Abstract: Sunflower is one of the preferred oil crops in the mildly to moderately saline-alkali soils, due to its strong tolerance to nutrient-poor soils and drought. Among them, sunflower disk rot can be caused by sclerotinia sclerotiorum infection, leading to increasing sterile seed rates. Nutritional components can also decline, such as kernel protein and oil content. Thereby, both the edible and commercial value of the seeds are often required for high yield. Consequently, it is crucial to efficiently and accurately detect disk rot severity for early disease control, precision pesticide application, and yield estimation. However, conventional disease classification has been limited to manual efficiency and subjectivity in recent years. In this study, an improved YOLOv12n-RCL model was proposed to detect the severity of sunflower disk rot at the mature stage. 1) C3K2-RC modules were used to replace C3K2 ones in the backbone and neck networks. Receptive-field attention convolution (RFAConv) and coordinate attention (CA) mechanisms were integrated to enhance the feature extraction from the different severity grades of disk rot in complex environments. 2) A lightweight upsampling operator, CARFAE, was integrated into the neck network for feature reconstruction. 3) A lightweight shared convolutional detection head with separated batch normalization (LSCSBD) was introduced to improve the detection accuracy and speed for small-scale lesions. Experimental results show that the YOLOv12n-RCL model achieved a precision, recall, mAP0.5, and mAP0.5~0.95 of 83.4%, 81.2%, 84.8%, and 50.2%, respectively, which was improved by 3.8, 3.2, 3.4, and 4.0 percentage points over the baseline model. The number of parameters, computational complexity, and model size were reduced to 2.11 M, 5.7 GFLOPs, and 4.5 MB, respectively, corresponding to reductions of 17.9%, 12.3%, and 19.6%, compared with the original model. The normalized confusion matrix indicated that the recall for the disease severity grades 0, 1, 2, 3, and 4 were 83.0%, 81.2%, 79.1%, 80.7%, and 82.0%, respectively, indicating a balanced recognition over all five grades. Furthermore, 8.2% of samples with a true label of grade 2 were misclassified as grade 3, and 9.3% of samples with a true label of grade 3 were misclassified as grade 2. Therefore, the confusion between severity grades occurred primarily between grades 2 and 3. The misclassification rate remained below 10% in the rest, indicating its strong performance for the disk rot at different grades. A field test was conducted on 70 images with 1 298 samples of different grades of disk rot. The YOLOv12n-RCL model achieved only 10 missed detections and 8 false detections. Compared with the baseline YOLOv12n model, the superior performance was achieved with a higher frame rate of 27.5 frames per second (FPS). Visualization was also conducted on the sunflower disk rot areas using UAV orthophotography. The improved model maintained stable performance even in complex scenarios with dense target distribution and leaf occlusion, without significantly missing or false detections. In summary, the YOLOv12n-RCL model improved the detection accuracy to effectively balance the lightweight deployment efficiency. The finding can also serve as an algorithmic reference to detect the sunflower disk rot at the mature stage in smart agriculture.