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

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

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 °BrixAcid [%] Control tree Control tree Control tree Mulched tree Mulched tree Mulched tree

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 nm Green nm Red 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

NIRRG nm nm 580 – 680 nm Silvacam false color image and bands

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_ _coT2L1.mpg

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

Rank experiments by box and whiskers plot

G refl. – nm R refl. – 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

Significance Testing – Reflectance Difference between control and mulched leaves

Linear regression results: equations

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

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

Linear regression results: plots LWP = ( )G refl. Multiple R 2 : 0.29

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

Whole Mandarin Orchard Image Segmentation – manual h 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 LWP difference between mulched and control area: Mulched area: MPa Control area: MPa Mean difference: 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 h Need large (6-8 rows) area to detect temperature difference.

Thermal image on whole mandarin orchard image Temperature difference between mulched and control area: Mulched area: 28.9 °C (mean) Control area: 26.8 °C (mean) Mean difference: 2.1 °C :15 h

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 ( 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: Present Kyoto University, Japan Precision Agriculture Mandarin Water Stress Author’s Bio

Thank you for your attention