A Data-Driven Framework for Crop and Fertilizer Recommendation in Smart Agriculture

Abstract

This research introduces a smart farming solution to aid farmers in selecting the most appropriate crops based on soil conditions and seasonal variations. Utilizing machine learning algorithms such as Random Forest and Decision Tree, the system analyzes soil parameters like nitrogen, phosphorus, potassium, pH, humidity, and rainfall to recommend optimal crops for cultivation. The model also supports fertilizer recommendations and detects plant diseases using image processing techniques. By integrating data mining with agricultural intelligence, this system empowers farmers to make data-driven decisions that enhance productivity and reduce crop failure risks. It ultimately aims to modernize traditional agricultural practices with a reliable, web-based application, improving crop yield and supporting sustainable farming.