Blister Blight Disease of Tea: An Enigma

Tea is one of the most popular beverages consumed across the world and is also considered a major cash crop in countries with a moderately hot and humid climate. Tea is produced from the leaves of woody, perennial, and monoculture crop tea plants. The tea leaves being the source of production the foliar diseases which may be caused by a variety of bacteria, fungi, and other pests have serious impacts on production. The blister blight disease is one such serious foliar tea disease caused by the obligate biotrophic fungus Exobasidium vexans. E. vexans, belonging to the phylum basidiomycete primarily infects the young succulent harvestable tea leaves and results in ~40% yield crop loss. It reportedly alters the critical biochemical characteristics of tea such as catechin, flavonoid, phenol, as well as the aroma in severely affected plants. The disease is managed, so far, by administering high doses of copper-based chemical fungicides. Although alternate approaches such as the use of biocontrol agents, biotic and abiotic elicitors for inducing systemic acquired resistance, and transgenic resistant varieties have been tested, they are far from being adopted worldwide. As the research on blister blight disease is chiefly focussed towards the evaluation of defense responses in tea plants, during infection very little is yet known about the pathogenesis and the factors contributing to the disease. The purpose of this chapter is to explore blister blight disease and to highlight the current challenges involved in understanding the pathogen and pathogenic mechanism that could significantly contribute to better disease management.

Recognition of Blister Blight Disease in Tea Leaf Using Fully Convolutional Neural Networks

Tea plantation contributes significantly to the agricultural economy of India. Automatic tea leaf disease detection is beneficial when compared to manual detection which is not only a tedious grueling task but also less accurate and time consuming. This paper presents an alternative image segmentation technique that can be used for automatic detection and classification of blister blight diseased tea leaf using a fully convolutional neural network (CNN) based method to segment blisters in a tea leaf image. The suggested technique proves to be beneficial in monitoring large fields of tea crops.