Abstract
In this paper, the classification capabilities of perceptron and radial neural networks are compared using the identification of selected pests feeding in apple tree orchards in Poland as an example. The goal of the study was the neural separation of five selected apple tree orchard pests. The classification was based on graphical information coded as selected characteristic features of the pests, presented in digital images. In the paper, MLP (MultiLayer Perceptrons), RBF (Radial Basis Function) and DNN (Deep Neural Networks) neural classification models are compared, generated using learning files acquired on the basis of information contained in digital photographs of five selected pests. In order to classify the pests, neural modeling methods were used, including digital image analysis techniques. The qualitative analysis of the neural models enabled the selection of optimal neuron topology that was characterized by the highest classification capability. As representative graphic features were selected five selected coefficients of shape and two defined graphical features of the classified objects. The created neuron model is dedicated as a core for computer systems supporting the decision processes occurring during apple production, particularly in the context of apple tree orchard pest protection automation.
Highlights
The observed progress in the broadly defined applied information technology results in the capability to successfully simulate complex identification processes using increasingly more effective computers
Methods that include computer analysis techniques and modern neural modeling methods are utilized for this purpose
It is a fact that the popularity of Artificial Neural Networks (ANN) mainly stems from the ability to fairly model linear and non-linear problems, and practical studying of matters described using curvilinear models
Summary
The observed progress in the broadly defined applied information technology results in the capability to successfully simulate complex identification processes using increasingly more effective computers. Methods that include computer analysis techniques and modern neural modeling methods are utilized for this purpose. As a result, this enables the identification process to be automated and certain problems arising out of human nature, such as subjectivism of the expert performing the analysis, to be alleviated. One can often observe problems appearing difficult and non-linear which can relatively be solved using broadly defined linear methods and techniques. It is a fact that the popularity of Artificial Neural Networks (ANN) mainly stems from the ability to fairly model linear and non-linear problems, and practical studying of matters described using curvilinear models.
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