Identification of Houseplants Using Neuro-Vision Based Multi-Stage Classification System

In this paper, we present a machine vision system that was developed on the basis of neural networks to identify twelve houseplants. image processing system was used to extract 41 features of color, texture and shape from the images taken from front and back of the leaves. the features were fed into the neural network system as the recognition criteria and inputs. multilayer perceptron (mlp) neural network with declining learning-rate factor algorithm (bdlrf) training algorithm was used as a classifier. classification was done in three stages based on eligibility and strength of characteristics in identifying the plants. eligibility criteria were assessed at each stage using plants class resolution power. in this classification method, each step requires a small number of attributes and for this reason its speed and accuracy can be very high. the results showed that the accuracy of classification of plants in three steps reaches 100%. also, the optimal features for classification included three inputting steps of morphological features, hsi color features extracted from back of the leaves, and hsi texture features of the back of the leaves.