The advancement of deep learning techniques has significantly improved diseases detection on plants remote sensing images. However, this imaging process comes with challenges due to the complex nature of plants remote sensing images abnormalities. The vast range of anomalies in terms of kind, shape, and magnitude of lesions makes it challenging to effectively detect them in different situations. However, differentiating an early-stage disease from a healthy plant on images is a major challenge, even for experienced professionals. Simultaneous identification of plant diseases in the same region re-mains a challenge for remote sensing imagery. In this paper, we build on the fact that several disease types exhibit a scale-sensitive feature that can be exploited by deep learning models based on multi-level features. We therefore propose to use the RT-DETR backbone in the YOLOv8 network for cotton disease detection. The integration of the RT-DETR backbone within the deep learning framework is to improve the detection and differentiation of cotton diseases and thus increase the capabilities of aerial imagery. This study analyzes five disease types: fungal leaf, asymptomatic leaf, viral leaf, co-infection, and boll rot. Experimentation with the model showed favorable results for detecting cotton diseases on UAV image by Deep Learning. The fungal leaf and boll rot classes showed the greatest results, with an intersection over union (IoU) of 0.7 and average accuracy of 82.7% and 73.8%, respectively.