ADC-YOLO: Adaptive Perceptual Dynamic Convolution-Based Accurate Detection of Rice in UAV Images

High-precision detection of rice targets in precision agriculture is crucial for yield assessment and field management. However, existing models still face challenges, such as high rates of missed detections and insufficient localization accuracy, particularly when dealing with small targets and dynamic changes in scale and morphology. This paper proposes an accurate rice detection model for UAV images based on Adaptive Aware Dynamic Convolution, named Adaptive Dynamic Convolution YOLO (ADC-YOLO), and designs the Adaptive Aware Dynamic Convolution Block (ADCB). The ADCB employs a “Morphological Parameterization Subnetwork” to learn pixel-specific kernel shapes and a “Spatial Modulation Subnetwork” to precisely adjust sampling offsets and weights—realizing for the first time the adaptive dynamic evolution of convolution kernel morphology with variations in rice scale. Furthermore, ADCB is embedded into the interaction nodes of the YOLO backbone and neck; combined with depthwise separable convolution in the neck, it synergistically enhances multi-scale feature extraction from rice images. Experiments on public datasets show that ADC-YOLO comprehensively outperforms state-of-the-art algorithms in terms of AP50 and AP75 metrics and maintains stable high performance in scenarios such as small targets at the seedling stage and leaf overlap. This work provides robust technical support for intelligent rice field monitoring and advances the practical application of computer vision in precision agriculture.