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

Vasundhara Arora; Bikram Jit Singh; Navneet Bithel; Tapan Kumar Mukherjee; Sushil Kumar Upadhyay; Rippin Sehgal; Raj Singh

doi:10.18006/2023.11(4).720.735

Authors

Vasundhara Arora Department of Botany C.C.S. University, Meerut, UP, India
Bikram Jit Singh Department of Mechanical Engineering, MM Engineering College, Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala, 133207, Haryana, India https://orcid.org/0000-0002-7250-0165
Navneet Bithel Department of Botany and Microbiology, Gurukul Kangri University, Haridwar -249404, Uttrakhand, India https://orcid.org/0000-0002-2583-9182
Tapan Kumar Mukherjee Department of Biotechnology, Amity University, Major Arterial Road, Action Area II-36, 37, 38, Rajarhat, New Town, Kolkata,West Bengal 700156, India https://orcid.org/0000-0001-6737-8321
Sushil Kumar Upadhyay Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, 133207, Haryana, India https://orcid.org/0000-0002-1229-4275
Rippin Sehgal Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, 133207, Haryana, India https://orcid.org/0009-0009-4026-5728
Raj Singh Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, 133207, Haryana, India https://orcid.org/0000-0002-6331-9196

DOI:

https://doi.org/10.18006/2023.11(4).720.735

Keywords:

Simulated Acid Rain, Seed Germination, Brinjal, Cowpea, Decision Tree Algorithm, Predictive Model

Abstract

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

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