Simulation of the of the DeepLabv3 neural network learning process for the agricultural fields segmentation

Objective . Monitoring and determining the state of crops in agricultural production requires the use and improvement of neural network methods of artificial intelligence. The aim of the study is to create a mathematical model of the learning process of the DeepLabV3 neural network for intelligent a...

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Bibliographic Details
Published in:Vestnik Dagestanskogo gosudarstvennogo tehničeskogo universiteta. Tehničeskie nauki (Online) Vol. 50; no. 3; pp. 142 - 149
Main Authors: Rogachev, A. F., Belousov, I. S.
Format: Journal Article
Language:English
Russian
Published: Dagestan State Technical University 29.10.2023
Subjects:
artificial neural networks
crops
mathematical modeling
segmentation problem
ISSN:2073-6185, 2542-095X
Online Access:Get full text
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Summary:Objective . Monitoring and determining the state of crops in agricultural production requires the use and improvement of neural network methods of artificial intelligence. The aim of the study is to create a mathematical model of the learning process of the DeepLabV3 neural network for intelligent analysis and segmentation of agricultural fields. Method . Based on the newly formed RGB database of images of agricultural fields, marked up into four classes, a neural network of the DeepLabV3 architecture was developed and trained. Approximations of the learning curve by the modified Johnson function are obtained by the methods of least squares and least modules. Result . A statistical assessment of the quality of training and approximation of neural networks to the DeepLabV3 architecture in combination with ResNet 50 was carried out. The constructed DNN family based on DeepLabV3 with ResNet50 showed the efficiency of recognition and sufficient speed in determining the state of crops. Conclusions . Approximation of the neural network learning diagram to the DeepLabV3 architecture, using a modified Johnson function, allows us to estimate the value of the “saturation” of the simulated dependence and predict the maximum value of the neural network metric without taking into account its possible retraining.
ISSN:2073-6185
2542-095X
DOI:10.21822/2073-6185-2023-50-3-142-149