The Use of Artificial Neural Networks to Model Selected Strength Parameters of the Giant Miscanthus Stalk

The aim of this work was to develop a model using Artificial Neural Networks (ANN) to predict stem cutting parameters for giant miscanthus. Experimental studies were conducted to determine biometric traits: maximum stem diameter (<i>Dmax</i>), minimum stem diameter (<i>Dmin</i>), stem wall thickness (<i>THwall</i>), and strength parameters (cutting force, cutting work) for two giant miscanthus genotypes, depending on the internode number (<i>NrNod</i>) and water content (<i>MC</i>). A total of 600 measurement results were obtained, which were randomly divided into training (60%), test (20%), and validation (20%) subsets. Two semantic models were adopted: one for predicting stem cutting force (ann1) and one for predicting cutting work (ann2). The independent variables (ANN inputs) were: <i>Gen</i>, <i>MC</i>, <i>NrNod</i>, <i>Dmax</i>, <i>Dmin</i>, and <i>THwall</i>. The ANN creation process was performed using Statistica Neural Networks. For each of the two semantic models (ANN1 and ANN2), 100 neural networks were developed, with the top 10 ANNs retained for further analysis. The criterion for selecting the best neural network was the root mean square error (RMSE) for the test subset. For ANN1, the RMSE values varied from 6.89 N to 8.70 N. For ANN2, the RMSE values varied from 0.086 J to 0.102 J. For the most accurate ANN1-03 (MLP 7-10-1), used to predict grass cutting force, the RMSE values were 6.46 N-6.89 N-4.70 N for the training, test, and validation subsets. For the most accurate ANN2-02 (MLP 7-10-1), used to predict grass cutting work, the RMSE values were 0.0646 J-0.0857 J-0.0596 J for the training, test, and validation subsets.