvesting of farming using drone technology |
-
2021
- 2021-11-01 CN CN202111280726.1A patent/CN113996557B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103699095A (en) * | 2013-12-25 | 2014-04-02 | 北京交通大学 | Greenhouse plant growth posture monitoring system based on binocular stereo vision and greenhouse plant growth posture monitoring method based on binocular stereo vision |
| CN104764533A (en) * | 2015-03-31 | 2015-07-08 | 梁伟 | Intelligent agricultural system based on unmanned aerial vehicle image collecting and thermal infrared imager |
| US9462749B1 (en) * | 2015-04-24 | 2016-10-11 | Harvest Moon Automation Inc. | Selectively harvesting fruits |
| CN109781729A (en) * | 2019-01-17 | 2019-05-21 | 广西慧云信息技术有限公司 | A kind of grape physiological conditions online monitoring system |
| CN111666883A (en) * | 2020-06-08 | 2020-09-15 | 佛山科学技术学院 | Grape picking robot target identification and fruit stalk clamping and cutting point positioning method |
| AU2020101843A4 (en) * | 2020-08-15 | 2020-09-24 | Ananth, Christo DR | A system monitoring for harvesting of farming using drone technology |
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