Processing of Mandarin Leaf Multispectral Reflectance Data for the Retrieval of Leaf Water Potential...
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Transcript of Processing of Mandarin Leaf Multispectral Reflectance Data for the Retrieval of Leaf Water Potential...
Processing of Mandarin Leaf Multispectral Reflectance Data
for the Retrieval of Leaf Water Potential Information
Janos Kriston-Vizi PhDKyoto University
Acknowledgement
This research was conducted by financial support of Japanese Society for Promotion of Science (JSPS).
Dr. Kumi Miyamoto senior researcherWakayama Research Center of Agriculture, Forestry and FisheriesFruit Tree Experiment Station
Professor Mikio UmedaKyoto University, Laboratory of Filed robotics and Precision Agriculture
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
Sugar and Acid Content Change due to Water Stress – Japanese Local Growers
Sugar content [degrees Brix]
Acid content [%]
Orchard properties
Place
Variety
Sugar and Acid Content Change due to Water Stress – Experimental Orchard
°Brix Acid [%]
Control tree 1. 11.1 0.8
Control tree 2. 11.1 0.7
Control tree 3. 11.1 0.7
Mulched tree 1. 13.3 1.1
Mulched tree 2. 13.1 1.3
Mulched tree 3. 13.6 1.3
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 FieldData Collection Equipments
Silvacam multispectral digital video camera
490-580 nm Green580-680 nm Red760-900 nm NIR
Pressure Chamber made by Pms Instrument Company, Model 600
GNU/Linux capture and non-linear DV editor software
1. Capture data from MiniDV to .dv file
2. Export .dv file to .png image sequence
Capture and export process
Capture by Kino video: 1_53s_mpeg1_Kino_demo_xvidcap_screen-video_capture_HDV.mpeg
NIR R G
760 - 9
00 n
m
490 - 5
80 n
m
580 – 680 nm
Silvacam false color image and bands
http://rsb.info.nih.gov/ij/index.html
Advantages:
customizable, open source
code many algorithms available free
Linux image processing program
2. Customized java script for SegmentingAssistant plugin to be able to segment image sequence
1. Customizable, free software
SegmentingAssisstant plugin
1. Setting segmenting parameters for image sequence
2. Automatically segmenting image sequence
Segmentation workflow
Automatized segmenting process video:2_10s_mpeg1_ImageJ_SegmentingAssisstant_XVidCap_screenshot_video_2005-11-24_coT2L1.mpg
Result file after analyzing an image sequence
NIRframe 1 R
GNIR
frame 2 RG
etc.
Python script to format ImageJ output file and preprocessing for statistical analysis: calculate abs. reflectance
1. Boxplot for initial comparison
Statistical analysis
2. Histogram, Kernel Density Estimates and Stem-and-leaf chart to find outliers
Rank experiments by box and whiskers plot - 2003
Rank experiments by box and whiskers plot - 2006
F p-value t df p-value Mean Diff. Diff.St.Err.2003. VIII. peach A 2,218 0,143 -6,543 46 0.000 -3,12 0,11 G refl. [%] B -8,091 29,657 0.000 -3,12 0,072003. VIII. peach A 0,648 0,425 -1,865 46 0,069 -1,17 0,47 R refl. [%] B -2,134 24,636 0,043 -1,17 0,382003. VIII. mandarin A 13,055 0,001 -4,367 44 0.000 -2,49 0,57 G refl. [%] B -3,125 12,656 0,008 -2,49 0,792002. XI. mandarin A 0,863 0,356 -1,864 62 0,067 -1,48 0,79 G refl. [%] B -1,597 20,644 0,125 -1,48 0,92
Levene-test Significance testing
G refl. – 490-580 nmR refl. – 580-680 nm
A – assume equal variancesB – 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
Mean differenceIX.27. peach LWP 1.96 MPaIX.27. peach G refl. -4.66%IX.27. peach R refl. -4.42%IX.28. mandarin LWP 0.35 MPaIX.28. mandarin G refl. -2.60%IX.28. mandarin R refl. -1.55%IX.29. mandarin LWP 0.48 MPaIX.29. mandarin G refl. t-test not passedIX.29. mandarin R refl. -2.32%IX.30. peach LWP 0.91 MPaIX.30. peach G refl. t-test not passedIX.30. peach R refl. -1.91%XI.24. mandarin LWP 1.04 MPaXI.24. mandarin G refl. -3.39%XI.24. mandarin R refl. -4.02%XI.25. mandarin LWP 0.99 MPaXI.25. mandarin G refl. -1.83%XI.25. mandarin R refl. -1.52%
Significance Testing – Reflectance Difference between control and mulched leaves - 2005
formula: LWP=IX.27. peach G refl.-LWP 0.29*(-0.26) G refl. 0.41IX.27. peach R refl.-LWP -0.79*(-0.23)R refl. 0.33IX.28. mandarin G refl.-LWP -0.43*(-0.09)G refl. 0.35IX.28. mandarin R refl.-LWP -0.79*(-0.12)R refl. 0.40IX.29. mandarin G refl.-LWP t-test not passed -IX.29. mandarin R refl.-LWP -1.7*(-0.10)R refl. 0.40IX.30. mandarin G refl.-LWP t-test not passed -IX.30. mandarin R refl.-LWP -0.98*(-0.20)R refl. 0.22XI.24. mandarin G refl.-LWP -0.02*(-0.20)G refl. 0.51XI.24. mandarin R refl.-LWP -0.71*(-0.17)R refl. 0.45XI.25. mandarin G refl.-LWP -0.53*(-0.14)G refl. 0.17XI.25. mandarin R refl.-LWP -1.22*(-0.096)R refl. 0.12
R2
Linear regression results: equations - 2005
LWP = -0.02 • (-0.2)G refl.Multiple R2: 0.51p = 1.15e-08
LWP = -0.71 • (-0.17)R refl.Multiple R2: 0.53p = 3.76e-09
Linear regression results: plots – 2005
Linear regression results: plots – 2003peach
peachLWP = 0.19 • ( - 21.02 )G refl.Multiple R2: 0.63
2002. XI mandarin G-LWP
.18.16.14.12.10.08.06.04.02
-.5
-1.0
-1.5
-2.0
-2.5
-3.0
-3.5
-4.0
-4.5
várható
gyakoriságok
megfigyelt
gyakoriságok
Linear regression results: plots - 2002
LWP = - 0.19 • ( - 21.02 )G refl.Multiple R2: 0.29
2002. XI mandarin R-LWP
.10.09.08.07.06.05.04.03.02
-.5
-1.0
-1.5
-2.0
-2.5
-3.0
-3.5
-4.0
-4.5
várható
gyakoriságok
megfigyelt
gyakoriságok
Linear regression results: plots – 2002
LWP = - 0.45 • ( - 29.15 )R refl.Multiple R2: 0.28
Whole Mandarin Orchard Image Segmentation – manual
2005. 09. 29. 10h
Manual segmentation by ImageJ:
Green channel,threshold intensity for ROI pixels = 30-70
Whole Mandarin Orchard Image Segmentation – automatic
4 class k-means canopy segmentation of multispectral orchard image
Infrared thermography
Objective: Find optimal conditions to detect water stress by infrared thermography.Hardware tool: Avio Nippon Avionics, Neo Thermo TVS-600
Thermal image on whole mandarin orchard image - 2005
LWP difference between mulched and control area: Mulched area: -2.552 MPa Control area: -2.071 MPaMean 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. 10h
Need large (6-8 rows) area to detect temperature difference.
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. 11:15h
Current work and near future research plan
Hyperspectral reflectanceObjective: Find optimal bandwith at visible range to detect LWP, that narrower than R,GHardware tool: Specim Imspector with Hamamatsu camera (400-1000 nm)
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
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
Thank you for your attention