Non destructive growth measurement of selected vegetable seedlings using machine vision
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NON-DESTRUCTIVE GROWTH MEASUREMENT OF SELECTED VEGETABLE SEEDLINGS USING MACHINE VISION. Ta-Te Lin, Sheng-Fu Cheng, Tzu-Hsiu Lin, Meng-Ru Tsai Department of Agricultural Machinery Engineering, National Taiwan University, Taipei, Taiwan, ROC. INTRODUCTION.

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Non destructive growth measurement of selected vegetable seedlings using machine vision

NON-DESTRUCTIVE GROWTH MEASUREMENT OF SELECTED VEGETABLE SEEDLINGS USING MACHINE VISION

Ta-Te Lin, Sheng-Fu Cheng, Tzu-Hsiu Lin, Meng-Ru Tsai

Department of Agricultural Machinery Engineering,

National Taiwan University,

Taipei, Taiwan, ROC


Introduction
INTRODUCTION SEEDLINGS USING MACHINE VISION

  • Plant growth measurement and modeling

  • Machine vision technique

  • Seedling characteristics

  • Applications in production management


Objectives
OBJECTIVES SEEDLINGS USING MACHINE VISION

  • Image processing algorithm development

  • Growth measurements of selected vegetable seedlings

  • Model parameter determination and simulations


System implementation
SYSTEM IMPLEMENTATION SEEDLINGS USING MACHINE VISION


SEEDLING CHARACTERISTICS SEEDLINGS USING MACHINE VISION

  • Stem length

  • Height

  • Span

  • Total leaf area

  • Top fresh weight

  • Top dry weight

  • Number of leaves


Image processing algorithm
IMAGE PROCESSING ALGORITHM SEEDLINGS USING MACHINE VISION


Result of node tracing
RESULT OF NODE TRACING SEEDLINGS USING MACHINE VISION


Result of node tracing1
RESULT OF NODE TRACING SEEDLINGS USING MACHINE VISION











Comparison between manually measured top fresh weight and that determined by the automatic measurement system.


Comparison between manually measured total leaf area and that determined by the automatic measurement system.


Comparison between manually measured top fresh weight and that determined by the automatic measurement system.



Kale seedlings images from different angles (images are not of the same scale)


Top fresh weight of kale seedlings growing under 25/20 (images are not of the same scale) C. Each curve indicates individual seedling.



Average plant top fresh weight of kale seedlings grown under five different day/night temperatures.




PLANT GROWTH MODELS different day/night temperatures.

  • LOGISTIC MODEL

Y = Y0 / [ Y0 + ( 1 -  Y0 ) e-m t]

t : Time

Y : Plant characteristics

 : Growth constant

 : Reciprocal of Y when t = 

Y0 : Y at time = 0


PLANT GROWTH MODELS different day/night temperatures.

  • RICHARDS MODEL

Y = Y0 / { ( Y0) + [ 1 - ( Y0 )] e-m t }1/

t : Time

Y : Plant characteristics

 : Growth constant

 : Reciprocal of Y when t = 

Y0 : Y at time = 0

 : For logistic model,  =1


Comparison of regression curves to the experimental data. Top fresh weight of cabbage seedlings growing under various day/night temperatures was used as an example.


GROWTH MODEL PARAMETERS Top fresh weight of cabbage seedlings growing under various day/night temperatures was used as an example.


Growth model parameters
GROWTH MODEL PARAMETERS Top fresh weight of cabbage seedlings growing under various day/night temperatures was used as an example.


RELATIVE GROWTH RATE, RGR Top fresh weight of cabbage seedlings growing under various day/night temperatures was used as an example.

  • LOGISTIC MODEL

  • RICHARDS MODEL


Predicted relative growth rate of cabbage seedling growing under 5 different day/night temperatures using the logistic model.


Comparison of calculated top fresh weight of cabbage, amaranth and kale seedlings growing at 25/200C.


Comparison of calculated relative growth rate (RGR) of cabbage, amaranth and kale seedlings growing at 25/200C.


SEEDLING 3-D RECONSTRUCTION cabbage, amaranth and kale seedlings growing at 25/20

  • ARTIFICIAL WIRE MODEL


SEEDLING 3-D RECONSTRUCTION cabbage, amaranth and kale seedlings growing at 25/20

  • CABBAGE SEEDLING


Conclusions
CONCLUSIONS cabbage, amaranth and kale seedlings growing at 25/20

  • A non-destructive machine vision system was successfully developed for the measurement of vegetable seedling characteristics. A new algorithm for the determination of seedling nodes was implemented.

  • 3-dimension reconstruction of seedling architecture can be achieved with the nodal coordinates determined with the machine vision system.

  • Growth responses of cabbage, kale and amaranth seedlings under various temperature conditions were measured and compared.

  • The dynamic growth responses of selected vegetable seedlings were analyzed with logistic and Richards growth model and the relative growth rates of the seedlings under various conditions were calculated.


FUTURE DEVELOPMENT cabbage, amaranth and kale seedlings growing at 25/20

  • Measurement under natural lighting

  • Leaf area index (LAI) determination

  • Extraction of information from serial images

  • Modification of the current growth model

  • Application of geometrical modeling in seedling 3D reconstruction


THANK YOU cabbage, amaranth and kale seedlings growing at 25/20

謝 謝


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