## 6 ANN Applications in Agricultural and Biological Engineering  
ANNs have the largest body of applications in agricultural and biological engineering compared to other soft computing techniques such as fuzzy logic and genetic algorithms. In summary of related 348 papers and reports, ANNs have been applied in solving problems in food quality and safety (35.3%), crop (22.7%), soil and water (14.4%), precision agriculture (6.6%), animal management (5.2%), post harvest (2.6%), food processing (2.3%), greenhouse control (2%), agricultural vehicle control (1.2%), agricultural machinery (1.2%), agricultural pollution (1.2%), agricultural biology (1.2%), ecology and natural resources (1.4%), agricultural robotics (0.3%), chemical application (0.3%), and others (2.3%) such as bioenergy and agricultural facilities (Figure 10). These ANN applications have been created mainly through classification (45.1%), modeling and prediction (44%), control (4%), and simulation (2.6%), parameter estimation (2%), detection (1.2%), data clustering (0.6%), optimization (0.3%) and data fusion (0.3%) as well (Figure 11). Early applications include modeling sensory color quality of tomato and peach [99], investigation of combined effects of CO\({}_{2}\) and sucrose on the growth of alfalfa cuttings using a Kalman filter neural network [100], classification of apple surface features [101], corn kernel breakage classification [102], and machine vision inspection of potatoes [103]. Recently, the BP ANN method was used in regression for modeling the correlation between crop yield and 10 yield components of chickpea [104]. A BP ANN was employed and trained with extracted features from the data collected by electronic nose to identify the wheat age [105]. BP neural network and generalized regression neural network techniques were developed and compared to model source gas and PM10 concentration and emission rate (GPCER) generated and emitted from swine deep-pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in-house manure storage levels, and weather conditions [106]. MLP models were developed and used to predict or classify water stress in samples of Sunagoke moss using the texture features extracted from color co-occurrence matrix (CCM) and gray-level co-occurrence matrix (GLCM) methods [107]. A BPANN was developed for predicting fungal infections using input from principal components extracted from sample visible and near-infrared (NIR) reflectance spectroscopy for early detection of Botrytis cinerea on eggplant leaves [108]. Similar to applications in general engineering fields, most of the work in agricultural and biological engineering has been accomplished by multilayer feedforward ANN trained by the famous BP algorithm, which was inspired by the work of [PERSON] _et al._ in 1986. Among the 348 collected papers and reports, besides 83 of them in which the ANNs' structure and training algorithm were not  
Figure 11: Technical area distribution of ANNs in agricultural and biological engineering. Figure 10: Application area distribution of ANNs in agricultural and biological engineering. explicitly stated, 210 of them (60%) were based on MFNs trained by the BP algorithm. Twelve of them (3.5%) were based on PNN (Probabilistic Neural Network). Eleven of them (3%) were based on MLP using different training algorithms such as Levenberg-Marquardt optimization procedure, Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization procedure, and genetic algorithm. Seven of them (2%) were based on Kohonen SOM. Six of them (2%) were based upon other unsupervised training algorithms such as fuzzy art, ART2 and Auto- Associative network. Four of them (1%) were based on RBF networks. Three of them (0.8%) were based on LVQ (Learning Vector Quantization). Two of them (0.6%) were based on GRNN (Generalized Regression Neural Network). Ten of them (3%) were based on other network structures such as Counter-Propagation (CP) and Adaptive Logic Network (ALN).