1. Processing of Mandarin Leaf Multispectral Reflectance Data for the Retrieval of Leaf Water Potential Information Janos Kriston-Vizi PhD Kyoto University

2. Acknowledgement This research was conducted by financial support of Japanese Society for Promotion of Science (JSPS). Dr. Kumi Miyamoto senior researcher Wakayama Research Center of Agriculture, Forestry and Fisheries Fruit Tree Experiment Station Professor Mikio Umeda Kyoto University, Laboratory of Filed robotics and Precision Agriculture

3. source: Yakushiji, H. et al. (1996): 1. Water stress induce sugar accumulation in mandarin fruit 2. Mulching induce water stress 3. Japanese mandarin farmer: „Leaf reflectance indicates water stress”… Physical and Physiological Background

4. Sugar and Acid Content Change due to Water Stress – Japanese Local Growers Sugar content [degrees Brix] Acid content [%] Orchard properties Place Variety

5. Sugar and Acid Content Change due to Water Stress – Experimental Orchard °BrixAcid [%] Control tree 1.11.10.8 Control tree 2.11.10.7 Control tree 3.11.10.7 Mulched tree 1.13.31.1 Mulched tree 2.13.11.3 Mulched tree 3.13.61.3

6. Satsuma Mandarin (Citrus unshiu Marc. var. Satsuma) rootstock and variety: Miyagawa Wase Mulch: plastic cover with DuPont Tyvek Wakayama Research Center of Agriculture, Fruit Tree Experiment Station (near Osaka) Experimental Field Data Collection Equipments Silvacam multispectral digital video camera 490-580 nm Green 580-680 nm Red 760-900 nm NIR Pressure Chamber made by Pms Instrument Company, Model 600

7. GNU/Linux capture and non-linear DV editor software

8. 1. Capture data from MiniDV to.dv file 2. Export.dv file to.png image sequence Capture and export process

9. Capture by Kino video: 1_53s_mpeg1_Kino_demo_xvidcap_screen-video_capture_HDV.mpeg

10. NIRRG 760 - 900 nm 490 - 580 nm 580 – 680 nm Silvacam false color image and bands

11. http://rsb.info.nih.gov/ij/index.html Advantages: customizable, open source code many algorithms available free Linux image processing program

12. 2. Customized java script for SegmentingAssistant plugin to be able to segment image sequence 1. Customizable, free software SegmentingAssisstant plugin

13. 1. Setting segmenting parameters for image sequence 2. Automatically segmenting image sequence Segmentation workflow

14. Automatized segmenting process video: 2_10s_mpeg1_ImageJ_SegmentingAssisstant_XVidCap_screenshot_video_2005-11- 24_coT2L1.mpg

15. Result file after analyzing an image sequence NIR frame 1R G NIR frame 2R G etc.

16. Python script to format ImageJ output file and preprocessing for statistical analysis: calculate abs. reflectance

17. 1. Boxplot for initial comparison Statistical analysis 2. Histogram, Kernel Density Estimates and Stem-and-leaf chart to find outliers

18. Rank experiments by box and whiskers plot - 2003

19. Rank experiments by box and whiskers plot - 2006

20. G refl. – 490-580 nm R refl. – 580-680 nm A – assume equal variances B – assume non-equal variances Reflectance of mulched leaves are higher than reflectance of control leaves. Significance Testing – Reflectance Difference between control and mulched leaves - 2003

21. Significance Testing – Reflectance Difference between control and mulched leaves - 2005

22. Linear regression results: equations - 2005

23. LWP = -0.02 (-0.2)G refl. Multiple R 2 : 0.51 p = 1.15e-08 LWP = -0.71 (-0.17)R refl. Multiple R 2 : 0.53 p = 3.76e-09 Linear regression results: plots – 2005

24. Linear regression results: plots – 2003 peach LWP = 0.19 ( - 21.02 )G refl. Multiple R 2 : 0.63

25. Linear regression results: plots - 2002 LWP = - 0.19 ( - 21.02 )G refl. Multiple R 2 : 0.29

26. Linear regression results: plots – 2002 LWP = - 0.45 ( - 29.15 )R refl. Multiple R 2 : 0.28

27. Whole Mandarin Orchard Image Segmentation – manual 2005. 09. 29. 10 h Manual segmentation by ImageJ: Green channel, threshold intensity for ROI pixels = 30-70

28. Whole Mandarin Orchard Image Segmentation – automatic 4 class k-means canopy segmentation of multispectral orchard image

29. Infrared thermography Objective: Find optimal conditions to detect water stress by infrared thermography. Hardware tool: Avio Nippon Avionics, Neo Thermo TVS-600

30. Thermal image on whole mandarin orchard image - 2005 LWP difference between mulched and control area: Mulched area: -2.552 MPa Control area: -2.071 MPa Mean difference: 0.481 MPa Temperature difference between mulched and control area: Mulched area: 29.2 °C (mean) Control area: 26.4 °C (mean) Mean difference: 2.8 °C 2005. 09. 29. 10 h Need large (6-8 rows) area to detect temperature difference.

31. Thermal image on whole mandarin orchard image - 2006 Temperature difference between mulched and control area: Mulched area: 28.9 °C (mean) Control area: 26.8 °C (mean) Mean difference: 2.1 °C 2006. 09. 27. 11:15 h

32. Current work and near future research plan Hyperspectral reflectance Objective: Find optimal bandwith at visible range to detect LWP, that narrower than R,G Hardware tool: Specim Imspector with Hamamatsu camera (400-1000 nm)

33. Current work and near future research plan Severe water stress effect on peach leaves’s reflectance at visible spectral range. LWPs mu: -4.0 MPa co: -0.9 MPa

34. PhD: 2005 (age of 29) Hungary, Corvinus University of Budapest Crop Sciences and Horticulture Research: 2002 - Present Kyoto University, Japan Precision Agriculture Mandarin Water Stress Author’s Bio

35. Thank you for your attention