Non-destructive Measurement of Vegetable Seedling Leaf Area using Elliptical Hough Transform

1. Non-destructive Measurement of Vegetable Seedling Leaf Area using Elliptical Hough Transform Chung-Fang Chien, Ta-Te Lin Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan, ROC

2. INTRODUCTION • Traditionally • measuring dry weight, fresh weight, plant height and coverage to represent plant growing stages • destructive and laborious

3. OBJECTIVES • Non-destructive • Fast and easy • Image processing • To count the seedling leaf number • To measure and estimate the leaf dimension (area and perimeter)

4. MATERIALS AND METHODS • Materials Cabbage, Chinese cabbage, Broccoli Growing at 25℃(day) / 20℃(night) 10 to 30 days after seeding 1 to 4 leaves

5. MATERIALS AND METHODS • Methods Hough transform for ellipses Focusing Morphological transformation

6. Hough transform for ellipses • 5-dimensional parameter space • Only the object pixels • Vote for thresholding

7. Focusing Lower resolutions and backmapping • Lower resolutions 512x512→32x32 • Search for ellipses • Backmapping : gradually increase the resolutions and shake the ellipse

8. Morphological transformation • Dilation • A⊕B={cEN| c=a+b for some aA and bB} • Erosion • AΘB={xEN| x+bA for every bB}

9. Procedures • Data preprocessing • Image segmentation • Place white paper on soil • Manual threshold • Morphological transformation • Edge detection • Thinning • Hough transformfor ellipses • Focusing

10. Procedures • Original • Threshold + Dilation + Erosion • Edge detection + Thinning • Hough transform for ellipse + Focusing

12. 512x512 128x128 64x64 32x32 ellipse 128x128 64x64

13. RESULTS

15. Relationship between actual leaf and ellipse • Area • Amaranth area (cm2)=1.1132*Ellipse area (cm2)+0.0613 (R2=0.954) • Cabbage area (cm2)=1.1158*Ellipse area (cm2)-0.6975 (R2=0.985) • Chinese cabbage area (cm2)=1.1386*Ellipse area (cm2)-0.5421 (R2=0.953) • Broccoli area (cm2)=1.0674*Ellipse area (cm2)-0.068 (R2=0.974)

16. Relationship between actual leaf and ellipse • Perimeter • Amaranth perimeter (cm)=1.0977*Ellipse perimeter (cm)+1.1233 (R2=0.954) • Cabbage perimeter (cm)=1.2679*Ellipse perimeter (cm)-0.523 (R2=0.985) • Chinese cabbage perimeter (cm)=1.1761*Ellipse perimeter (cm)-0.5421 (R2=0.953) • Broccoli perimeter (cm)=1.2282*Ellipse perimeter (cm)-0.1998 (R2=0.974)

17. Axial occlusion

18. Radial occlusion

23. Broccoli leaf number estimation error rate

26. CONCLUSIONS • An image processing algorithm using elliptical Hough transform is developed to locate seedling leaves and to estimate leaf area. • All regressions are highly correlated between leaf and ellipse area and perimeter. • Error rate is less than 20% when the occlusion ratio is under 40% between the actual and predicted value.

27. CONCLUSIONS • When very small object is observed, the initial processing resolutions should be increased. • The accuracy to predict the leaf number from seedling top-view image is above 75%. • Though the seedling actual leaf area and perimeter are not the same as the predicted value, the relationships are highly correlated.

28. Thank you very much !!

29. Shake ellipse

30. Focusing algorithm • An image size of NxN • the computational complexity C=P[16log2N-11]+[1-(t)]24[2t(log2N-t)-log2N]