酸雨与种子萌发:基于ML-based CART算法的预测模型

Q4 Veterinary
Vasundhara Arora, Bikram Jit Singh, Navneet Bithel, Tapan Kumar Mukherjee, Sushil Kumar Upadhyay, Rippin Sehgal, Raj Singh
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引用次数: 0

摘要

酸雨对种子发芽的影响是农业和环境研究中的一个重要问题。酸雨的特点是由于二氧化硫和氮氧化物等污染物导致酸度升高,会对各种植物的发芽过程产生不利影响。研究了模拟酸雨(SAR)对茄子(Solanum melongena Linn.)和豇豆(Vigna unguiculata ssp.)萌发的影响。白茅属(L. Walpers)作物。实验使用8个约25厘米的塑料托盘进行。尺寸x30厘米。4个托盘用于茄子种子(Set I)的实验,另外4个托盘用于豇豆种子(Set II)的实验。每组1个托盘作为阳性对照,用正常pH 5.6处理,每批3个托盘用pH 4.5、3.5和2.5的SAR溶液处理。茄子种子发芽率和种子活力低于豇豆种子。pH为4.5、3.5和2.5的SAR处理对种子萌发有抑制作用。此外,在较低的pH值下观察到更显著的抑制作用。在标准SAR (pH 5.6)下,茄子种子的平均发芽率最高,豇豆种子的平均发芽率最低。结果表明,植物对SAR的响应并不一致。为了研究模拟酸雨数据的行为,采用基于机器学习的决策树算法来识别和优化条件。结果表明,在pH为5.05的酸雨条件下,豇豆种子萌发率为95%,而茄子种子萌发率仅为64%。总之,利用基于机器学习的CART算法为预测酸雨影响下种子的发芽行为提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Acid Rain and Seed Germination: A Predictive Model Using ML-based CART Algorithm
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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来源期刊
Journal of Experimental Biology and Agricultural Sciences
Journal of Experimental Biology and Agricultural Sciences Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
1.00
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127
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