Time-lapse of plant movement classification
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Date
2018-05
Authors
Azrul Zhafran Azreezul
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Abstract
Plant motions are commonly explored from perspectives of its responses to wind
and water. As plant motion is too slow to be observed quickly, the time-lapse technology
offers the solution. Previous studies have explored the movement of the plants by
applying the tree modelling and botanical simulation. The data mining concept to study
plant movements patterns is not adopted yet. However, not many papers reported the
plant movement patterns in response to external perturbations such as wind, heat, light
and water. Therefore, the goals of this study are to classify the plants responses by
perturbation: wind and water, differentiate the classes by plant type in response to wind
and water perturbations and compare the branches movement patterns towards wind or
water. An experiment was conducted on time-lapse captures on five types of potted
plants in response to wind and water. Six markers were placed on identified locations
of tree branches (top, middle and bottom) to enable the motion tracking. The videos
were translated into numeric data for which the changes in patterns of plants biomotion
will be quantitatively analysed using data mining approach. Stages involved include (i)
data preprocessing, (ii) classification (iii) knowledge discovery. Data preprocessing
techniques include normalize, standardize and remove potential outlier and extreme
value. The plants motion are grouped into its attribute classes: perturbation and plant
type based on Decision Tree and Lazy classifiers built-in Weka tool. Further analysis
was performed to examine the type of plant and location of markers that result in
misclassifications. Findings from this study show that 91.1745% classification
accuracies were retrieved on J48 classifier for perturbation while 78.8992% for type of
plants.