International Research Journal of Education and Technology

Tea is one of the main agricultural industries in worldwide, and is available in various forms of consumer beverages. Green tea is richer in antioxidants compared to other forms of tea. Tea is composed of polyphenols, caffeine, minerals, and trace amounts of

vitamins, amino acids, and carbohydrates. The problem that often attacks green tea plant is Exobasidium vexans which causes a disease called blister blight. The detection of disease can be identified manually but it needs huge processing time and accuracy of plant disease is need. Hence, there are various ways have developed to predict diseases in tea plant which is more efficient and accuracy in recent technology advances, and one of the way is to apply in this study are machine learning. The crop field environmental data is captured by developing an IoT-based hardware prototype. The environmental conditions like temperature, humidity, and rainfall are used to determine the probability of the occurrence of a blister blight disease attack on tea plants. This review discusses a summary on tea plant diseases by machine learning which are used for identifying blister blight disease.

Keywords: Polyphenols, Caffeine, Exobasidium vexans, Carbohydrates.