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Biodegradation of pesticides using density-based clustering on cotton crop affected by Xanthomonas malvacearum

机译:基于密度的聚类分析法对棉花黄单胞菌感染棉花的生物降解

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摘要

In today's world, cotton is the most vital non-food agrarian commodity. During its growth, it is prone to various kinds of pests and bacteria which causes many diseases. And to save the cotton crop from bacteria and pests, various harmful chemicals in the form of pesticides are used. Over the years, due to the extensive use of pesticides, their interaction with the biological system in the environment has caused many problems. After analyzing the harmful effects of pesticides, it is essential to remove these harmful chemicals from the environment to plant the other seasonal crops. To accommodate this constraint, this paper proposes a method to efficiently use pesticides on the cotton crop which is affected by Xanthomonas malvacearum and how to biodegrade the pesticides from the soil where it was grown. To rectify this instance, we use a methodology named density-based clustering for finding the areas of the cotton plant that were affected by Xanthomonas malvacearum. On the other hand, the same algorithm is used to identify the areas in the soil that were highly affected by pesticides. By identifying the affected areas in the field, the methods such as biostimulations, bioventing, bioaugmentation, and other related techniques can be applied to degrade the levels of pesticides. It can be incurred from experimental results that the density-based method outperforms in identifying the pesticide-affected areas which will help the farmers to make the crop production effectively.
机译:在当今世界,棉花是最重要的非粮食农业商品。在它的生长过程中,很容易引起各种病虫害和细菌。为了使棉花作物免受细菌和害虫的侵害,使用了多种农药形式的有害化学物质。多年来,由于农药的广泛使用,其与环境中生物系统的相互作用已引起许多问题。在分析了农药的有害影响之后,必须从环境中除去这些有害化学物质以种植其他季节性作物。为了适应这一限制,本文提出了一种在棉花农作物中有效使用农药的方法,该作物受到了黄单胞菌的影响,以及如何从种植土壤中对农药进行生物降解。为了纠正这种情况,我们使用一种称为基于密度的聚类方法来查找受黄单胞菌侵染的棉花植物区域。另一方面,使用相同的算法来确定土壤中受农药高度影响的区域。通过确定现场受影响的地区,可以采用诸如生物刺激,生物通风,生物强化和其他相关技术之类的方法来降低农药水平。从实验结果可以看出,基于密度的方法在确定受农药影响的地区方面表现优于其他方法,这将有助于农民有效地进行农作物生产。

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