Automatic Tealeaf Disease Detection Using Machine and Deep Learning Method

Agriculture is a main source of livelihood. In everyday life, the tea plant is the most cultivatable plant in the highlands. Most farmers in India have turned to manual labor due to a lack of experience and technological understanding. When plants are infected with a variety of diseases, it has an impact on losses. In tea farming, the most profitable part of the plant is on the top. This leaf is quickly infected by illnesses. Farmers face a difficult task in identifying illnesses in tea plant leaves. Finding diseases in tea plant leaves is a challenge for the farmers. The purpose of this research is to build a CNN model using large datasets. To detect tea plant diseases from images, we developed CNN architecture, including Inception-V3, Googlenet, and Resnet-110. We also developed a machine learning model, the SVM classifier, to forecast diseases, prescribe pesticides, and compare machine and deep learning models.