Quality assessment of black tea based on physical parameters using machine vision

Abstract

Tea is a valuable cash crop throughout the world. It is a major export product of India. As far as social aspect is concerned, about 1.2 million people are directly employed as labour in tea industry. This constitutes a large proportion of human resource of the country. Quality of tea plays a significant role in its marketability as international export price of tea is fixed according to its quality. At present, tea quality is validated by professional Tea Tasters who charge exorbitantly for every sip they take. Conventionally, these experts evaluate tea quality by use of organoleptic methods during fermentation and sorting stage. In addition to this, gas chromatography and colorimetery are employed for chemical analysis of tea liquor and for colour analysis, respectively, at various stages of tea processing. These conventional methods have many shortcomings. First of all, being small in number, the Tea Tasters are difficult to hire and there is every possibility of formation of a cartel by them. Their evaluation methods are subjective and suffer from high labour costs, inconsistency and variability. The prominent physical parameters that establish tea quality include colour, texture, grain shape and size. The approach of Tea Tasters does not quantify these parameters and hence, it is difficult to correlate various parameters of tea for assessment of tea quality. Increasing competition and concerns about tea quality leading to rejection of export orders has resulted in substantial fall in tea export from India in the recent past and consequently, tea industry of India is slowly dying. If proper measures are not adopted and lessons not learnt from the past, situation may aggravate in future. There is a dire need to carry out research in this field so as to meet requirements of global standards. There has been lack of research specifically related to grading and quality assessment of tea all over the world. The above issues are aptly addressed by machine vision based techniques.