Barley Spectroscopy Data Analysis

Transcription

1 Barley Spectroscopy Data Analysis Dootika Vats September 26, 2014 Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

2 Summer Consulting Where: Department of Agronomy and Plant Genetics Who: Prof. Kevin Smith s Lab: Postdoc - Dr. Mohsen Mohammadi Graduate Student - Celeste Falcon Who else: Logan Sullivan What:? Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

3 What:? Measure protein content of different varieties of Barley from two different treatment patterns: 1 Low Nitrogen (Stressed Condition) 2 Normal Nitrogen Old Method: Measure protein from grains after harvest Wastes grains Slow process Manpower required Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

4 New Method Measure light reflectance at various (2048) wavelengths off of plants using JAZ spectrometer. Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

5 The Problem Goal: Identify what different wavelengths tell us about nitrogen use in different lines which wavelengths indicate variability between varieties of barley at which wavelengths the low nitrogen plants differ form the nitrogen plants how do current methods fit into this scenario Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

6 The Problem Goal: Identify what different wavelengths tell us about nitrogen use in different lines which wavelengths indicate variability between varieties of barley Not achieved at which wavelengths the low nitrogen plants differ form the nitrogen plants Not achieved how do current methods fit into this scenario Somewhat achieved Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

7 Definitions Barley: a member of the grass family, is a major cereal grain. Line: different varieties of barley Check Line: lines for which repeated measurements are collected Index: a function of reflectance at different wavelengths. For eg: IND = R 990 R 750 Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

8 The Data Spectrometer scan: 2048 wavelengths, 315 plots, 268 lines Some check lines repeated over 315 plots Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

9 The Data Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

10 The Data R = S D W D where: S = Raw Measured reflectance D = Reflectance from dark reference W = Reflectance from white reference R = Effective reflectance R measures the adjusted percentage of light reflected = 0 < R < 100 Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

11 Data Cleaning Irregular values after 900 and before 350 machine had highest efficiency(> 30%!) in nm chop off the ends! Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

12 Data Cleaning Data looks much better 2-3 areas where lines separate Around 460nm we get values below 0 Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

13 First Impressions Lines follow similar patterns, which is reassuring Distinguishable effect of day We notice wavelengths that separate the lines due to effect of lines? due to external factors? Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

14 Possible External Factors Data collected over 2 days: possible difference in days Time of collection: 9am - 4pm overheating of machine other environmental factors Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

15 Time Effect Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

16 Time Effect Some chunks of time have drifts in them. the drift might be due to baseline referencing, sensor overheating, sun angle How do we know effect is not due to line or gradual soil changes? Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

17 Remember Plot Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

18 Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

19 Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

20 Solution? Adjust for time in each block for a quadratic fit, at each wavelength. 1 Identify blocks of time. Do the following for each block 2 At a particular wavelength, fit the following regression: E (Time Effect w ) = β 1,w time + β 2,w time 2 3 Time-Adjusted Reflectance w = Reflectance w - E(Time Effect w ) 4 Repeat 2-3 for all wavelengths Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

21 Quadratic Fit Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

22 Time-Adjusted Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

23 Analysis We do not know what signals look like, and we know the noise is likely to be misunderstood as signal by different methods. Analysis tried: Preliminary PCA: Not interesting Preliminary Factor Analysis: Not interesting Binary classification of wavelengths as active or passive : Not interesting Clustering Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

24 Clustering Wavelengths separate from the rest, but that is expected from the noise. Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

25 Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

26 Current Indices We were interested in whether indices would be trustworthy in a real-world setting. Eg GNDVI = R 780 R 550 R R 550 Three check lines are known to differ in protein content: KLBC Gen2 and Lacey. We plot 12 indices over these three lines. Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

27 Current Indices Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

28 Conclusion Efficiency range of machine is important Use of check lines is crucial Possible time effect on reflectance, especially for certain wavelengths What we think is time effect, could be a machine heating problem! Current indices might not be trustworthy in a field setting Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

29 Future Suggestions Data Measurement: take measurements in a short time around solar noon balance white and dark referencing as often as possible (ideally, after every plot scan) design data collection so as to have multiple observations from various days and time record the presence of unusual conditions, like cloud cover, airborne dust etc. Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

30 Future Suggestions Drones a lot of time/date/drift/heating effects might be taken care of might introduce new external effects, like height of drone, airborne dust etc. Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31

31 Questions? Thankful to Prof. Kevin Smith, Dr. Mohsen Mohammadi, Celeste Falcon, Logan Sullivan, and Sijia Liang. Questions? Dootika Vats Barley Spectroscopy Data Analysis September 26, / 31