SMART UI: Crop Classification in Karnataka Nippani Using Ensemble Random Forest

This study helped in enhancing agricultural management, predicting yields, tracking crop health which are all dependent on accurate crop classification. But the conventional methods are labor intensive and time consuming. The goal of this study is to automate crop classification system that uses machine learning and satellite image to accurately classify the crops. Ground Truth data is gathered from data.gov.in website and Random Forest classifier is used to train on data. To be more accurate the algorithm is verified using dataset from specific regions. The Random Forest effectively classified crops with an accuracy of $79 \%$. The outcome shows how well crop classification works when used with machine learning and satellite image together. This study provides scalable algorithm for crop classification, which helps in various agricultural related works like crop health. The future studies should investigate alternative use of real-time data for dynamic classification.