## 7 Conclusion and Future Directions  
As described above, ANNs have their advantages and have been used as a powerful tool in solving problems in scientific research and engineering applications. ANNs have their own limitations to restrict them as a substitute of traditional methods such as statistical regression, pattern recognition, and time series analysis. In the next decade with advanced development of computer power, ANNs \begin{table}
\begin{tabular}{c l l l} \hline
2004 & [PERSON] _et al._[134] & SVM neural network & Classification of milk by an electronic nose \\  & & classification & \\
2004 & [PERSON] _et al._[135] & SVM classification & Classification for recognition of plant \\  & & & disease \\
2005 & [PERSON] and [PERSON] & SVM with RBF kernel of RBF & Classification of electronic nose data \\
2005 & [PERSON] _et al._[133] & SVM classification & Classification of modified starches by \\  & & & Fourier transform infrared spectroscopy \\
2006 & [PERSON] _et al._[129] & SVM classification & Identification of tea varieties by computer \\  & & & vision \\
2006 & [PERSON] _et al._[127] & SVM classification & Classification for weed and nitrogen stress \\  & & & detection in corn \\
2006 & [PERSON] _et al._[131] & SVM classification & Detection of underdeveloped hazenuts from \\  & & & fully developed nuts by impact acoustics \\
2006 & [PERSON] _et al._[128] & SVM classification & Discrimination of screening of compound \\  & & & feeds using NIR hyperspectral data \\
2006 & [PERSON] and [PERSON] [130] & Least-Squares SVM & Discrimination of wheat classes with NIR \\  & & classification & spectroscopy \\
2007 & [PERSON] _et al._[125] & Gaussian kernel based SVM & Black walnut shell and meat classification \\  & & classification & using hyperspectral fluorescence imaging \\
2007 & [PERSON] _et al._[137] & SVM modeling and prediction & Simulation of daily, weekly, and monthly \\  & & & runoff and sediment yield from a watershed \\
2007 & [PERSON] _et al._[126] & Multi-class SVM with kernel of & Classification to differentiate individual \\  & & RBF neural network & fungal infected and healthy wheat kernels. \\
2008 & [PERSON] _et al._[138] & Least-Squares SVM modeling & Quantification of vitamin C content in \\  & & and prediction & kiwifruit using NIR spectroscopy \\
2008 & [PERSON] _et al._[124] & SVM classification & Classification of meat with small data set \\
2008 & [PERSON] _et al._[139] & SVM modeling and prediction & Prediction of different concentration classes \\  & & & of instant coffee with electronic tongue \\  & & & measurements \\
2008 & [PERSON] and [PERSON] [140] & Least-Squares SVM modeling & Prediction of pork meat total viable bacteria \\  & & and prediction & count with hyperspectral imaging \\
2008 & [PERSON] _et al._[106] & SVM modeling and prediction & On-line assessing internal quality of pears \\  & & & using visible/NIR transmission \\
2008 & [PERSON] and [PERSON] [123] & SVM classification & Classification of forest data cover types \\
2008 & [PERSON] _et al._[9] & Least-Squares SVM modeling & Rice wine composition prediction by \\  & & and prediction & visible/NIR spectroscopy \\
2009 & [PERSON] _et al._[122] & SVM classification & classification of intact and cracked eggs \\ \hline \end{tabular}
\end{table} 
Table 2: Cont. will continue to develop new applications in various fields, including agricultural and biological engineering. As a powerful alternative to conventional methods, ANNs will be studied more to develop approaches to overcoming problems of ANNs in general or in a specific research area. For effective research and development, the guidelines may be generated for predetermining best ANN structure and training algorithms for a given problem. For process statistical modeling, additional research topics may be to establish generic procedures to include significant variables to and exclude non-significant ones from ANN models and to add confidence limits on the output predictions and parameter estimations.