1. VEGETABLE SEEDLING FEATURE EXTRACTION USING STEREO COLOR IMAGING Ta-Te Lin, Jeng-Ming Chang Department of Agricultural Machinery Engineering, National Taiwan University, Taipei, Taiwan, ROC

2. INTRODUCTION n Plant growth measurement and modeling n Machine vision technique n Seedling characteristics n Applications in production management

3. OBJECTIVES n Implementation of stereo machine vision system n Development of image segmentation algorithm n Development of seedling feature extraction algorithm 3D reconstruction of seedling structure and graphical representation 3D reconstruction of seedling structure and graphical representation

4. SYSTEM IMPLEMENTATION Rotary stage Image processing board RS-232 interface Rotary stage CCD Camera RS-232 interface Image processing board

5. IMAGE PROCESSING ALGORITHM

6. BASIC SETUP PROCEDURES Acquisition of top-view and front-view images of the seedling Acquisition of top-view and front-view images of the seedling Rotate stage 90º to obtain side-view image of the seedling Rotate stage 90º to obtain side-view image of the seedling Geometric calibration of the three acquire images Geometric calibration of the three acquire images Training and testing of image segmentation using neural network Training and testing of image segmentation using neural network

7. FEATURE EXTRACTION AND MEASUREMENT PROCEDURES Seedling image segmentation using neural network Image registration to find the main stem (central point) position Image registration to find the main stem (central point) position Determination of leaf number and axial direction of each leaf Image acquisition at each corresponding axial direction Calculation of leaf area, nodal coordinates and other features 3D reconstruction and graphic simulation of the seedling

8. IMAGE SEGMENTATION

9. R G B Input layer Hidden layer Output layer Background Foreground

10. R G B 輸入層隱藏層 輸出層 背景 前景物件

11. IMAGE SEGMENTATION

12. IMAGE REGISTRATION A B C D

13. Angle (degree) LEAF NUMBER AND AXIAL DIRECTION DETERMINATION

14. LEAF NUMBER AND AXIAL DIRECTION DETERMINATION

15. Seedling height height Petiole Stem length to petiole Petiole angle Leaf stalk length Leaf width Leaf length Internodal length Seedling span Projection area Schematic of seedling features determined with the automatic machine vision system

16. SEEDLING CHARACTERISTICS n Stem length n Height n Span n Total leaf area n Top fresh weight n Top dry weight n Number of leaves n Leaf area index, LAI n Leaf length n Leaf width

17. GEOMETRIC CALCULATION OF THE TOTAL LEAF AREA

18. TRACING THE LEAF EDGE TO DETERMINE THE LEAF ANGLE

19. Comparison of predicted total leaf area to the actural total leaf area (cabbage seedlings).

20. Comparison of predicted total leaf area to the actural total leaf area (Chinese mustard seedlings).

21. A B 3D RECONSTRUCTION OF SEEDLING STRUCTURE

23. Serial images of Chinese mustard seedlings at various growth stages. (images are not of the same scale)

24. CALIBRATION FOR FRESH WEIGHT, DRY WEIGHT AND LEAF AREA Side-view projected area Top-view projected area Combined projected area Calculated total leaf area Top fresh weight Top dry weight Total leaf area Calibration function

25. Top fresh weight of Chinese mustard seedlings growing under artificial lighting/pot treatment.

26. Averaged top fresh weight of Chinese mustard seedlings growing under four different treatments.

27. CONCLUSIONS n A non-destructive machine vision system based on stereo color imaging was successfully developed for the measurement of vegetable seedlings. n The seedling image segmentation was based on a back-propagation neural network that allowed for robust segmentation of seedling from background under natural lighting conditions. n The registration and mapping of coordinates from top-view and lateral images allowed for the determination of central point and stem location of the seedling. Based on this information, seedling leaf number and axial directions can be determined.

28. n Image acquisition based on the information of leaf axial directions provided better accuracy in extracting the seedling features. n The leaf area of seedling can be predicted based on the projection leaf area and leaf angle with satisfactory accuracy. n The measured nodal and stem coordinates allowed for 3D reconstruction of the vegetable seedling for graphic display. CONCLUSIONS

29. THANK YOU 謝 謝 謝 謝