CN114190166A - Tea picking method based on image and point cloud data processing - Google Patents
Tea picking method based on image and point cloud data processing Download PDFInfo
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- CN114190166A CN114190166A CN202111537896.3A CN202111537896A CN114190166A CN 114190166 A CN114190166 A CN 114190166A CN 202111537896 A CN202111537896 A CN 202111537896A CN 114190166 A CN114190166 A CN 114190166A
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- tea
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D91/00—Methods for harvesting agricultural products
- A01D91/04—Products growing above the soil
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D46/00—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs
- A01D46/04—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs of tea
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30188—Vegetation; Agriculture
Abstract
The invention discloses a tea picking method based on image and point cloud data processing, and relates to the technical field of tea picking. The invention relates to a tea picking method based on image and point cloud data processing, which comprises the following steps: shooting by using a thermal imager to obtain a thermal image of the cutting knife and the tea tree crown, scanning by using a laser radar to obtain point cloud information of the cutting knife and the tea tree crown, selecting an interested area S according to the thermal image, and calculating to obtain the number n of tender buds in the interested area S; fitting to obtain a curve of the tea tree crown, and calculating to obtain a distance d2 between the cutting knife and the tea tree crown; and judging according to n and d2, adjusting the position of the cutting knife, and finally cutting. The invention discloses a tea picking method based on image and point cloud data processing.
Description
Technical Field
The invention relates to the technical field of tea picking, in particular to a tea picking method based on image and point cloud data processing.
Background
China is a genuine big country for tea planting and production, in 2020, the yield and the value of national dried raw tea respectively reach 298.60 ten thousand tons and 2626.58 hundred million yuan, however, the traditional tea production mostly takes families as a unit, and the tea is mainly picked and processed manually, and the tea belongs to labor-intensive industry. From the tea leaf picking perspective, on one hand, the production cost of the tea leaves is increased due to high manual picking cost; on the other hand, the tea leaf picking time is short, and the efficiency of manual picking is low, so that the yield of the tea leaves is severely limited. At present, the contradiction between tea picking and labor in China has become the bottleneck of the development of the tea industry, so that many scientific and technical personnel begin to research automatic tea picking devices to solve the problems.
The existing tea plucking machines can be divided into a selective tea plucking machine and a non-selective tea plucking machine according to different plucking modes. The selective tea picking machine is mainly used for picking famous tea, and the non-selective tea picking machine is mainly used for picking bulk tea. Most of the tea pluckers popularized and used at present are produced aiming at plucking fresh tea leaves from a large amount of tea, and the tea pluckers utilize mechanical power to drive blades to move, rapidly shear and collect the tea leaves. The tea plucking machine has high plucking efficiency, can reduce labor cost and labor intensity, and relieves the contradiction of insufficient labor for plucking tea to a certain extent.
Although the tea-leaf picker can improve picking efficiency, the tea-leaf picker is lack of selectivity, the tea-leaf picker does not separate old and tender during working, one blade is cut off, the sizes of buds and leaves are different, the integrity is poor, mechanical damage to tea trees is large, simultaneously, picked leaves are old and tender and mixed, old stems and old leaves and damaged leaves are high in content, popularization and use of the tea-leaf picker are influenced to a certain extent, automatic and intelligent tea-leaf picking machinery is an urgent need of the tea industry, the integrity and consistency of young shoot picking are improved, the overall quality of tea is improved, image recognition is developed, tender buds are selected and picked, but the problem of picking precision cannot be solved only by adopting the image recognition mode due to interactive overlapping of the old leaves and the tender leaves of the tea trees, the color difference of the tea leaves is small.
Disclosure of Invention
In view of the above problems, the invention aims to disclose a tea picking method based on image and point cloud data processing, which is characterized in that thermal imagers and laser radars are used for processing tea tree crown information, and meanwhile, the relative position between a cutting knife and the tea tree crown is adjusted, so that the tea picking precision can be effectively improved.
Specifically, the tea picking method based on image and point cloud data processing comprises the following steps:
s1: shooting by using a thermal imager to obtain a thermal image of the cutting knife and the crown of the tea tree, and scanning by using a laser radar to obtain point cloud information of the cutting knife and the crown of the tea tree;
s2: selecting an interested area S according to the acquired thermodynamic diagram image of the crown of the tea tree, and calculating to acquire the number n of tender shoots in the interested area S;
s3: respectively fitting to obtain a curve of the tea tree crown according to the obtained point cloud information of the cutter and the tea tree crown, and calculating to obtain a distance d2 between the cutter and the tea tree crown;
s4: judging according to the number n of the tender shoots and the distance d2 between the cutting knife and the crown of the tea tree, adjusting the position of the cutting knife, and finally cutting.
Further, the interested area S is a rectangular area and is located right in front of the cutting knife, the width of the interested area S is the same as the length of the cutting knife, the straight line at the bottom of the interested area S is the projection of the cutting knife on the tea tree, and the distance between the straight line at the front end and the straight line at the bottom is d 1.
Further, the number of the tender shoots in the step S2 is detected by using a target detection algorithm YOLO-V5 in deep learning.
Further, in the step S4, when the cutter is located above the tea tree crown, the value of d2 is positive, and the value is larger the further away, and when the cutter is located above the tea tree crown, the value of d2 is negative, and the value is smaller the further away.
Further, the specific determination criteria in the step S4 are:
if n < n1 and d2>0, this means that the number n of shoots within the region of interest S is too low and that the cutting knife is above the shoots, no cutting is required and the position of the cutting knife does not need to be adjusted.
If n < n1 and d2<0, it means that the number n of shoots in the region of interest S is too low, and the cutting knife is below the shoots, the cutting knife position is low, and no cutting is needed, and an upward adjustment is needed.
If n > n1 and d2< -r, it means that the number n of shoots in the region of interest S is sufficient and the knife is below the shoots at this time, but the knife position is too low, at which time cutting is required, and the knife position is moved upwards.
If n > n1 and-r < d2<0, it means that the number n of shoots in the region of interest S is sufficient and the knife is below the shoots at the appropriate position, at which time cutting is required and the knife position does not need to be adjusted.
If n is greater than n1 and d2 is greater than 0, the number n of the tender shoots in the region S of interest is enough, the cutting knife is positioned above the tender shoots, the position of the cutting knife is too high, the cutting is needed, and the position of the cutting knife needs to be adjusted downwards;
wherein r is the theoretical difference of tender shoots of the tea plant relative to the cutting knife during cutting, and n1 is the initial set tender shoot number.
The invention has the beneficial effects that:
1) the invention discloses a tea picking method based on image and point cloud data processing, which solves the problem of automatic control of the position and the angle of a cutting knife, reduces the requirements of a riding type tea picking machine on the topography of a tea garden and the flatness of a tea crown, and widens the application range of the tea picking machine.
2) The problem of cutting knife position control error caused by long-time fatigue operation of passengers is reduced, and the picking efficiency and quality are ensured.
Drawings
FIG. 1 is a schematic view of the structure of the tea plucking machine of the present invention;
FIG. 2 is a bottom view of the tea-plucking machine support platform;
FIG. 3 is a cross-sectional view taken along line A-A of FIG. 2;
FIG. 4 is a schematic view showing a state during cutting;
the thermal imager comprises a movable base 1, a rack 2, a supporting platform 3, a base 31, a supporting plate 32, a supporting lug 33, a movable roller 34, a ball screw 4, a roller sliding rail 5, a groove 51, a bearing seat 6, a rotating motor 7, a direct current push rod motor 8, a laser radar sensor 9, a thermal imager 10 and a cutting knife 11.
Detailed Description
The present invention will be described in detail with reference to specific examples below:
examples
The invention relates to a tea picking method based on image and point cloud data processing, which is characterized in that a thermal imager and a laser radar are used for picking tea leaves, the tea picking precision can be effectively improved, the tea picking method can be realized by adopting a tea picking machine shown in figure 1, the tea picking machine comprises a movable base 1 and a machine frame 2 arranged on the movable base 1, a tea picking mechanism is fixedly arranged on the machine frame 2, the tea picking mechanism comprises a slide rail unit and a supporting platform 3 arranged on the slide rail unit in a sliding way, the slide rail unit comprises a ball screw 4 and a roller slide rail 5 which are arranged in parallel, the supporting platform 3 comprises a base 31 and a supporting plate 32 which are fixedly connected, the ball screw 4 and the roller slide rail 5 penetrate into the base 31, one end of the ball screw 4 is fixedly arranged on the machine frame 2 through a bearing seat 6, the other end of the ball screw is fixedly connected with a rotating motor 7, and the rotating motor 7 is fixedly arranged on the machine frame 2, both ends of the roller slide rail 5 are fixedly arranged on the frame 2 through a bearing seat 6, a groove 51 with an upward opening is arranged on the roller slide rail 5, two symmetrically arranged support lugs 33 are fixedly arranged on the support plate 32 at the positions corresponding to the groove 51, a movable roller 34 is arranged between the two support lugs 33, the movable roller 34 is positioned in the groove 51, and contacts with the bottom of the groove 51, the structural arrangement of the ball screw 4 and the roller slide rail 5 enables the support platform 3 to run more stably, when in use, the rotating motor 7 is utilized to drive the ball screw 4 to rotate, the ball screw 4 drives the supporting platform 3 to move horizontally, so as to adjust the position of the cutting unit, the arrangement of the groove 51 and the moving roller 34 reduces the friction force between the base 31 and the sliding rail unit, and the operation is more stable and the response is faster in the moving process, thereby being beneficial to reducing the energy consumption.
Two direct current push rod motors 8 are hinged to the supporting platform 3, the two direct current push rod motors 8 are symmetrically arranged on the supporting platform 3, a laser radar sensor 9 and a thermal imager 10 are fixedly arranged at the position, between the two direct current push rod motors 8, on the supporting platform 3, the laser radar sensor 9, the thermal imager 10 and the signal input end of the PLC are in signal connection, the signal output end of the PLC is in signal connection with the direct current push rod motors, accordingly, efficient cutting is guaranteed to a certain extent, and a cutting knife 11 is hinged and fixedly arranged at the free end of a push rod of the direct current push rod motor 8.
Tea picking is carried out by utilizing the tea picking machine, and the tea picking machine comprises the following specific steps:
s1: the tea plucking machine is moved to the end part of a ridge of tea trees by using the moving base, so that the cutting knife corresponds to the crown position above the tea trees, the supporting platform is moved to a state that the cutting knife is aligned with the end part of the ridge of tea trees, the push rods of the direct current push rod motors above the cutting knife are extended, so that the cutting knife is lowered to a state close to and not in contact with the tea trees, thermal images of the cutting knife and the crowns of the tea trees are obtained by shooting by a thermal imager, and meanwhile, point cloud information of the cutting knife and the crowns of the tea trees is obtained by scanning by using a laser radar;
s2: selecting an interested region S from the thermodynamic diagram image of the tree crown of the tea tree obtained by shooting, wherein the interested region S is a rectangular region and is positioned right in front of the cutting knife, the width of the interested region S is the same as the length of the cutting knife, the bottom straight line of the interested region S is the projection of the cutting knife on the tea tree, the distance between the front end straight line and the bottom straight line is d1, and meanwhile, detecting and calculating by using a target detection algorithm YOLO-V5 in deep learning to obtain the number n of tender buds in the interested region S;
s3: respectively fitting to obtain a curve equation of the tea tree crown according to the obtained point cloud information of the cutting knife and the tea tree crown, and calculating to obtain a distance d2 between the cutting knife and the tea tree crown, wherein when the cutting knife is positioned above the tea tree crown, d2 is a positive value, the farther the distance is, the larger the value is, and when the cutting knife is positioned above the tea tree crown, d2 is a negative value, and the farther the distance is, the smaller the value is;
s4: judging according to the number n of the tender shoots and the distance d2 between the cutter and the crown of the tea tree,
if n < n1 and d2>0, this means that the number n of shoots within the region of interest S is too low and that the cutting knife is above the shoots, no cutting is required and the position of the cutting knife does not need to be adjusted.
If n < n1 and d2<0, it means that the number n of shoots in the region of interest S is too low, and the cutting knife is below the shoots, the cutting knife position is low, and no cutting is needed, and an upward adjustment is needed.
If n > n1 and d2< -r, it means that the number n of shoots in the region of interest S is sufficient and the knife is below the shoots at this time, but the knife position is too low, at which time cutting is required, and the knife position is moved upwards.
If n > n1 and-r < d2<0, it means that the number n of shoots in the region of interest S is sufficient and the knife is below the shoots at the appropriate position, at which time cutting is required and the knife position does not need to be adjusted.
If n is greater than n1 and d2 is greater than 0, the number n of the tender shoots in the region S of interest is enough, the cutting knife is positioned above the tender shoots, the position of the cutting knife is too high, the cutting is needed, and the position of the cutting knife needs to be adjusted downwards;
wherein r is the theoretical difference of the tender shoots of the tea tree relative to the cutting knife during cutting, n1 is the initial setting of the tender shoot number, the cutting knife is cut after the position of the cutting knife is adjusted to a proper position,
s5: the tea plucker advances along the tea ridge by utilizing the movable base, the tea leaves are continuously cut by the cutting knife, the thermal infrared instrument and the laser radar continuously detect the thermal image of the tea tree crown and the point cloud data of the cutting knife and the tea tree crown in the cutting process, and the PLC controller is utilized to process, so that the calculated n and d2 are respectively kept in the range of n < n1 and | d2| < r.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.
Claims (5)
1. A tea picking method based on image and point cloud data processing is characterized by comprising the following steps:
s1: shooting by using a thermal imager to obtain a thermal image of the cutting knife and the crown of the tea tree, and scanning by using a laser radar to obtain point cloud information of the cutting knife and the crown of the tea tree;
s2: selecting an interested area S according to the acquired thermodynamic diagram image of the crown of the tea tree, and calculating to acquire the number n of tender shoots in the interested area S;
s3: respectively fitting to obtain a curve of the tea tree crown according to the obtained point cloud information of the cutter and the tea tree crown, and calculating to obtain a distance d2 between the cutter and the tea tree crown;
s4: judging according to the number n of the tender shoots and the distance d2 between the cutting knife and the crown of the tea tree, adjusting the position of the cutting knife, and finally cutting.
2. The tea plucking method based on image and point cloud data processing as claimed in claim 1, wherein the region of interest S is a rectangular region located right in front of the cutter and having the same width as the length of the cutter, the straight line at the bottom of the region of interest S is the projection of the cutter on the tea tree, and the distance between the straight line at the front end and the straight line at the bottom is d 1.
3. The tea-leaf picking method based on image and point cloud data processing as claimed in claim 2, wherein the number of tender shoots in the step S2 is detected by using a target detection algorithm YOLO-V5 in deep learning.
4. The tea plucking method based on image and point cloud data processing as claimed in claim 3, wherein in the step S4, when the cutter is located above the crown of the tea tree, the d2 is positive and the value is larger the farther away, and when the cutter is located above the crown of the tea tree, the d2 is negative and the value is smaller the farther away.
5. The tea-plucking method based on image and point cloud data processing as claimed in any one of claims 1 to 4, wherein the specific judgment criteria of the step S4 are as follows:
if n < n1 and d2>0, this means that the number n of shoots within the region of interest S is too low and that the cutting knife is above the shoots, no cutting is required and the position of the cutting knife does not need to be adjusted.
If n < n1 and d2<0, it means that the number n of shoots in the region of interest S is too low, and the cutting knife is below the shoots, the cutting knife position is low, and no cutting is needed, and an upward adjustment is needed.
If n > n1 and d2< -r, it means that the number n of shoots in the region of interest S is sufficient and the knife is below the shoots at this time, but the knife position is too low, at which time cutting is required, and the knife position is moved upwards.
If n > n1 and-r < d2<0, it means that the number n of shoots in the region of interest S is sufficient and the knife is below the shoots at the appropriate position, at which time cutting is required and the knife position does not need to be adjusted.
If n is greater than n1 and d2 is greater than 0, the number n of the tender shoots in the region S of interest is enough, the cutting knife is positioned above the tender shoots, the position of the cutting knife is too high, the cutting is needed, and the position of the cutting knife needs to be adjusted downwards;
wherein r is the theoretical difference of tender shoots of the tea plant relative to the cutting knife during cutting, and n1 is the initial set tender shoot number.
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| CN202111537896.3A CN114190166B (en) | 2021-12-15 | 2021-12-15 | Tea picking method based on image and point cloud data processing |
| JP2022095024A JP7309160B2 (en) | 2021-12-15 | 2022-06-13 | Tea picking method based on image and point cloud data processing |
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| CN202111537896.3A CN114190166B (en) | 2021-12-15 | 2021-12-15 | Tea picking method based on image and point cloud data processing |
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| CN114190166B CN114190166B (en) | 2023-05-02 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116058176A (en) * | 2022-11-29 | 2023-05-05 | 西北农林科技大学 | Fruit and vegetable picking mechanical arm control system based on double-phase combined positioning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB253000A (en) * | 1925-12-01 | 1926-06-10 | Robert Vernon Yates | Improvements in tea harvesting machines |
| CN104063862A (en) * | 2014-06-18 | 2014-09-24 | 浙江工业大学 | Method for controlling cutting knife of tea-leaf picker based on visual inspection |
| CN112131982A (en) * | 2020-09-10 | 2020-12-25 | 安徽农业大学 | Tea tree tender shoot identification method based on convolutional neural network |
| CN113592882A (en) * | 2021-08-09 | 2021-11-02 | 中国林业科学研究院资源信息研究所 | Crown extraction method based on multi-source remote sensing of unmanned aerial vehicle |
| CN113674226A (en) * | 2021-07-31 | 2021-11-19 | 河海大学 | Tea leaf picking machine tea leaf bud tip detection method based on deep learning |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000139164A (en) | 1998-11-05 | 2000-05-23 | Kawasaki Giken Kk | Picking of tea leaf |
| CN1620848A (en) | 2004-12-15 | 2005-06-01 | 西南交通大学 | Automatic tea plucker |
| CN202998870U (en) | 2013-01-18 | 2013-06-19 | 丽水学院 | Tea picking system |
| CN103999635B (en) | 2014-05-21 | 2016-01-06 | 浙江工业大学 | Based on intelligent automatic cutting type tea picking machine and the method for work of machine vision |
| CN110188680B (en) | 2019-05-29 | 2021-08-24 | 南京林业大学 | Tea tree tender shoot intelligent identification method based on factor iteration |
| CN110178540B (en) | 2019-06-13 | 2021-08-17 | 西安邮电大学 | Tea picking machine based on image processing and working method |
| CN111428712B (en) | 2020-03-19 | 2021-11-30 | 青岛农业大学 | Famous tea picking machine based on artificial intelligence recognition and recognition method for picking machine |
-
2021
- 2021-12-15 CN CN202111537896.3A patent/CN114190166B/en active Active
-
2022
- 2022-06-13 JP JP2022095024A patent/JP7309160B2/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB253000A (en) * | 1925-12-01 | 1926-06-10 | Robert Vernon Yates | Improvements in tea harvesting machines |
| CN104063862A (en) * | 2014-06-18 | 2014-09-24 | 浙江工业大学 | Method for controlling cutting knife of tea-leaf picker based on visual inspection |
| CN112131982A (en) * | 2020-09-10 | 2020-12-25 | 安徽农业大学 | Tea tree tender shoot identification method based on convolutional neural network |
| CN113674226A (en) * | 2021-07-31 | 2021-11-19 | 河海大学 | Tea leaf picking machine tea leaf bud tip detection method based on deep learning |
| CN113592882A (en) * | 2021-08-09 | 2021-11-02 | 中国林业科学研究院资源信息研究所 | Crown extraction method based on multi-source remote sensing of unmanned aerial vehicle |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116058176A (en) * | 2022-11-29 | 2023-05-05 | 西北农林科技大学 | Fruit and vegetable picking mechanical arm control system based on double-phase combined positioning |
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| JP2023088826A (en) | 2023-06-27 |
| CN114190166B (en) | 2023-05-02 |
| JP7309160B2 (en) | 2023-07-18 |
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