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

8. Morphological transformation Dilation –A ⊕ B={c  E N | c=a+b for some a  A and b  B} Erosion –AΘB={x  E N | 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 transform for ellipses –Focusing

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

12. 512x512 128x128 64x64 32x32 ellipse 128x12864x64

13. RESULTS

15. Relationship between actual leaf and ellipse Area Amaranth area (cm 2 )=1.1132*Ellipse area (cm 2 )+0.0613 (R 2 =0.954) Cabbagearea (cm 2 )=1.1158*Ellipse area (cm 2 )-0.6975 (R 2 =0.985) Chinese cabbagearea (cm 2 )=1.1386*Ellipse area (cm 2 )-0.5421 (R 2 =0.953) Broccoliarea (cm 2 )=1.0674*Ellipse area (cm 2 )-0.068 (R 2 =0.974)

16. Relationship between actual leaf and ellipse Perimeter Amaranth perimeter (cm)=1.0977*Ellipse perimeter (cm)+1.1233 (R 2 =0.954) Cabbageperimeter (cm)=1.2679*Ellipse perimeter (cm)-0.523 (R 2 =0.985) Chinese cabbageperimeter (cm)=1.1761*Ellipse perimeter (cm)-0.5421 (R 2 =0.953) Broccoliperimeter (cm)=1.2282*Ellipse perimeter (cm)-0.1998 (R 2 =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[16log 2 N-11]+[1-  (t)]24[2 t (log 2 N-t)-log 2 N]