2.3.2. Model Inputs and Outputs

A thorough comprehension of the determinants of threshing performance is required to obtain accurate threshing performance prediction. The rotational speed of the cylinder is closely associated with the performance of the thresher because high-speed results in cracking of the grain, and low-speed leads to unthreshed grain. Moreover, the threshing clearance and separating clearance of the concave sieve significantly influence the threshing performance. Furthermore, the size of the feeding quantity is closely correlated to the threshing characteristics [30].

The primary factors affecting threshing performance include the rotational speed of the cylinder, threshing clearance of the concave sieve, separating clearance of the concave sieve, and feeding quantity. Other factors include total threshing power consumption and grain moisture content that contribute to a lesser degree, thus considered secondary. Grain moisture content was excluded in this work since the tests were conducted under specific moisture content conditions during the harvest period.

The aforementioned factors, i.e., the rotational speed of cylinder (RS), threshing clearance of concave sieve (TC), separating clearance of concave sieve (SC), and feeding quantity (FQ), were introduced to the ANN as the model input variables. On the other hand, the crushing rate (YP), impurity rate of threshed materials (YZ), and entrainment loss rate (YS) were the output variables. Sensitivity analysis was conducted on the trained network to identify the input variables with the most significant impact on threshing performance predictions.

Sensitivity analysis (Figure 4) was based on the validation set, where the input variable was RS, TC, SC, and FQ. To normalize the input variables, the value of the input variable was first changed, the trained network was introduced, the maximum and minimum output values were recorded, the difference between the maximum and the minimum value was computed, the difference to the maximum value was calculated, before finally taking the mean of all ratios as the sensitivity of the classification variable. Lastly, the sensitivity size was compared to establish the sensitivity of each categorical variable to the output variable. The sensitivity analysis results will be discussed later.

**Figure 4.** The sensitivity analysis flow.

2.3.3. Data Division and Preprocessing

The database was randomly divided into three sets, i.e., training, testing, and validation. A training set was used to construct the neural network model, whereas an independent validation set was used to estimate model performance in the deployed environment [31]. In total, 60% of the data were used for training, 20% for testing, and 20% for validation. Table 4 shows the orthogonal test for different levels as well as the data ranges used for the ANN model variables.

**Table 4.** Orthogonal test factor level table used for Artificial Neural Network Model Variables.


Notably, it is critical to preprocess the data into an appropriate format before applying it to the ANN. Preprocessing the data by scaling is crucial in ensuring that all variables receive equal attention during training. The output variables must be scaled to commensurate with the limits of the transfer functions used in the output layer. Although scaling the input variables is not necessary, it is often recommended [32]. Here, the input and output variables were scaled between −1.0 and 1.0, as the purelin sigmoidal transfer function was used in the output layer.
