Using Open Remote Sensing Data to build an Agriculture Big Data System

Landsat, MODIS, and Sentinel satellites are continuously producing multispectral sensor data with different spatial, temporal, and radiometric resolutions. This raw sensor data is calibrated and processed further, and additional data products are derived, which greatly reduces the burden for downstream applications from preprocessing these data. These petabyte-scale datasets are available to anyone free of charge. Remote sensing plays a key role in modern Agriculture. We can extract information about Soil, Weather, Water, and vegetation from these datasets. By processing historical remote sensing data, we can build temporal profiles of soil, weather, water, and agricultural conditions of the land. Deep learning and Spatio-temporal data mining algorithms can be applied to this data to extract hidden information. Having access to all this information via an agriculture information system, farmers will understand their land better and they will be empowered to make better decisions on a day-to-day activity. Although it looks simple from the surface, collecting, analyzing, and deriving insights from these sensor data and other data products from a multitude of sources is a big data and high-performance computing challenge. In this paper, we discuss the current open datasets and how these datasets can be used to solve various problems in agriculture. Also, we discuss implementing a cloud-based scalable agricultural information system which provides actionable insights to farmers.

AQI Predication Using Temporal Data Mining

The Air Quality Index (AQI) is an air quality standards indicator based on air pollutants that have negative impacts on human health and the environment. Because of several human activities, air pollution is growing very quickly, and it is the introduction of chemicals, particulate matter or biological materials into the atmosphere that cause human suffering and also harms the natural environment. Indeed, air pollution in metropolitan and industrial cities is one of the major environmental problems. So predicting pollution and avoiding these issues is very crucial. One of the most exciting and difficult functions is the forecast of air pollution using data mining. Many systems are designed to help data storage, inventory management and convenient statistics generation. India's air quality indicator is a standard measure used to indicate pollution (so2, no2, rspm, spm, etc.) from time to time. The main purpose of the current study is to predict the temporal AQI used by the previous day AQI and climate change is used to predict and visualize the temporary data mine using a gradient break and an unreasonable forecasting process. In Navigation Forecast, we divide the database into 85% data and 15% data based on data testing and training to determine seasonal variations and styles. Balance problems are often exploited by problems and forecasting uses an unreasonable prediction process and gradient downtime. Air quality forecasts based on historical data of previous years and predictions for less than a year as a reputable gradient using a recurring problem.

AQI Predication Using Temporal Data Mining

The Air Quality Index (AQI) is an air quality standards pointer based on air pollutants that have negative impacts on human health and the environment.Due to many human achievements, air pollution is increasing very rapidly and it is the introduction of chemicals, particles or organic materials into the atmosphere that harms the human environment and the natural environment.Indeed, air pollution in metropolitan and industrial cities is one of the major environmental problems. Therefore it is very important to predict pollution and avoid these problems.One of the most exciting and difficult functions is the forecast of air pollution using data mining. Many systems are intentionally help with data storage, inventory management, and convenient data creation.India's air quality indicator is a standard measure used to indicate pollution (so2, no2, rspm, spm, etc.) from time to time. The main objective of the current study is to estimate the temporal AQI used by the previous day AQI and to predict and visualize the temporal data mine using a slope interval and an arbitrary forecasting process of climate change. In Navigation Forecast, we divide the database into 85% data and 15% data based on data testing and training to determine seasonal variations and styles. Balancing problems are often exploited by problems and forecasting uses an arbitrary forecasting process and gradient idle time. Air quality forecast based on at least one year's forecast as a reliable slope using historical data of previous years and a persistent problem.