Design of Tea Ontology for Precision-based Tea Crop Yield Prediction Using Machine Learning

Ontology plays a crucial role in machine learning by providing a structured representation of knowledge that can be used to facilitate learning and reasoning. Ontology is a classification of objects and the formal classification of a domain, which describes relationships that exist within that domain. Accurate and efficient assessment of tea crop yield prediction is crucial for any tea industry. This chapter introduces tea ontology (TO) to formalize and capture the expert's knowledge and to involve information from industry standards. The TO specifies classes of tea, their attributes, and relations so as to make the process of data curation easier. This chapter is an attempt to create ontology based on different parameters that affect tea cultivation and its various stages of processing. Such parameters as biotic stress; abiotic stress; morphological, agronomic, phonological, and physiological conditions; weather; and quality are discussed in this study. Using this ontology, it would be easier for anyone to curate data pertaining to tea crops, which can be further used by any machine learning model for precision- based predictions.