Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters.

ABSTRACT

The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R² value (0.993) and adjusted R² value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production.