Automatic Detection of Tea Leaf Diseases using Deep Convolution Neural Network

In India, tea is the most consumed and popular beverage, all over the world too. It is one of the popular and widely cultivated perennial plantation crop in our state. Their productions are heavily affected and destroyed by different diseases. Deep learning is a machine learning technique that teaches a computer with the help of pre-existing data and enables the system to do what comes naturally to humans. The main challenge faced in deep learning is that it needs a large amount of training data. In this model, we are going to propose a unique idea to detect and classify diseases in tea leaves by incorporating deep learning techniques. The critical processes in this tea leaf disease classification are health monitoring and disease detection and are essential for sustainable agriculture. Manually observing the tea leaf diseases is a tedious and time taking processthat requires skilled workers, extra time, and manpower with knowledge about tea leaf diseases. Hence, image processing models were widely utilized for these kinds of disease detections. In this proposed work, we have applied the Convolutional Neural Network(CNN) model with 1 input layer, 4 convolution layers, and 2 fully connected layers. The image is passed to the input layer. The convolution layers mainly extract features from the input image in the dataset and the output layer classifies the given image to 8 classes such as the normal leaf, Algal leaf spot, Gray blight, White spot, Brown blight, Red scab, Bud blight, and Grey blight.