Soil Classification and Crop Suggestion Using Machine Learning

Abstract

Abstract: Agriculture, a cornerstone of our society, hinges onthe critical factor of soil for its success. Soil composition varies, influencing the growth of crops through its chemical features. Selecting the right crops for specific soil types is pivotal, requiring proper soil classification and informed crop selection for optimizing agricultural productivity. In this project, we strive to empower farmers by developing a system that seamlessly integrates soil classification techniques with crop suggestion algorithms. Leveraging advanced Machine Learning techniques, specifically Random Forest and K- Nearest Neighbors (KNN), we classify soil series data. These classifications are then harmonized with a comprehensive crop dataset to predict suitable crops for specific soil seriesin a given region, considering its unique climatic conditions. Our datasets encompass chemical and geographical attributes of both soil and crops, providing a holistic understanding. In the ever-evolving landscape of agriculture, Machine Learning emerges as a budding technology, promising to enhance productivity and elevate the quality of crops in our vital agricultural sector.