PREDICTION AND CLASSIFICATION OF TEA LEAF DISEASES USING DEEP LEARNING TECHNIQUES ELeNet

Abstract

Tea is the most critical beverage of people next to the water. So, tea production is essential in India. To increase the crop, yield early-stage diagnosis of tea leaf disease is essential. Generally, Tea leaves are affected by 100 types of diseases, but 15 of which occur in leaf and buds. Among these disease blister blight, anthracnose, brown blight, red leaf spot and fungal leaf spot, white scab, grey blight may affect the quality and quantity of the tea production. So, data mining plays a vital role in the early diagnosis of tea leaf disease and assess the crop yield. Diagnosis of plant disease is typically based on disease characteristics. Developing and implementing a diagnostic structure for tea plant illnesses would, therefore, assist farmers in ensuring precise and timely identification of tea plant illnesses. Such improvements would lead to better techniques of control that would restore problems due to disease economically and efficiently. This research starts the new method to predict and classify the tea leaves disease. Finally, this research forecast crop yield by identifying the major diseases that are limiting yield. For this the following objectives are framed