Abstract:
There is an emerging consensus that sustainable and more productive agriculture is
needed to encounter the local, regional and global food security challenges. As the
environmental factors are changing rapidly and questions regarding the selection of the
crop is very important. This consensus implies significant emergence in new and
improved tools that can be used to ensure the sustainability. Therefore, this project
proposes to provide real-time information to support farmers to make decision on the
crop selection and also to identify the hidden relationships among the environmental
variables and the crop, hence improving the science behind predictive tools.
Agricultural technology is dependent on the prediction about weather, diagnosis of
fertilizers in soil, type of crop and environmental issues. Precisely suitable soil types are
selected for a particular crop types. Weather may be classified on the basis of
temperature, rain and humidity. Producer prices are considered as other attributes
related to environment.
As the first level Monaragala and Badulla districts are the areas taken into consideration.
Maize, Potato, Tomato, Green gram and Red onion are selected as crop types. Rainfall,
humidity, temperature, soil pH, soil types and producer price are some of the inputs for
this prediction model. The most appropriate crop set for the relevant land output is the
predicted output.
The purpose of this project is to address the literature by exhibiting an efficient model
that stores agricultural data in proper and efficient manner to response ad-hoc queries of
the farmers while providing a user interface which would provide most appropriated crop
list for the relevant land.
