Authors: Yash Pratap Singh, Tanya Dwivedi
Abstract: Agriculture plays a major role in the economy and livelihood of many people, especially in developing countries. Farmers often face difficulties in choosing the correct crop because soil nutrients, weather conditions, and rainfall vary from place to place. Choosing the wrong crop can reduce yield and lead to financial loss. To solve this problem, a machine learning based crop recommendation system can be used. This system analyzes soil features such as Nitrogen (N), Phosphorus (P), Potassium (K), pH value, and environmental factors like temperature, rainfall, and humidity. Based on these inputs, the system suggests the most suitable crop for cultivation. In this research, different machine learning algorithms are studied, and Random Forest is selected theoretically because it provides high accuracy and stable performance. The main aim of this study is to support farmers in making better decisions, reduce risk, and improve productivity. The proposed approach is simple, understandable, and can be further developed into a mobile or web application for real-world use.
DOI: http://doi.org/10.5281/zenodo.17708787
