Automation in horticulture with computer vision and deep learning revolutionizes the industry by enabling precise and efficient harvesting and disease detection. This technology enhances productivity and quality control, benefiting farmers and food manufacturers alike. The present work offers a comparative analysis of advanced deep learning detection models-YOLO V8, RT-DETR with ResNet and with EfficientNet backbones-focused on identifying diseased (canker) and healthy (fresh) oranges. A comprehensive evaluation of these three models was conducted to assess their performance. YOLO V8 emerged with superior results, showcasing its robustness and effectiveness for real-time detection. Amongst the 3models YOLO V8 attained the best accuracy value of 99.6%. The other performance metrics underscore the model's high accuracy and efficiency in identifying and classifying objects with minimal errors, affirming its suitability for real-time horticultural diagnostics. The comparative analysis highlights YOLO V8's superiority over RT-DETR models with ResNet and EfficientNet backbones, positioning it as a highly reliable choice for detecting healthy and diseased oranges. This work advances the field by proving the superior performance of YOLO V8 for this specific application and validating the performance with the other sophisticated deep learning architectures.