In the present-day, crop recommendation is of utmost importance in methodical agricultural practices. The data available in the domain is of a surplus amount, but the knowledge-centric paradigms are scarce. Thereby, in this age of Web 3.0, Crop recommendation is one of the crucial aspects of the digital transformation of agriculture. As a result, An Ontology-driven and Knowledge centric favorable crop recommendations are of extreme value. This work puts forth an Ontology-based semantically directed, favorable crop recommendation system with a selective Knowledge Stack. The system uses Lin similarity, Wu Palmer Index, Cosine similarity and van Belle and Ahmad Index as semantic measures. Support Vector Classifiers, the Random Forest algorithm and AdaBoost is used for Bagging and Classification of the generated feature pool. The experiments have been conducted for Knowledge Stack using Wikidata that were obtained from various agricultural websites and an accuracy of 96.3% is achieved by the approach

AdaBoost, Bagging, Cosine Similarity, Knowledge Stack, Van Belle and Ahmad Index