Predictive Analysis of Digital Agriculture

Download Project Document/Synopsis

Weather simply refers to the condition of air on earth at a given place and time. The application of science and technology are used to predict the state of the atmosphere in future time for a given location and is so important due to its effectiveness in human life. Today, weather forecasts are made by collecting quantitative data about the current state of the atmosphere and using scientific understanding of atmospheric processes to project how the atmosphere will evolve. The chaotic nature of the atmosphere implies the need of massive computational power required to solve the equations that describes the atmospheric conditions. This is resulted from incomplete understanding of atmospheric processes which means that forecasts become less accurate as the difference in time between the present moment and the time for which the forecast is being made increases. Weather is a continuous, data-intensive, multidimensional, dynamic and chaotic process and these properties make weather prediction a big challenge. Generally, two methods are used for weather forecasting
(a) the empirical approach and
(b) the dynamical approach.
The first approach is based on the occurrence of analogs and is often referred by meteorologists as analog forecasting. This approach is useful for predicting local-scale weather if recorded data are plentiful. The second approach is based on equations and forward simulations of the atmosphere and is often referred to as computer modelling. The dynamical approach is only useful for modelling large-scale weather phenomena and may not forecast short-term weather efficiently. Most weather prediction systems use a combination of empirical and dynamical techniques Artificial Neural Network (ANN) provides a methodology for solving many types of nonlinear problems that are difficult to be solved by traditional techniques. Most meteorological processes often exhibit temporal and spatial variability. They are suffered by issues of nonlinearity of physical processes, conflicting spatial and temporal scale and uncertainty in parameter estimates.



Advantages
  • Neural networks are in forecasting the weather and the working of most powerful prediction algorithm called Naïve Bayes algorithm.
  • User would get to know the climatic condition based in historical data.
  • User could get to know which crop to be grown for particular climate
Disadvantages
  • Admin needs to keep inserting climatic data for prediction results
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