Agricultural crop productivity data analysis with higher chemical usage with district classification

By attaining maximum crop output, the agricultural sector plays a crucial role in preserving food security. However, some farmers' overuse of pesticides and herbicides raises serious issues for the environment and public health. A suggested solution uses a dataset that links crop production tenure with actual yields in order to address this issue. The system analyses pesticide use and groups districts according to crop productivity in a short amount of time. This system provides accurate classification and comprehensive result analysis by utilising the Random Forest algorithm in conjunction with the Decision Tree. By means of graphical graph plots, the system displays chemical consumption trends, identifying areas that exhibit both high chemical consumption and high production levels. Focussing mostly on districts that use dangerous chemicals, especially in the Indian agriculture sector, this system seeks to provide stakeholders and policymakers with useful information. By implementing focused measures to reduce the risks associated with the use of chemicals in agriculture, these insights enable stakeholders to create a more secure and sustainable farming environment.