<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The objective of the research was to create a model which defines the relation between a fundamental contact area of a seed and the pressure force, water content in a seed and its geometrical dimensions with application of artificial neural networks (SSN). Computer program Statistica Neural Networks v. 6.0. was used for formation of a neural model. Tests were carried out on Roma wheat seed and Dańkowskie Złote rye with six various water contents: 0.11 0.15 0.19 0.23 0.28 0.33 (kg·kg<sup>-1</sup> dry mass). Caryopses were loaded with eight values of compression force - from 41 N to 230 N. Multiplicity of iterations was 5. Seed material was moistened to obtain a specific water content. Each seed was loaded with compression force with respectively growing values: 41N, 68N, 95N, 122N, 149N, 176N, 203N and 230N. A four-layer network of Perceptron type with 10 neurons in the first and 8 neurons in the second hidden layer was selected as a model which the best defines the contact area of grain seeds loaded with axial force at various moisture levels. This network has 4 inputs (water content, pressure force, thickness and length of caryopses) and one output (elementary contact area of rye and wheat seeds). Comparison of the neural model with empirical formulas obtained from nonlinear estimation proved a considerable higher precision of the first one.</p></abstract>

The objective of the research was to create a model which defines the relation between a fundamental contact area of a seed and the pressure force, water content in a seed and its geometrical dimensions with application of artificial neural networks (SSN). Computer program Statistica Neural Networks v. 6.0. was used for formation of a neural model. Tests were carried out on Roma wheat seed and Dańkowskie Złote rye with six various water contents: 0.11 0.15 0.19 0.23 0.28 0.33 (kg·kg-1 dry mass). Caryopses were loaded with eight values of compression force - from 41 N to 230 N. Multiplicity of iterations was 5. Seed material was moistened to obtain a specific water content. Each seed was loaded with compression force with respectively growing values: 41N, 68N, 95N, 122N, 149N, 176N, 203N and 230N. A four-layer network of Perceptron type with 10 neurons in the first and 8 neurons in the second hidden layer was selected as a model which the best defines the contact area of grain seeds loaded with axial force at various moisture levels. This network has 4 inputs (water content, pressure force, thickness and length of caryopses) and one output (elementary contact area of rye and wheat seeds). Comparison of the neural model with empirical formulas obtained from nonlinear estimation proved a considerable higher precision of the first one.

Application of Artificial Neural Networks in Modelling The Contact Area of Grain Seeds

Department of Mechanical Engineering and Agrophysics, University of Agriculture in Krakow,

Agricultural Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The objective of the research was to create a model which defines the relation between a fundamental contact area of a seed and the pressure force, water...

Application of Artificial Neural Networks in Modelling The Contact Area of Grain Seeds

Publié en ligne: 09 févr. 2017

Pages: 27 - 37

Reçu: 01 août 2016

Accepté: 01 sept. 2016

DOI: https://doi.org/10.1515/agriceng-2016-0061

Mots clés
contact area, seed material, artificial neural networks

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Application of Artificial Neural Networks in Modelling The Contact Area of Grain Seeds

Publié en ligne: 09 févr. 2017

Pages: 27 - 37

Reçu: 01 août 2016

Accepté: 01 sept. 2016

DOI: https://doi.org/10.1515/agriceng-2016-0061

Mots cléscontact area, seed material, artificial neural networks

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
contact area, seed material, artificial neural networks