Plant diseases, leading to resultant severe financial losses and food shortages, are one of the greatest threats faced by agricultural production. Our good treatment and prevention of most diseases is conducted through timely and accurate diagnoses of all diseases. This paper determines the identification and classification of plant leaf diseases based on a deep learning technique by employing the ResNet50 convolutional neural network (CNN). Photos of plant leaves are processed, and the model ResNet50 which is widely recognized because of its effectiveness in identifying images in classification tasks, classifies the leaves on the bases of healthy and unhealthy. One may train the model using a large sample of various leaf images depicting various types of plants and illnesses. We use the ResNet50 architecture with transfer learning in order to deduce a form that can best perform in our specific dataset, and it can accurately perform with minimal processing overhead. Due to these reasons, the model can be effective in the real-life setting, providing farmers and other professionals operating in the agricultural sector with a viable alternative to use. Another factor that the study explores is incorporating the model into a user-friendly software that would allow users to post some pictures of their leaves and get real-time responses on the status of healthiness of their plants. Besides making use of proactive crop health monitoring opportunities, such an automated process makes it possible to implement early interventions that can limit the effects of diseases. This study eventually seeks to recommend sustainable agricultural practices with the addition of an easily accessible, well-organized, user-friendly tool for the diagnosis and classification of diseases of planting.