Prediction of Physical Parameters of Pumpkin Seeds Using Neural Network
Demir, Bünyamin
The design of the machines and equipment used in harvest and post-harvest processing should be compatible with the physical, mechanical and rheological characteristics of the fruits and vegetables. In machine design for agricultural products, several characteristics of relevant products and seeds should be known ahead. Designers can either measure all these design parameters one by one, or they may use intelligent systems to estimate such parameters. Neural networks (NNs) are new computational tools that provide a quick and accurate means of physical properties prediction of agricultural materials, and have been shown to perform well in comparison with traditional methods. In this research, some physical properties of pumpkin (Cucurbita pepo L.) seeds, including linear dimensions, volume, ...
Estimation of the Colour Properties of Apples Varieties Using Neural Network
Demir, Bünyamin
The consumer acceptance and the quality standard of agricultural products such as apple are determined mostly by their colour. Colour is measured with a colorimeter and quantified using the C.I.E. L*, a*, b* colour space system. It is used commonly by researchers for the classification and identification of apple fruit. To the best of our knowledge, the present study is the first study investigating the prediction of some colour properties of six apple varieties through artificial neural networks (ANN). The apple varieties are ‘Amasya’, ‘Starking’, ‘Granny Smith’, ‘Pink Lady’, ‘Golden Delicious’, ‘Arapkızı’ and the colour properties are L* (lightness), a* (redness), b* (yellowness), C* (chroma), h* (hue angle), CI (chroma index). General Regression Neural Networks (GRNN) and Adaptive Neuro...
Design of Neural Network Predictor for the Physical Properties of Almond Nuts
Demir, Bünyamin
In this study, an adaptive neuro fuzzy interface system (ANFIS) based predictor was designed to predict the physical properties of four almond types. Measurements of the dimensions, length, width and thickness were carried out for one hundred randomly selected samples of each type. With using these three major perpendicular dimensions, some physical parameters such as projected area, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, shape index and aspect ratio were estimated. In in a various Artificial Neural Network (ANN) structures, ANFIS structure which has given the best results was selected. The parameters analytically estimated and those predicted were given in the form of figures. The root mean-squared error (RMSE) was found to be 0.0001 which is ...
Predictive Modelling of Ball Burnishing Process Using Regression Analysis and Neural Network
ÖZGÜN, SÜEDA
The present paper focuses on two techniques, namely regression and neu- ral network techniques, for predicting surface roughness in ball burnish- ing process. Values of surface roughness predicted by the two techniques were compared with experimental values. Also, the effects of the main burnishing parameters on surface roughness have been determined. Sur- face roughness (Ra) was taken as response (output) variable and burnish- ing force, number of passes, feed rate, and burnishing speed were taken as input parameters. Relationship between the surface roughness and burnishing parameters was found out for direct measurement of the surface roughness. Results showed the application of the regression and neural network models to accurately predict the surface roughness. Der vorliegende Beitrag...