Tomato plants are vulnerable to numerous diseases, which can have significant impact their growth and yield. Early discovery and accurate analysis of these diseases are critical for implementing timely and operative control measures. In modern years, deep learning-based computer visualization methods have emerged as powerful tools for real-time tomato leaf disease recognition and diagnosis. The system utilizes a convolutional neural network (CNN) model to repeatedly categorize tomato leaves as healthy or diseased based on images taken in the field. The model is skilled on a dataset of labeled imageries of tomato leaves, with each image containing a specific disease or being healthy. The system is implemented on a portable device for real-time use in the field, allowing for rapid and accurate diagnosis of tomato leaf diseases. The investigational results show that the suggested system achieved high accuracy in disease classification, with an average precision of 98.11%. Furthermore, the system also successfully detects multiple diseases present in the same leaf, which is a significant improvement over traditional methods that often fail to detect multiple diseases. The proposed real-time tomato leaf disease detection and diagnosis system using deep learning-based computer vision procedures can effectively detect and diagnose multiple diseases in tomato leaves in real time. This technology can be used to improve the effectiveness and correctness of disease discovery, ultimately reducing the use of harmful pesticides and increasing crop yields. This chapter highlights the potential impact of real-time tomato leaf disease detection and diagnosis using deep learning-based computer vision performances. Early detection and diagnosis can facilitate timely interventions, leading to improved disease management, increased crop yield, and reduced reliance on chemical treatments.