Modular Neural Networks Approach to Chemical Content Analysis of Vegetation

1. Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 1N. Kussul, 1V. Yatsenko, 2A. Sachenko, 3G. Markowsky,1A. Sydorenko, 1S. Skakun, 2S. Ganzha 1Space Research Institute NASU-NSAU,40 Glushkov Ave 03187 Kiev, Ukraine, inform@space.is.kiev.ua 2Institute of Computer Information Technologies of Ternopil Academy of National Economy3Peremoga Square, 46004, Ternopil, Ukraine, itu@tanet.edu.te.ua 3Department of Computer Science,5752 Neville Hall, University of Maine, Orono, ME 04469-5752, markov@cs.umaine.edu

2. Contents . . . Introduction Experimental results Architecture Comparison Problem solution Conclusions

3. Contents . . . Introduction Experimental results Architecture Comparison Problem solution Conclusions

4. Introduction Spectral characteristics of light, which is reflected from Earth objects, represent convenient and high informative data sources for remote investigations. It can be used for estimation of vegetation state to determine infection and pollution level of vegetation. Intensity dependence of reflected light on wave-length with different chlorophyll content

5. Introduction Each spectral curve contains 350 points, which determines the dimension of Neural Network input layer. It is evident that high dimension of input data and large training set requires the use of modular Neural Network architecture. Intensity dependence of reflected light on wave-length with different chlorophyll content

6. Contents . . . Introduction Experimental results Architecture Comparison Problem solution Conclusions

7. Architecture To determine plants damage (infection) level a modular Neural Network is used. It consists of classifier and interpolator.

8. Architecture Classifier executes data pre-processing (brute classification), dividing input data into 2 classes: damaged and undamaged.

9. Architecture If classifier output is 0 (i.e. input pattern is classified as damaged), then it is put on interpolator input.

10. Contents . . . Introduction Experimental results Architecture Comparison Problem solution Conclusions

11. Problem solution Before the investigation of modular architecture effectiveness is done, we will define the best training parameters of Neural Network and find the quantitative rates of training process

12. Problem solution To estimate the best Neural Network training parameters appropriate experiments were run. Dependence of number of training epochs on learning coefficient (full range)

13. Problem solution It is evident that the best values are the following: learning coefficient — 0.06, moment coefficient — 0.125. Dependence of number of training epochs on learning coefficient (smaller range)

14. Problem solution It is evident that the best values are the following: learning coefficient — 0.06, moment coefficient — 0.125. Dependence of number of training epochs on moment coefficient

15. Contents . . . Introduction Experimental results Architecture Comparison Problem solution Conclusions

16. Experimental results Obtained experimental results showed that both types of classifiers train quickly enough (classifier of the first type for 300-400 epochs, and classifier of the second type — for about 20 epochs. Classifier training process

17. Experimental results For interpolator a described above multi-layered Neural Network was used. A training set has smaller dimension. Dependence of interpolator training time on learning coefficient Dependence of interpolator training time on moment coefficient

18. Contents . . . Introduction Experimental results Architecture Comparison Problem solution Conclusions

19. Comparison Conducted experiments showed that modular architecture has advantages over traditional in the sense of training time. Comparative training time analysis of traditional and modular NN architectures. On x-axis there are values of learning coefficients (uniform fill) and moment coefficients (line fill). On y-axis there is a ratio between numbers of training iterations for traditional NN (T) and for modular NN (M)

20. Contents . . . Introduction Experimental results Architecture Comparison Problem solution Conclusions

21. Conclusions Full spectral analysis of plants (determination of full chemical composition of plants) with expansion of Neural Network architecture. Proposed modular architecture of NN for extended analysis of plants chemical contents