The intelligent systems specialized in monitoring plant growth have entered into precision agriculture, and integrated systems have been built that contain built-in cameras within these systems. This monitoring takes place over varying periods and results in many captured images. The monitored plants may be unhealthy and infected with bacteria. However, some are healthy, leading to significant damage as these bacteria move around and spread to healthy plants. From this perspective, a proposed system based on deep learning is developed to classify plant images into healthy and unhealthy categories. The images from the observation are usually random and need to be more organized. Hence, using classification algorithms helps extract organized and arranged image data for easier access according to labels. This process enables us to save time and effort by monitoring a plant’s leaves. The proposed model achieved 87% accuracy on the plantvillage tomato leaf dataset, consisting of one healthy class and nine different unhealthy classes.