Quadratic-radial-basis-function-kernel for classifying multi-class agricultural datasets with continuous attributes

Abstract Classification of agricultural data such as soil data and crop data is significant as it allows the stakeholders to make meaningful decisions for farming. Soil classification aids farmers in deciding the type of crop to be sown for a particular type of soil. Similarly, wheat variety classification assists in selecting the right type of wheat for a particular product. Current methods used for classifying agricultural data are mostly manual. These methods involve agriculture field visits and surveys and are labor-intensive, expensive, and prone to human error. Recently, data mining techniques such as decision trees, k-nearest neighbors (k-NN), support vector machine (SVM), and Naive Bayes (NB) have been used in classification of agricultural data such as soil, crops, and land cover. The resulting classification aid the decision making process of government organizations and agro-industries in the field of agriculture. SVM is a popular approach for data classification. A recent study on SVM highlighted the fact that using multiple kernels instead of a single kernel would lead to better performance because of the greater learning and generalization power. In this work, a hybrid kernel based support vector machine (H-SVM) is proposed for classifying multi-class agricultural datasets having continuous attributes. Genetic algorithm (GA) or gradient descent (GD) methods are utilized to select the SVM parameters C and γ. The proposed kernel is called the quadratic-radial-basis-function kernel (QRK) and it combines both quadratic and radial basis function (RBF) kernels. The proposed classifier has the ability to classify all kinds of multi-class agricultural datasets with continuous features. Rigorous experiments using the proposed method are performed on standard benchmark and real world agriculture datasets. The results reveal a significant performance improvement over state of the art methods such as NB, k-NN, and SVM in terms of performance metrics such as accuracy, sensitivity, specificity, precision, and F-score.