Classification of Varieties of Grain Species by Artificial Neural Networks

In this study, an Artificial Neural Network (ANN) model was developed in order to classify varieties belonging to grain species. Varieties of bread wheat, durum wheat, barley, oat and triticale were utilized. 11 physical properties of grains were determined for these varieties as follows: thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters. It was found that these properties had been statistically significant for the varieties. An Artificial Neural Network was developed for classifying varieties. The structure of the ANN model developed was designed to have 11 inputs, 2 hidden and 2 output layers. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour were used as input parameters; and species and varieties as output parameters. While classifying the varieties by the ANN model developed, R2, RMSE and mean error were found to be 0.99, 0.000624 and 0.009%, respectively. In classifying the species, these values were found to be 0.99, 0.000184 and 0.001%, respectively. It has shown that all the results obtained from the ANN model had been in accordance with the real data.

Influence of moisture content on physical properties of maize

Influence of moisture content on physical properties of maizeThe physical properties of maize kernels have been evaluated as a function of moisture content (12.8-29.0% w.b.). The maize kernel length, width, thickness, geometric mean diameter, surface area, sphericity and kernel volume increased linearly with increase in moisture content. The bulk density and true density decreased while porosity and thousand grains mass increased with increasing moisture content. The static coefficient of friction increased for mild steel sheet, galvanized iron sheet and aluminium sheet surfaces while rupture or cutting force and energy absorbed decreased with increasing moisture content.

Evaluation of single-kernel density of scab-damaged winter wheat

Evaluation of single-kernel density of scab-damaged winter wheatMeasurements of single-kernel mass and volume made on healthy (control) and scab-damaged samples of grain of three winter wheat varieties never resulted in lower values of mean single-kernel density for scab-damaged grain. This finding, contrary to common opinion, can be explained as being a result of the comparable magnitude of relative decrease (due to infestation) of two features (mass and volume) that define single-kernel density. The discrepancy between results presented in this paper (kernel volume was determined with an air pycnometer) and the results in some other reports (liquid pycnometers used) can result from the different methods applied for kernel volume measurements: when a liquid medium is used the surface tension effect tends to overestimate the volume, especially for scabby kernels that are known to be shrivellediepossessing voids and pores at the surface that the liquid cannot penetrate. As a consequence kernel density of scabby kernels can be significantly underestimated.

The Relation of Certain Physical Characteristics of the Wheat Kernel to Milling Quality

Kernel volume, because of its relation to the ratio of endosperm to non-endosperm structures, varies directly with the potential flour yield when comparisons are restricted to the same type or variety of wheat.Accurate determination of kernel density must include the complete removal of all mechanically held air.