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Author(s)
First Name Middle Name Surname Role Email
Jiang huan yu ASABE Member
Affiliation
Organization Address Country
College of Biosystems Engineering and Food science, Zhejiang University
268Kuaixuan Road, Hangzhou, 310029, People’s Republic of China
China
Author(s)
First Name Middle Name Surname Role Email
Ren Ye [email protected]
Affiliation
Organization Address Country
College of Biosystems Engineering and Food science, Zhejiang University
268Kuaixuan Road, Hangzhou, 310029,People’s Republic of China
China
Publication Information
Pub ID Pub Date
073127 2007 ASABE Annual Meeting Paper
The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2007. Title of Presentation. ASABE Paper No. 07xxxx. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at [email protected] or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).
An ASABE Meeting Presentation
Paper Number: 073127
Machine Vision For Automatic Seedling Transplanting
Huanyu Jiang, Assistant ProfessorCollege of Biosystems Engineering and Food science, Zhejiang University, 268Kuaixuan Road, Hangzhou, 310029,People’s Republic of China, [email protected].
Ye Ren, Graduate StudentCollege of Biosystems Engineering and Food science, Zhejiang University, 268Kuaixuan Road, Hangzhou, 310029,People’s Republic of China, [email protected].
Written for presentation at the2007 ASABE Annual International Meeting
Sponsored by ASABEMinneapolis Convention Center
Minneapolis, Minnesota17 - 20 June 2007
Abstract. This paper presents a machine vision system for automatic seedling transplanting. The system comprises of a color CCD camera, a PCI frame grabber, a Pentium 4 computer and a lighting box with six fluorescent lamps. To reduce transplanting time, image which seedling plants grow in tray was acquired and processed in order to identify the cells to be transplanted. Overlapping of the border seedlings and extruding leaves from neighbouring cells always leads to identification failures. So a digital image processing algorithm based on morphological watersheds was developed to segment the border of leaves. The Area and the perimeter were extracted, by which whether the cell was suit for transplanting could be determined. In this research, using tomato seedlings as samples, good identification rate for suitable seedling was obtained (97%). The result shows this method could be used in various growing status of seedling, and this system could be used for automatic seedling transplanting robot.
Keywords. Machine vision, Seedling transplanting, Watershed
The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2007. Title of Presentation. ASABE Paper No. 07xxxx. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at [email protected] or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).
IntroductionGreenhouse has been used in agriculture widely all over the world. But the environment in
greenhouse is terrible, where it is always wet and fuggy. More plants now use automatic seeder and seedling growth monitoring systems to produce uniform seedlings with reduced time and labour requirements. Transplanting is an operation frequently performed in plug seedling production systems in greenhouses. However, Seedling transplanting is a labor intensive task, it is necessary to use a robotic transplanter to reduce the labor requirement.
Research for the development of a transplanter began several years ago. Hwang et,(1986) developed a commercial pepper transplanter using a basic robotic manipulator. Kutz et (1987), studied the feasibility of seedling transplanting by utilizing a Puma 560 articulated robot with a parallel-jaw-type gripper. Ting et(1992) developed a plug transplanting workcell based on an AdeptOne selective compliance assembly robot arm type robot. Tesch et (1993) invented a seedling array transplanter, which consisted of a transplant head, a pneumatically mechanical linkage, a carriage, and a computer controller. Onosaka et(1996) developed a transplanter, which can be operated rapidly and has been used commercially. K.H.Ryu et (2001) used Cartesian coordinate axes to develop a robotic transplanter for bedding plants, produced good transplanting performance.
Some of the plug seedling may be absent or growth defectiveness. In order to detect the presence of a seedling, machine vision was used to improve the quality of transplanted seedling tray. Machine vision was able to locate empty cubes and guide the manipulator to transplant the seedling. Beam (1991) used a machine vision to inspect the results of seedling transplanting. Tai (1994) studied the detection of misses on transplanted flats by machine vision techniques and the retransplanting operation with a robot. K.H.Ryu (2001) used machine vision to monitor seedling transplanting, which based on the information about the seedling leaves. The limit in the application of machine vision system mentioned above appears when the overlapping of leaves of seedling in neighbouring plug cell, which leads to the identification failure.
This paper studied an efficient machine vision system for robotic transplanting. A digital image processing algorithm based on morphological watersheds was developed to segment the border of leaves. Analysis accuracy of the machine vision system was tested using a tomato seedling in a multi-cell plug tray.
MATERIALS AND METHODES
System Description
The machine vision system consisted of a CCD camera（Model TMC7DSP, Pulnix）, an industry computer, a frame grabber board and six fluorescent lamps. The camera was equipped with three parallel analog video signals, R (red), G (green), B (blue) corresponding to the NTSC (National Television System Committee) color primaries, and it was mounted on the top of the seedling tray. The six fluorescent lamps mounted next to the digital camera provide a uniform illumination. The frame grabber could provide high color quality, PCI bus for real time transmission of images into memory. The image processing was programmed using Visual C++ 6.0.
Tomato seedlings were selected as samples, and the trays with 50 cells (5 10) and 60 cell cells (6 12) have been used in this research.
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Image analysis
Image analysis algorithm was established for seedling transplanting. The chart of the image procedures were shown in fig.1. More detailed discussion of each procedure step is presented below.
Image Capture
Image segmentation
Character capture
result
End
Figure 1 Flowchart Of Image Processing
Image Capture A digital color image with a resolution of 640×480 was taken by the digital camera, and transferred to the industry computer. The images were shown in fig.2.
Image segmentation
In order to extract the object we are interested in from the background, image threshold was used in this study.
As we all know, there were great difference in color between seedling plants and plug tray. Based on the color character of the seedling plants, we defined the chromatic aberration between RGB as following:
3/)3( BRGT (1)
Where
T is the chromatic aberration.
R,G,B are the RGB value of the original image.
Choosing the appropriate threshold based on the analysis of histogram, we can get a binary image. But there were always some noise in the binary image, which affected the following processing. So the method of blob analysis was used to remove the noise, fig.3 showed the result after noise remove.
3
(a) (b)
Figure.2 Image Of Seedling Plants
(a) the seedlings 10 days after (b) The seedling 15 days after
(a) (b)
Figure.3 Image After Segmentation
(a) the seedlings 10 days after (b) The seedling 15 days after
Character capture The seedlings which are suitable for transplanting grow healthily, so the leaf area of them always larger than the leaves of sapless or un-germinated seeds. In this research, the area and the perimeter of each seedling in different cells were calculated.
We can see from Fig.3, it is easy to segment seedling plant when it is younger using the small rectangle. The area and the perimeter of each seedling can be calculated with the number of white pixels in the cell rectangle. But it would cause the identification failure when there were extruding leaves from neighboring cells. So we need a new algorithm to improve the problem. Watershed segmentation algorithm was applied in this study.
The concept of watersheds is based on visualizing an image in three dimensions, and its principal objective is to find the watershed lines. One of the principal application of watershed segmentation is in the extraction of nearly uniform objects from background. The principle of the segmentation algorithm was presented in following:
ntsgtsnT ),(|),(][ (2)
Where
][nT represents the set of coordinates ),( ts ;
4
),( yxg is the gray level value of a image;
Let )( iMC be a set denoting the coordinates of the points in the catchment basin associated
with regional minimum iM . )( in MC denote the set of coordinates of point in the catchment
basin associated with minimum iM that are flooded at stage n .
][)()( nTMCMC iin (3)
Next, ][nC denotes the union of the flooded catchment basins portions at stage n :
)(][1
in
R
iMCnC
(4)
)(]1[max1
i
R
iMCC
(5)
The algorithm for finding the watershed lines is initialized with ]1[min]1[min TC , and then
proceeds recursively, assuming at step n that ]1[ nC has been constructed.
The object segmented by watershed algorithm were shown as fig.4, the overlapping of the border seedling has been segmented. The catchment basins should be reconstructed in order to control over-segmentation of the image.
Figure 4 Watershed Segmentation
Blob analysis was used to calculate the sum of the area and perimeter of the blobs within each rectangle based on the position of the blob center. The problem of extruding leaves from neighboring cells was improved. Next, chose an appropriate threshold for area and perimeter, the cells with sapless seedlings or none could be selected. The position of the seedlings to be transplanted could be memorized.
RESULTS AND DISCUSSION The result of the identification based on the characters of the seedlings was shown in fig.5, the seedlings need to be transplanted were marked in the figure. 4 cell trays of 510 (15-20 days after) and 3 cell tray of 612 (15-20 days after) were used as the experimental samples. The identification of the seedlings was shown in the Table.1.
5
(a) (b)
Figure 5 The Result Of Identification
(a) The original image (b)The result of the image
Table 1 The Result Of the Identification
Tray type
Number of cells Number of error
Success rate
510 200 6 97%
612 216 7 96.8%
The identification error mainly appeared in the seedling grow after 20 days, because the leaves grow great bigger than the cells. The leaves almost overlapped together, so it is difficult to segment. However, this system has a good identification rate for the seedling grow after 15-20 days, and it has good adjustability for different type of trays.
CONCLUSIONSA machine vision system for transplanting was developed in this paper. The new algorithm of image processing was used for capture of seedling characters, and the success rate of identification was improved. Tomato seedlings in plug tray could be recognized, and the position of the seedling to be planted was detected successfully. This machine vision system could be an assistant of the transplanting robot.
The problem of superposition of leaves of different seedlings can be overcome, in principle, by applying several shape recognition algorithms using different patterns. This is a work for future development.
REFERENCESHwang H; Sistler F E. 1986. A robotic pepper transplanter. Applied Engineering in Agriculture, 2(1):2-5
L.J.Kutz. 1987. Robotic transplanting of bedding plants[J]. Transactions of the ASAE. 30(3):586-590
Ting K C; Giacomelli G A; Shen S J; KabalaWP . 1990. Robot workcell for transplanting of seedlings. Part I: Layout and materials flow. Transactions of the ASAE. 33(3):1005-1010
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K.C.Ting. 1990. Robot workcell for transplanting of seedlings. Part II: end-effector development[J]. Transactions of the ASAE. 33(3): 1013-1017
Tai.Y.W; Ling.P.P; Ting K C. 1994. Machine vision assisted robotic seedling transplanting. Transactions of the ASAE. 37(2): 661-667
K.H.Ryu; G.Kim; J.s.Han. 2001. Development of a Robotic Transplanter for Bedding Plants[J]. Aagric.Engng. Res. 78(2): 141-146
Brewer H L. 1994. Conceptual modeling automated seedling transfer from growing trays to shipping modules. Transactions of the ASAE, 37(4): 1043-1051
P.soille. 2000. Morphological image analysis applied to crop field mapping, image and vision computing 2000, 18:1025-1032
Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing. Second ed. Prentice-Hall, Reading. NJ. USA.
Onosaka, Takashi. 1996. Transplanter. Eurpean patent application. No. 0712569
Sylvester M. Tesch, Jr. 1993. seedling array transplanter. United states patent. No.5215550
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