Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement.

The flowering stage is a critical phase in the growth of rapeseed crops, and non-destructive, high-throughput quantitative analysis of rape flower clusters in field environments holds significant importance for rapeseed breeding. However, detecting and counting rape flower clusters remains challenging in complex field conditions due to their small size, severe overlapping and occlusion, and the large parameter sizes of existing models. To address these challenges, this study proposes a lightweight rape flower clusters detection model, SPL-YOLOv8. First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. Second, a feature fusion module (C2f-Star) is integrated into the backbone to enhance the feature representation capability of the neck through expanded spatial dimensions, mitigating the impact of occluded regions on detection performance. Additionally, a lightweight Partial Group Convolution Detection Head (PGCD) is proposed, which employs Partial Convolution combined with Group Normalization to enable multi-scale feature interaction. By incorporating additional learnable parameters, the PGCD enhances the detection and localization of small targets. Finally, channel pruning based on the Layer-Adaptive Magnitude-based Pruning (LAMP) score is applied to reduce model parameters and runtime memory. Experimental results on the Rapeseed Flower-Raceme Benchmark (RFRB) demonstrate that the SPL-YOLOv8n-prune model achieves a detection accuracy of 92.2% in Average Precision (AP₅₀), comparable to SOTA methods, while reducing the giga floating point operations per second (GFLOPs) and parameters by 86.4% and 95.4%, respectively. The model size is only 0.5 MB and the real-time frame rate is 171 fps. The proposed model effectively detects rape flower clusters with minimal computational overhead, offering technical support for yield prediction and elite cultivar selection in rapeseed breeding.