Accurate and fast detection of tomato diseases is critical for good crop management and sustainable agriculture, as illnesses identified correctly and promptly can lead to early treatment of the plants. Due to the complexity and similarity of different tomato diseases and pests in the natural environment, there is a need for state-of-the-art object detection models to meet the demands for real-time and accurate detection. YOLOv8 and RT-DeTR are the most advanced object detection models, boasting great accuracy and real-time detection speeds. This research aims to compare the efficacy of the YOLOv8 model and the RT-DeTR model in tomato leaf disease detection. Our dataset is acquired using two methods: selecting from Kaggle a dataset containing a single tomato leaf in each image and capturing real-life photographs of tomato leaves in Da Lat City. Additionally, the dataset is augmented using several approaches, such as left and right rotation, blurring, and contrast adjustment. Various training strategies are utilized such as fine-tuning the pre-trained models using our dataset and experimenting with different optimizers, learning rates, weight decay value, and number of epochs. The numerical results show that RT-DeTR has a better precision score but detects significantly slower than YOLOv8. Moreover, YOLOv8’s inferred results make slightly less leaf detection and accurate classification than RT-DeTR, whose results also occasionally beat YOLOv8's detection confidence score.