Krishi Sewak

Abstract

Agriculture wears around 17% on Indian GDP and employs more than 60% of the country's population. Despite technological advances such as vertical agriculture, many Indian farmers continue to rely on traditional practices, which are often less productive due to unpredictable weather patterns. This study proposes a harvest recommendation system that uses machine learning models to enable farmers to select the best plant based on environmental factors such as soil nutrients, pH, humidity, and precipitation. A variety of machine learning techniques such as Decision Trees (DT), Support Vector Machines (SVM), Logistic Regression (LR), and Gaushenive Bayes (GNB) are used to predict the best crop selection for a variety of scenarios.