CROP RECOMMENDATION USING MACHINE LEARNING

The proposed crop recommendation system leverages machine learning techniques to support farmers in making informed crop selection decisions. It analyzes essential parameters such as soil pH, nutrient content, rainfall, and climate conditions to determine the most suitable crops for cultivation in a given region. By processing this data, the system improves agricultural planning, promotes water-efficient practices, and helps reduce the risk of crop failure caused by climatic uncertainty. This research assesses various machine learning algorithms and performance metrics, comparing different crop recommendation approaches using a publicly available dataset from www.kaggle.com. In this study, multiple machine learning algorithms were compared using a publicly available dataset containing 22 crop types. Among the tested models, the Random Forest classifier demonstrated the highest prediction accuracy of 99.31%, making it a promising tool for practical agricultural applications.