# Quality Assessment of Components of Wheat Seed Using Different Classifications Models
## 4 Discussion and Conclusions  
A continuous increase in the number of classification features reduces accuracy. This fact was observed in the QDA model. Except for the first 35 features, the other features were almost insignificant. Thus, by leaving the classification accuracy untouched, they can be removed and one can only use the top 35 features of Table 1 in classifying seven-grain groups (sound white wheat, sound white wheat, broken white wheat, shrunken white wheat, red wheat, barley, and rye). An acceptable classification accuracy and the low intrusion of sound white wheat and red wheat grains is a consequence of their differences in colour and texture. Being uniform in colour and texture, barley grains were classified with 100% accuracy, while shrunken white wheat grains were classified with 90.7% accuracy. The similarities in colour and shape of shrunken white wheat with other grains except barley decreases classification accuracy in general. For example, 13 grains out of 300 shrunken white wheat were wrongly classified as sound white wheat class. [PERSON] et al. [1] reached 95% accuracy in classifying shrunken white wheat by analysing the image of the transmitted light through the bulk of single-layer. It should be noted that the number of classes in their study was fewer than in the present work. The average of classification accuracy of this research is also lower than the work of [PERSON] and [PERSON] [7]. They studied only five groups of complete cereal grains and used a camcorder for imaging. For this reason, they may have attained a higher accuracy. However, in comparison with the study of [PERSON] et al. [7], this work had a higher classification accuracy. Since they had 10 classification groups, the accuracy was quite low. By taking note of the obtained acceptable accuracy, one can conclude that imaging with the resolution of five mega pixels (1920 \(\times\) 2560) is enough to evaluate the visual features of wheat. The use of fluorescent lamps in a closed (controlled) environment may suffice as a lighting system due to the stability of light intensity. The application of all the features, however, might decrease the classification accuracy. The suitable area for classification accuracy with lower number of features indicates the applicability of the ReliefF method in ranking features; a comparison of different methods, especially genetics algorithms, is highly recommended. Statistical classification models and support vector machines gave an acceptable accuracy by employing only 35 features (16 shape features, 11 texture features, and 8 colour features). The lowest classification accuracy in identifying seven groups of grains with the use of independent data was observed in the linear discriminate analysis (LDA) model. The quadratic support vector machine (QSVM) was proven to be of better applicability in this study. Barley grains were identifiable with the highest accuracy due to their shape differences. It can be concluded that the industrial digital camera and support vector machine classification methods or statistical methods have enough potential for classifying quality elements of wheat seed. It is possible to increase the identifiable groups if system training with new groups was required. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Target Classes**} & \multirow{2}{*}{**Test Samples**} & \multicolumn{6}{c}{**Output Classes**} & \multirow{2}{*}{**Classification Accuracy**} \\ \cline{3-3} \cline{5-10}  & & **a** & **b** & **c** & **d** & **e** & **f** & **g** \\ \hline a & 300 & 296 & 2 & 0 & 0 & 1 & 0 & 1 & 98.7 \\ b & 300 & 0 & 294 & 0 & 0 & 0 & 0 & 6 & 98 \\ c & 300 & 0 & 0 & 300 & 0 & 0 & 0 & 100 \\ d & 300 & 0 & 0 & 3 & 292 & 3 & 0 & 2 & 97.3 \\ e & 300 & 1 & 1 & 0 & 1 & 297 & 0 & 0 & 99 \\ f & 300 & 0 & 0 & 0 & 0 & 0 & 298 & 2 & 99.3 \\ g & 300 & 3 & 13 & 0 & 5 & 2 & 5 & 272 & 90.7 \\ \hline \end{tabular}
\end{table} 
Table 3: Confusion matrixes of the QSVM classifier using the first 35 features on test data.