Intelligent Shiny Dashboard for District-wise Crop Suggestion in Tamil Nadu

Agriculture plays a significant role in people’s lives, especially for farmers. The success of getting high yields from the crops is influenced by various factors, including climate and rainfall. Therefore, utilizing machine learning techniques can assist in determining suitable crops for specific regions, considering influential factors, and improving crop yields. This work aims to develop an intelligent dashboard using the Shiny framework that provides crop suggestions for all districts in Tamil Nadu. The prediction model used in the shiny framework is built using XGBoost, which provides high accuracy in decision making problems and is efficient at handling huge datasets. The dataset consists of climatic parameters and a 13-year history of crops cultivated in each district of Tamil Nadu. The current data of the influential factors are fetched using APIs and fed to the model in order to predict the favourable crop for each district. By leveraging the obtained results, farmers can make informed decisions regarding crop selection, ultimately leading to improved crop yields.