Acid Rain and Seed Germination: A Predictive Model Using ML-based CART Algorithm

The impact of acid rain on the germination seeds is a significant concern in agricultural and environmental studies. Acid rain, characterized by elevated acidity levels due to pollutants like sulfur dioxide nitrogen oxides, can adversely affect process various plant species. objective this study was evaluate simulated (SAR) Brinjal (Solanum melongena Linn.) Cowpea (Vigna unguiculata ssp. cylindrica L. Walpers) crops. experiments were conducted using eight plastic trays approximately 25 cm. x 30 cm dimensions. Four used for with brinjal (Set I), while other four cowpea II). One tray from each set as positive control treated normal pH 5.6, three batch SAR solutions 4.5, 3.5, 2.5. seed percentage vigor inferior seeds. (pH 2.5) showed hindered germination. Furthermore, more inhibitory effect observed at lower values. mean highest standard 5.6) case seeds, it recorded lowest results indicate that plants do not respond uniformly SAR. To investigate behavior data, Machine Learning-based Decision Tree Algorithm employed identify optimize conditions. predicted get 95% germination, whereas would only be 64% value 5.05 36 hours. In conclusion, utilizing CART algorithm has provided valuable insights into predicting under influence rain.