Crop Recommendation Using Machine Learning Based on Soil, Weather, and Other Agronomic Factors

Agriculture remains a critical sector for food production and rural livelihood across the globe. However, climate

change, unpredictable rainfall patterns, declining soil fertility, and improper crop selection often lead to reduced agricultural

productivity. Traditionally, farmers rely on experience or local knowledge for crop selection, which may not always result in

optimal yield. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), there is a significant opportunity to

revolutionize agriculture by providing intelligent crop recommendation systems. This research focuses on developing a machine

learning-based crop recommendation model that utilizes key parameters such as soil nutrient composition (Nitrogen,

Phosphorus, Potassium), temperature, humidity, pH level, and rainfall. Various supervised learning algorithms were employed,

including Decision Trees, Random Forests, and Support Vector Machines, to predict the most suitable crop for a given set of

environmental and soil conditions. The Random Forest algorithm emerged as the most accurate with an average prediction

accuracy of over 96%.