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Abstract: The shared specification of conceptual vocabulary used for formulating knowledge-level theories about a domain of discourse is known as Ontology. Data set is created by manually collecting information about different diseases related to crops, it's pesticides and weedicides. Ontology modelling is used for knowledge representation of various domains. Ontology extraction is a process in which important concepts related to a domain are extracted and relationships between them is formed. Ontology modelling is used for knowledge representation of various domains. Majority of Indian population relies on farming but the technologies are sparsely used for the aid and benefit of farmers. India is an agricultural based economic country. Ontology based modelling for agricultural knowledge can change this scenario. The farmers can understand it easily in their native languages like Marathi, Hindi or any other Indian languages. The Ontology Extraction system will model and extract knowledge in Marathi language. A review of various existing agriculture ontology along with some of Natural Language Processing (NLP) models is overviewed. The concept of NLP is useful for input processing and for human-computer interaction. Ontology model for agriculture domain system aims to retrieve appropriate answers to the farmers query and Rule-Based and Conditional Random Fields based models for Ontology extraction is explored. The extraction methods and pre-processing phases of proposed system is discussed.