Agrisense- Tool for Soil Analysis and Crop Recommendation System

Abstract: As per the recent Indian Economic Survey, agriculture in India employed over half of the workforce available. Consequently, it is crucial to recommend crops those are most suitable for varying soil types and environmental conditions to promote sustainable agricultural practices. This goal can be achieved by utilizing ML, including DL algorithms for managing complex datasets and natural language processing techniques. This paper gives an overview and complete insight about studies and works done on soil analysis and crop recommendation systems to give idea about better algorithms for crop recommendation systems using the latest Machine learning also DL algorithms for better accuracy and efficiency, highlighting their significance, effectiveness, and practicality in crop recommendation systems, with the relevant datasets.

Keywords: Crop recommendation, Crop prediction, Fertilizer recommendation, Yield prediction, Rainfall prediction Soil analysis, Machine learning, KNN, Random Forest, Decision Tree.