在农业领域利用物联网和机器学习进行空中化学物质检测

IF 0.6 Q4 AUTOMATION & CONTROL SYSTEMS
Anju Augustin,  Cinu C. Kiliroor
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引用次数: 0

摘要

农业是每个国家的支柱。一个国家的发展只有在农产品随人口增长而增加的情况下才是完整的。但是,由于气候变化和虫害对农作物造成的巨大损失,这一比例往往无法保持。因此,当今农业中使用了大量杀虫剂和化学品。大量使用化学品不仅会影响农作物,还会影响空气。化学品的使用对空气污染有很大影响,会导致呼吸道疾病和各种过敏症。因此,需要一种方法来实时检测空气中的这些化学物质。本文提出了一种基于物联网的系统,该系统使用两个传感器来测量不同有害化学物质的浓度水平,并使用两种机器学习算法逻辑回归和支持向量机(SVM)来预测空气污染的风险。该系统利用传感数据计算空气质量指数(AQI)。建议的系统将对官员和普通人了解特定地区的空气质量非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Airborne Chemical Detection Using IoT and Machine Learning in the Agricultural Area

Airborne Chemical Detection Using IoT and Machine Learning in the Agricultural Area

The agriculture sector is the backbone of every country. The growth of a country is complete only if there is an increase in agricultural products following the increase in population. But this ratio is often not maintained due to climate change and pest attacks causing huge crop damage. Therefore, a large amount of pesticides and chemicals are used in agriculture today. Massive chemicals application not only affects the crops but also the air. The use of chemicals has a large impact on air pollution, which causes respiratory diseases and various types of allergies. Therefore, a method is needed to detect these chemicals in the air in real-time. Here proposes an IoT-based system that uses two sensors to measure concentration levels of different harmful chemicals and two machine learning algorithms logistic regression, and support vector machine (SVM) to predict the risk of air pollution. Using the sensed data, the system calculates the air quality index (AQI). The proposed system will be very useful for officials as well as common people to find the quality of air in a particular area.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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