Using Artificial Neural Networks for Plant Taxonomic Determination Based on Chlorophyll Fluorescence Induction Curves

ABSTRACT

Photosynthetic apparatus is a highly conservative element of the plant cell and its characteristics are comparatively similar no matter where a plant stands in the taxonomic classification. A problem of great interest is whether a highly informative method can be applied for taxonomic classification of plants based on their photosynthetic characteristics. We used as such one method artificial neural networks and as a source of information about the photosynthetic apparatus, chlorophyll fluorescence induction curves. We used feedforward neural network with back-propagation of error signals through the connections of the network. The designed network has two layers, five hidden neurons, and logarithmic sigmoidal transfer function. As input the network receives the numerical equivalent of the induction curves. In our studies we used induction curves from 35 different species which belonged to 19 different families from 17 orders divided into 11 superorders, all of which were from one of the two major groups of flowering plants (Monocotyledones, Dicotyledones). All together we used 2016 curves (61 curves per plant for most of the species). When a neural network is trained with the whole data set, the error is very high (over 50%). That's why we developed a system of neural networks each of which divides a taxonomic rank to its subgroups. This step-by-step approach starts with analysis of the whole data set and ends with classification of each family into its species used in the studies. This system allows taxonomic classification with high accuracy (the error is under 5%). Considering the written above, we can conclude that photosynthetic apparatus contains information about the genotype of the plants and can be used for their taxonomic classification.

Keywords:

• Artificial neural networks
• JIP-test
• Mathematical modeling
• Taxonomy
• Variable chlorophyll fluorescence