Automated Identification of Tomato Plant Disease using CNN based Deep Learning Model

Tomato plants can catch a lot of different diseases very easily. Early identification of these diseases plays an important role in effective agricultural management. The traditional detection method depends mainly on manual inspection by experts, which not only takes long time but also has a high chance of errors. An automated tomato plant disease identification system is proposed based on Convolutional Neural Network (CNN) in this paper. The model recognizes between healthy and diseased plants from images of tomato leaves. Training uses a publicly available dataset and evaluating the model, the experimental results show that the CNN based approach gives reliable and efficient disease classification. This method helps in identifying tomato plant diseases at an early stage and it can support farmers for taking better agricultural decisions, improving crop health and reducing losses in farming.