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Processing of Mandarin Leaf Multispectral Re fl ectance Data for the Retrieval of

Processing of Mandarin Leaf Multispectral Re fl ectance Data for the Retrieval of Leaf Water Potential Information. Janos Kriston-Vizi PhD Kyoto University. Acknowledgement. Professor Mikio Umeda Kyoto University, Laboratory of Filed robotics and Precision Agriculture.

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Processing of Mandarin Leaf Multispectral Re fl ectance Data for the Retrieval of

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  1. Processing of Mandarin Leaf MultispectralReflectance Data for the Retrieval of LeafWater Potential Information JanosKriston-Vizi PhD Kyoto University

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

  3. 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 source: Yakushiji, H. et al. (1996):

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

  5. Sugar and Acid Content Change due to Water Stress– Experimental Orchard

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

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

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

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

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

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

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

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

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

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

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

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

  18. Rank experiments by box and whiskers plot - 2003

  19. Rank experiments by box and whiskers plot - 2006

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

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

  22. Linear regression results: equations - 2005

  23. Linear regression results: plots – 2005 LWP = -0.02 • (-0.2)G refl. Multiple R2: 0.51 p = 1.15e-08 LWP = -0.71 • (-0.17)R refl. Multiple R2: 0.53 p = 3.76e-09

  24. Linear regression results: plots – 2003 peach peach LWP = 0.19• (- 21.02 )G refl. Multiple R2: 0.63

  25. Linear regression results: plots - 2002 LWP = -0.19• (- 21.02 )G refl. Multiple R2: 0.29

  26. Linear regression results: plots – 2002 LWP = -0.45• (- 29.15 )R refl. Multiple R2: 0.28

  27. Whole Mandarin Orchard Image Segmentation – manual 2005. 09. 29. 10h 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 2005. 09. 29. 10h Need large (6-8 rows) area to detect temperature difference. 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

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

  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. Author’s Bio • 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

  35. Thank you for your attention

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