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ESCOLA POLITÉCNICA DA UNIVERSIDADE DE SÃO PAULO AGRICULTURAL AUTOMATION LABORATORY INSTITUTO DE BOTÂNICA PLANT PHYSIOLOGY AND BIOCHEMISTRY SECTION MODELING.

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Presentation on theme: "ESCOLA POLITÉCNICA DA UNIVERSIDADE DE SÃO PAULO AGRICULTURAL AUTOMATION LABORATORY INSTITUTO DE BOTÂNICA PLANT PHYSIOLOGY AND BIOCHEMISTRY SECTION MODELING."— Presentation transcript:

1 ESCOLA POLITÉCNICA DA UNIVERSIDADE DE SÃO PAULO AGRICULTURAL AUTOMATION LABORATORY INSTITUTO DE BOTÂNICA PLANT PHYSIOLOGY AND BIOCHEMISTRY SECTION MODELING PHOTOSYNTHESIS OF THE TROPICAL TREE Hymenaea courbaril L. USING ARTIFICIAL NEURAL NETWORKS Madeleine Barriga Puente de la Vega Antonio Mauro Saraiva Hernán Prieto Schmidt Marcos Silveira Buckeridge

2 2 TOPICS  Motivation  Objectives  Modeling photosynthesis with artificial neural networks (ANNs)  Results  Conclusions

3 3 MOTIVATION Evaluate carbon exchange plants-atmosphere Artificial neural networks (ANNs) Non-linear problems Potential to be used in ecophysiology Ecophysiologic process (photosynthesis): non-linear Modeling photosynthesis in Hymenaea courbaril with ANNs Initial step to quantify CO 2 absorption by plants

4 4 OBJECTIVE Modeling the daily cycle of photosynthesis in Hymenaea courbaril (jatobá), in leaf scale, at three levels of the plants with artificial neural networks.

5 5 MODELING PHOTOSYNTHESIS WITH ANNs  Multilayer perceptron (MLP) with backpropagation training algorithm.  Supervised training: input and target vector pairs.  Collected data were used for the training: 65% for the training and 35% for the test.

6 6 MLP Network used to model photosynthesis. MODELING PHOTOSYNTHESIS WITH ANNs Photosynthesis Tleaf Tair R. H. PAR CO 2 Time Output Layer Hidden Layer Input Layer

7 7 CombinationInput Variables (IV) 1 6 IV: Time, Tair, Tleaf, CO 2 R, RH-R, PARi 2 7 IV: Time, Tair, Tleaf, CO 2 R, CO 2 S, RH-R, PARi 3 8 IV: Time, Tair, Tleaf, CO 2 R, CO 2 S, RH-R, RH-S, PARi MODELING PHOTOSYNTHESIS WITH ANNs Modeling for each of the three plant levels. Different combinations of the input variables.

8 8 RESULTS Results for training of the ANNs corresponding to three combinations and three levels. Combination Mean Relative Error (%) Level 1Level 2Level

9 9 RESULTS Results for test of the ANNs corresponding to three combinations and three levels. Combination Mean Relative Error (%) Level 1Level 2Level

10 10 RESULTS Combination Mean Relative Error (%) Level 1Level 2Level 3 TrTeTrTeTrTe Combination 1: error difference (test - training) < 9% -Combinations 2 and 3: error difference (test - training) <3%

11 11 RESULTS Results of modeling the daily photosynthesis cycle for combination 3 corresponding to level 1.

12 12 CONCLUSION  Modeling photosynthesis with ANNs showed to be very efficient.  Results corresponding to combination three, levels one and two, had an average error of 8%, and to level three, 6%.  The variables that influenced the good performance of the ANNs were CO 2 R and CO 2 S.  The results suggest that the ANNs technique could be useful for forecasts of carbon assimilation by tropical trees.


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