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

Abstract

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.