Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN

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.

Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed

“…Currently, one of the most modern techniques of image processing [ 19 , 20 ] and image classification [ 21 , 22 ] in decision-making processes is the use of artificial intelligence methods [ 23 , 24 ]. In recent years, artificial neural networks have become so popular and effective that they started to be used in various problematic areas, among other things, in the optimization of food processes [ 25 ].…”

Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

“…Figure 2 shows that MLP benefits from a three-layer structure, including the input layer, hidden layer/s, and output layer/s, in which each neuron is connected to all the neurons in the next layer. It is frequently reported that MLP has a great function in non-linear problems [46,47].…”

“…Figure 2 shows that MLP benefits from a three-layer structure, including the input layer, hidden layer/s, and output layer/s, in which each neuron is connected to all the neurons in the next layer. It is frequently reported that MLP has a great function in non-linear problems [46,47]. where I represent the input layer, Ii is the input variable i, n shows the total number of inputs, βj is a bias value, and ωij is the weight of connections in j level.…”

Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

“…Figure 2 shows that MLP benefits from a three-layer structure, including the input layer, hidden layer/s, and output layer/s, in which each neuron is connected to all the neurons in the next layer. It is frequently reported that MLP has a great function in non-linear problems [49][50]. (1) Where I represent the input layer, Ii is the input variable i, n shows the total number of inputs, βj is a bias value, ωij is the weight of connections in j level.…”