## 6 ANN Applications in Agricultural and Biological Engineering
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Year** & **Author** & **Fusion Type** & **Application Area** \\ \hline
1992 & [PERSON] _et al._[109] & ANN modeling for fuzzy control & Extrusion control \\
1997 & [PERSON] and [PERSON] [110] & ANN modeling plus fuzzy control simulation & & Bread baking process control \\
1997 & [PERSON] _et al._[111] & ANN modeling plus GA parameter optimization for fuzzy control & Fruit storage control \\
2001 & [PERSON] _et al._[112] & Conceptual and structural fusion of fuzzy logic and ANN & ET model optimization \\
2003 & [PERSON] _et al._[118] & FCM clustering for RBF training & Prediction of the performance of vegetative filter strips \\
2003 & [PERSON] _et al._[113] & SOM with FCM clustering & Color image segmentation of edible beans \\
2003 & [PERSON] _et al._[119] & ANFIS modeling & Prediction of multiple soil properties \\
2003 & [PERSON] _et al._[120] & ANFIS classification & Adaptive image segmentation for weed detection \\
2004 & [PERSON] _et al._[115] & Fuzzy-Neural Network unsupervised classification & Classification of soils \\
2004 & [PERSON] _et al._[114] & ANFIS classification & Classification of uniform plant, soil, and residue color images \\
2004 & [PERSON] _et al._[121] & Fuzzy c-means clustering for RBF training & Prediction of sediment and phosphorous movement through vegetative filter strips \\
2006 & [PERSON] and [PERSON] [115] & ANFIS classification & Hydraulic vane pump health classification \\
2007 & [PERSON] and [PERSON] [117] & ANN modeling plus ANFIS training of fuzzy logic controller & Outdoor automatic camera parameter control \\ \hline \end{tabular}
\end{table} 
Table 1: Papers and reports on fusion of fuzzy logic and ANNs in agricultural and biological engineering. and an ANFIS for implementing controller to automatically adjust multispectral camera parameter to compensate for changing natural lighting conditions and to acquire white-balanced images. The interest of SVMs in agricultural and biological engineering is steadily growing (Figure 13). The figure indicates that although the number of peer reviewed publications on this topic did not increase steadily over the past few years, a trend exists for total number of papers and report to increase in the past few years and to be able to project this trend in the next few years. The successful implementations of SVMs in agricultural and biological engineering are listed in Table 2, which includes classification of intact and cracked eggs [122], classification of forest data cover types [123], classification of meat with small data set [124], black walnut shell and meat classification using hyperspectral fluorescence imaging [125], classification to differentiate individual fungal infected and healthy wheat kernels [126], classification for weed and nitrogen stress detection in corn [127], discrimination for screening of compound feeds using NIR hyperspectral data [128], identification of tea varieties by computer vision [129], discrimination of wheat classes with NIR spectroscopy [130], detection of underdeveloped hazenuts from fully developed nuts by impact acoustics [131], classification of electronic nose data [132], classification of modified starches [133], classification of milk with an electronic nose [134], and recognition of plant disease [135]. In these applications, [PERSON] _et al._[126] fused SVM with kernel of RBF neural network. [PERSON] and [PERSON] [132] implemented SVM with RBF kernel. [PERSON] _et al._[134] applied SVM neural network with one hidden layer of non-linear neurons, one-output linear neuron and specialized learning procedure leading to the global minimum of the error function and excellent generalization ability of the trained network.