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, mAP_0.5, and mAP_0.5~0.95 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.

Film covering is one of the most important parts during cotton planting. Multiple advantages remain, such as the heat preservation and moisture retention, inhibition of weed growth, fertilizer utilization rate, as well as the reduction of pests and diseases for the high yield. Among them, the film of cotton seedlings has been limited to low film-breaking efficiency. However, the labor-intensive manual breaking cannot fully meet the needs of large-scale cotton planting. Intelligence and precision operations are often required for the cotton film breaking using artificial intelligence in recent years. Intelligent machines for the cotton film breaking can be expected to accurately and rapidly identify the target of cotton seedlings under the film. In this study, a lightweight detection model, YOLOv11n-PRML, was proposed to optimize using YOLOv11n as follows: (1) A PConv-CGLU hybrid module was proposed to combine the PConv (Partial Convolution) of FasterNet and CGLU (Convolutional Gated Linear Unit) of TransNext. The C3k2 module was refactored to reduce the model complexity for the feature extraction; (2) An RSCD (Rep Shared Convolutional Detection) head with a heavy parameter-sharing strategy was employed to improve the accuracy and processing speed of the model in small target detection tasks; (3) The loss function was optimized as MPDIoU (Minimum Points Distance Intersection over Union) to improve the detection performance in dense environments; (4) The model was lightweighted using the LAMP (Layer-Adaptive Magnitude-Based Pruning) strategy. The TIDE (Toolkit for Identifying Detection and Segmentation Errors) evaluation indicator was introduced to evaluate the performance. The superiority of the YOLOv11n-PRML model was verified to detect the cotton seedling under film. Ablation tests were carried out to compare the different models. The experimental results show that the YOLOv11n - PRML model attains a precision of 90.1% and a mean Average Precision 0.5 (mAP_0.5) of 89.6%, representing increases of 1.8 and 1.0 percentage points, respectively, compared to the original YOLOv11n model. Its detection speed is improved to 114.4 frames per second. The localization error, missed ground truth error, and model size are 0.83, 0.92, and 4.0 MB, respectively, showing decreases of 0.32, 0.85, and 1.5 MB compared to the original model. Compared to the models like YOLOv5s-S (YOLOv5s-ShuffleNetV2), YOLOv7-tiny-M (YOLOv7-tiny-MobileNetV3), YOLOv8n-G (YOLOv8n-GhostNetV2), YOLOv9t, YOLOv10n, and YOLOv12n, the model's mAP_0.5 increased by 0.5, 4.3, 0.1, 4.0, 4.8, and 1.6 percentage points, respectively, while the model size was reduced by 2.8, 6.8, 1.2, 0.3, 1.8, and 1.3 MB, respectively. The YOLOv11n and YOLOv11n-PRML models were deployed on the NVIDIA GeForce RTX 2070 Ti platform, respectively, and optimized by TensorRT high-performance operators and Int8 quantization technology. The test results show that the detection accuracy mAP_0.5 of the YOLOv11n model is 87.6%, and the detection speed is 60.7 frames per second, while the detection accuracy mAP_0.5 of the YOLOv11n-PRML model is 89.1%, and the detection speed is 80.3 frames/s. In terms of inference time, the inference time of the model before and after the improvement is 16.3 ms and 11.1 ms respectively. YOLOv11n-PRML outperforms YOLOv11n in all indicators on the mobile terminal. In conclusion, the proposed YOLOv11n-PRML model provides technical support for the detection of cotton seedlings under film in complex environments and facilitates its practical application in real-world scenarios. It also offers a valuable reference for the development of intelligent film-breaking machinery. Future research will focus on expanding the dataset to include seedlings from multiple cotton varieties and conducting extensive comparative experiments to enhance the model's versatility and robustness.