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Chlorophyll Estimation Using Multi-spectral Reflectance and Height Sensing C. L. JonesResearch Engineer N. O. Maness Professor M. L. Stone Regents’ Professor.

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Presentation on theme: "Chlorophyll Estimation Using Multi-spectral Reflectance and Height Sensing C. L. JonesResearch Engineer N. O. Maness Professor M. L. Stone Regents’ Professor."— Presentation transcript:

1 Chlorophyll Estimation Using Multi-spectral Reflectance and Height Sensing C. L. JonesResearch Engineer N. O. Maness Professor M. L. Stone Regents’ Professor R. Jayasekara Research Engineer 2004 ASAE/CSAE Annual International Meeting Paper Number:

2 Objectives Select “best” wavelength or indices for estimating chlorophyll content and concentration in spinach Non-destructively quantify chlorophyll content and concentration by combining biomass estimates with multi-spectral imaging system reflectance data. Assess the feasibility of reflectance-based sensing in estimating chlorophyll concentration in spinach Paper Number:

3 Let’s Talk Chlorophyll… (Content vs. Concentration) Content: related to nitrogen concentration in green vegetation Chlorophyll mass per unit ground surface area or per plant (mg/ha or mg/plant) Indicator of photosynthetic capacity: productivity of the plant canopy Paper Number:

4 Let’s Talk Chlorophyll… (Content vs. Concentration) Concentration: estimate of pigment concentration – chlorophyll a and b Chlorophyll mass per unit mass of plant material (mg/kg) Indicator of plant physiological status: level of stress Paper Number:

5 Previous Work No single spectral approach offers an adequate method of estimating pigment (chlorophyll) concentration in all plants. Paper Number:

6 Experimental Plan Consider spinach plants at the single plant level Use chlorophyll content estimates to determine chlorophyll concentration (implies a need for biomass estimate) C content (mg) = C concentration (mg/kg) x biomass (kg) Use ultrasound and digital imagery for biomass estimate Paper Number:

7 Experimental Plan Estimators used for chlorophyll content investigation: –R green : Reflectance at 550 nm ±10 nm –R red : Reflectance at 670 nm ±10 nm –R NIR : Reflectance at 780 nm ±10 nm –NIR/RED : Ratio of reflectance at 780 and 670 nm ±10 nm –NIR/GREEN : Ratio of reflectance at 780 and 550 ±10 nm –NDVI 670 = (R 780 – R 670 )/(R R 670 ) –NDVI 550 = (R 780 – R 550 )/(R R 550 ) Paper Number:

8 Plant Samples Two flats- no fertilizer Two flats adequate fertilizer Two flats 2x adequate fertilizer amount Provided varying biomass and chlorophyll levels Sampled at six weeks post plant Paper Number:

9 Data Gathering Overview Plants removed from flats Individually placed on turntable (0.91 m diameter) 1.04 m below ultrasonic distance sensor (UDS) for height estimation Turntable rotating at 1.06 rpm Paper Number:

10 Data Gathering Overview Plants placed under multispectral camera mounted on tripod for reflectance data and top view surface area estimate Vegetative portion harvested, weighed, and packed on ice for transport to lab Chlorophyll analysis per Inskeep and Bloom (1985) Paper Number:

11 Ultrasonic Distance Sensor (UDS) Senix™ Ultra-SPA. (Senix, Bristol, Virginia) 50 kHz, piezoelectric 12 degree angle view, signal > 3db Accuracy: better than 1% of target distance Laptop computer SoftSpan © software Paper Number:

12 UDS Response Paper Number:

13 Multispectral Camera DuncanTech MS , 670, 780 nm ±10 nm FWHM Resolution: 1392 x 1040 x 3 sensors Pixel size: 4.65 x 4.65 microns 14mm focal length lens RS232 interface with National Instrument’s PCI-1424 frame grabber Dolch ruggedized laptop Paper Number:

14 Multispectral Camera Mounted on tripod 1 m above target Target illuminated with incandescent lighting Labsphere Reflectance Calibration Standard targets: 10%, 50%, 75%, and 99% (Weckler, et al. 2002) Paper Number:

15 Multispectral Camera Images processed with Matlab © NIR, green, and red reflectance pixel values identified > 0 : plant material, < 0 : background, nonplant material, NDVI averaged Image binarized “bwarea” command to calculate vegetation area Paper Number:

16 Biomass Estimation PWC = 0.86 Paper Number: W EST = A MS x H UDS Where: W EST = estimated plant biomass A MS = surface area estimate from camera (pixels) H UDS = estimated plant height from UDS (cm)

17 Biomass Estimation PWC = 0.86 Paper Number:

18 Chlorophyll Content Estimation, r 2 Paper Number: EstimatorC content vs. estimator R green 0.21 R red 0.10 R NIR 0.58 NIR/RED 0.15 NIR/GREEN 0.05 NDVI NDVI

19 Chlorophyll Content Estimation, r 2 Paper Number: EstimatorC content vs. estimator C content vs. estimator * W EST R green R red R NIR NIR/RED NIR/GREEN NDVI NDVI

20 Chlorophyll Concentration Estimation Paper Number: Estimated chlorophyll content calculated from regression equations for each band or ratio C CONC = C CONT / W EST Compared to actual Chlorophyll concentration

21 Chlorophyll Concentration Estimation, r 2 Paper Number: EstimatorC content vs. estimator C content vs. estimator * W EST Actual C conc vs. C conc estimate R green R red R NIR NIR/RED NIR/GREEN NDVI NDVI

22 Conclusions Of the indices tested in this study, NDVI 670 multiplied by estimated biomass appears to provide best estimate of chlorophyll content in spinach Multiplying reflectance ratios and indices by estimated biomass improved chlorophyll content estimations Reflectance-based remote sensing may not be the best method for estimating plant pigment concentrations (chl. a and b): R RED best, r 2 = 0.30 Paper Number:

23 QUESTIONS? Acknowledgments Support through funding from the USDA Special Research Grant Number Paper Number:


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