Air pollution has turned to no less than a monster and is becoming notorious with every passing day. The human race has been solely responsible for taking the world to such a state. With ill effects of pollution becoming glaringly evident it has been instrumental in forcing the world to get up from the deep slumber and act out. Air quality monitoring is a process in which the quality of air is monitored and on the basis of recorded information, it is conveyed to general public about the quality of air they are breathing. Air pollution poses serious problems to persons suffering respiratory disorders and there is a necessity to provide such target group with a tool which helps them to be aware about the pollution scenario and also alarms them with the impending critical situation well in advance. It is elementary for them to avoid situations where in lies a chance of exposure to pollutants leading to attacks which could prove to be fatal at times. This advance information will go a long way in helping such target audience to minimize their exposure to pollutants and thereby helping them to mitigate their ordeal on exposure to pollutants. Apart from getting predictive alarm, it gives a fair idea of the existing pollution scenario to the targeted stakeholders. This work discusses the implementation of cloud based IoT system for air quality monitoring which is available as a web interface as well as in a form of an android application.The developed system uses Nitrogen Dioxide, Sulphur dioxide, Particulate Matter 10 micrometers or less in diameter (P.M.10) sensors along with the temperature and the humidity sensors to form a wireless sensor node. An android application has also been developed which can be installed by the user. Once registered the user can access the data from the application which allows the users to observe the data of sensors along with the air quality index (AQI) and also provide the registered user with an alarm notification one day in advance about the probable level of pollutants as well as the AQI.a
In our suggested system, we employed a vast dataset that included all of India's states, whereas in the old system, just a single state was considered. These suggestions may be extracted and used to educate the farmers. The farmer can have a better understanding of the crops to cultivate by using a pictorial depiction. Machine Learning Techniques develops a well-defined model with the data and helps us to attain predictions. Agricultural issues like crop prediction, rotation, water requirement, fertilizer requirement and protection can be solved. Due to the variable climatic factors of the environment, there is a necessity to have a efficient technique to facilitate the crop cultivation and to lend a hand to the farmers in their production and management. This may help upcoming agriculturalists to have a better agriculture. A system of recommendations can be provided to a farmer to help them in crop cultivation with the help of data mining. To implement such an approach, crops are recommended based on its climatic factors and quantity. Data Analytics paves a way to evolve useful extraction from agricultural database. Crop Dataset has been analyzed and recommendation of crops is done based on productivity and season Keywords: Machine learning, Agriculture techniques, crop predictions
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