PREDICTION OF STEM WEIGHT IN SELECTED ALFALFA VARIETIES BY ARTIFICIAL NEURAL NETWORKS, MULTIVARIATE ADAPTIVE REGRESSION SPLINES AND MULTIPLE REGRESSION ANALYSIS - Ş. Çelik; E. Çaçan; S. Yaryab (Journal of Animal and Plant Sciences, 2023)
In this study,artificial neural networks (ANNs), Multivariate Adaptive Regression Splines (MARS) algorithm and multiple regression analysis (MLR) were used for plant stem weight prediction. Stem length, stem diameter, number of lateral branch, branch length, leaf number, stipule length and distance between stipules have been selected as input variables in these mentioned methods. A total of 150 plants were examined. Fifty plants from each of Gea, Bilensoy and Basbag alfalfa cultivars were analyzed separately. Our alfalfa varieties in this study are Gea, Bilensoy and Başbağ. In the ANN method, 70% of the data were allocated for training 20% for validation and 10% for testing. ANN training data were used in MARS algorithm and MLR. To measure which models can predict better, the coefficient of determination (R2) and mean square error (MSE) were compared each other. Correlation coefficients (r) of ANN, MARS and MLR in Stem Weight estimation were 0.801, 0.999 and 0.753 for Gea clover variety, respectively; 0.864, 0.997 and 0.711 for Bilensoy variety, respectively, and 0.781, 0.998 and 0561 for the Basbag variety, respectively. In the same models, R2 was 0.642, 0.998 and 0.567 for the Gea variety, respectively, 0.746, 0.994 and 0.505 for the Bilensoy variety, respectively, and 0.610, 0.997 and 0.315 for the Basbag variety, respectively. MSE values were 0.023, 0.008 and 2.498 for the Gea variety, respectively, 0.113, 0.014 and 1.409 for the Bilensoy variety, respectively, and 0.151, 0.017 and 4.641 for the Basbag variety, respectively. According to these criteria, the MARS algorithm provides a more realistic prediction than ANNs and MLR. The order of used algorithms in obtaining better prediction results in stem weight estimation in alfalfa plants was MARS> ANN> MLR.
Keywords: Artificial neural network; Medicago sativa L.; Plant Characteristics; Regression