Smart Agriculture: Machine Learning Approach for Tea Leaf Disease Detection

Across the globe, plant infections from pathogens such as fungi, bacteria and viruses are the major issues in the agricultural sector. Agricultural productivity is one of the most important things on which the nations' economy highly depends. The detection of diseases in plants plays a major role in the agricultural field. This study proposes a multi-stage network involving Convolutional neural network, Pattern identification and Classification using Siamese network. The main objective behind this study is to enhance the disease detection technique performance. The image data of Tea leaves chosen for this study will be gathered. The algorithms based on techniques of image processing would be designed. The proposed algorithm was tested on the following diseases namely Red rust, Blister blight, Twig dieback, Stem canker, Grey Blight, Brown Blight, Brown root rot disease and Red root rot disease in Tea leaves.