Agriculture is the largest and most vital sector of our economy, providing employment to over 70% of the Indian population. In an agricultural field identification and classification of disease in a tomato plant plays a vital role. If proper care is not taken in advance with this, it causes the serious effect on the tomato plants. The plants which are affected by the disease reflect on the quality and quantity of the production as well as the economy of the country. Tomato is one of the main vegetables in India which plays an important role in providing food for public needs and it’s a very vulnerable plant to insects and diseases caused by different pathogens like bacteria, virus, and fungi. This paper proposes a convolutional neural network (CNN) for automatic identification of plant disease caused by fungi. The proposed model attains the results of F1 score is 91% to differentiate tomato leaves in both healthy and unhealthy conditions. Measures like a high Area Under Curve (AUC) score on the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, and specificity show the model’s remarkable performance. The practical uses of this discovery for early illness detection in agriculture make it significant. With its exceptional specificity and precision, ResNet-18 serves as a potential tool for agriculture.