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

2. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Introduction Contents... Architecture Problem solution Experimental results Comparison Conclusions

3. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Introduction Contents... Architecture Problem solution Experimental results Comparison Conclusions

4. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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 Introduction

6. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Introduction Contents... Architecture Problem solution Experimental results Comparison Conclusions

7. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation To determine plants damage (infection) level a modular Neural Network is used. It consists of classifier and interpolator. Architecture

8. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Classifier executes data pre-processing (brute classification), dividing input data into 2 classes: damaged and undamaged. Architecture

9. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation If classifier output is 0 (i.e. input pattern is classified as damaged), then it is put on interpolator input. Architecture

10. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Introduction Contents... Architecture Problem solution Experimental results Comparison Conclusions

11. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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 Problem solution

12. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation To estimate the best Neural Network training parameters appropriate experiments were run. Dependence of number of training epochs on learning coefficient (full range) Problem solution

13. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Dependence of number of training epochs on learning coefficient (smaller range) It is evident that the best values are the following: learning coefficient — 0.06, moment coefficient — 0.125. Problem solution

14. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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 Problem solution

15. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Introduction Contents... Architecture Problem solution Experimental results Comparison Conclusions

16. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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 Experimental results

17. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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 Experimental results

18. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Introduction Contents... Architecture Problem solution Experimental results Comparison Conclusions

19. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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) Comparison

20. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation Introduction Contents... Architecture Problem solution Experimental results Comparison Conclusions

21. ForwardBack Modular Neural Networks Approach to Chemical Content Analysis of Vegetation 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 Conclusions