Application of Data Mining Technologies for Crop Selection Based on Environmental Variables

Abstract

Sustainable agriculture is a necessity to overcome global and local food security challenges. At the same time, productive agriculture is also important to enhance the socio-economic status of farmers. Integrating modern technologies with the agricultural sector was identified as one of the most important solutions to overcome many issues. Therefore, we aimed to apply machine learning technologies to identify the most suitable crop types for productive farming. Badulla district was the focused area for these studies. Potato, tomato, green gram and red onion were the selected crop types. Rainfall, minimum and maximum temperature, minimum and maximum relative humidity were the selected weather conditions. Wholesale price and retail price of each of the above crop type were considered crop prices. Locations were specified as gramasewa divisions and their soil types were considered. CRISP-DM methodology was followed throughout the research. Weka libraries were integrated with Java programming language for the implementation and MYSQL database was used with JDBC database connector to maintain the data. Data mining classification technologies were trained and tested in different conditions while performances were evaluated using mean absolute error values and root mean squared error values. M5P model tree and Random forest tree performed comparatively better performances in weather forecasting and crop prices forecasting. In the system, farmers have to select the relevant gramasewa division for their farming location. The system will identify specific soil types in the relevant land and identify suitable crop types. Then, predicted weather conditions are compared with required weather conditions for each crop. Finally, the crop prices were evaluated. According to the results, the higher-ranking crop list was provided to farmers as the output. These results may help in decision making in the crop selection process while contributing to change the field of agriculture as a profitable industry.