Machine Learning in Soil Classification and Crop Detection

This paper describes the SVM based classification and grading of soil samples using different scientific features. Different algorithms and filters are developed to acquire and process the colored images of the soil samples. These developed algorithms are used to extract different features like color, texture, etc. Different soil types like red, black, clay, alluvial, etc are considered. The classification makes use of Support Vector Machine, machine learning technique. SVM seeks to fit an optimal hyper plane between the classes and uses only some of the training samples that lie at the edge of the class distributions in feature space (support vectors). This should allow the definition of the most informative training samples prior to the analysis. The accuracy of a supervised classification is dependent to a large extent on the training data used. Till now classification of soil and classification of crop for the appropriate soil is done separately. This project aims at combining both the techniques, where classification of crop for appropriate soil is a part of classification of soil.