In modern agriculture, monitoring plant health in large nurseries, organic farms, or plantations requires substantial investments in time, labor, and money. To address this challenge, proposed an automated bot system designed to reduce these costs by efficiently monitoring plant health and managing diseases. The bot is equipped with a camera module and a custom deep learning model (CNN) to preprocess leaf images from each plant. For binary classification, where the labels are healthy and diseased, the model achieves an accuracy of 99.37%. In a multiclass setting, distinguishing between healthy, early blight, and late blight, the model achieves an accuracy of 93%. If a plant is classified as diseased, the bot automatically takes mechanical action, such as spraying general pesticides. The bot functions as a line follower, navigating through predefined tracks between rows of plants to monitor and classify each plant, ensuring precision in disease detection and intervention. The proposed automated bot system represents a promising step towards precision agriculture, offering farmers a practical and scalable solution for plant health management.