Crop Recommendation Based on Geographical Factors Using Machine Learning Approach

Precision agriculture aims to optimize crop yield and sustainability by leveraging advanced technologies. This study investigates the application of machine learning for recommending suitable crops based on geographical factors, including soil properties, climate conditions, and topographical features. By integrating these diverse datasets, the proposed machine learning model aims to enhance the accuracy of crop recommendations. Experimental results demonstrate the model's effectiveness in capturing complex interactions between the factors, leading to improved agricultural decision- making compared to traditional methods.