## 1 Introduction  
In 2021, China's pepper planting area accounted for 36.72% of the global planting area, and its production accounted for nearly half of the world's total. However, the degree of mechanized picking in China remains low because current target detection algorithms cannot effectively identify the specific location of the peppers. Deep learning algorithms have been proven to be the most robust methods for automatic fruit picking, and many researchers have utilized various target detection methods to optimize mean Average Precision (mAP) and detection speed [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. For instance, [PERSON] et al. [14] used a variant of YOLOv4 and Deep SORT to develop a robust real-time pear fruit counter for a mobile application, effectively supporting automatic pear fruit picking and yield prediction. [PERSON] [15] addressed environmental challenges such as stem and leaf shading, uneven illumination, and fruit overlapping by proposing the YOLOFruit algorithm, which utilizes a spatial pyramid and a feature pyramid network to extract detailed features, achieving an average detection accuracy of 86.2% and a detection time of 11.9 ms. [PERSON] [16] achieved 94.77% accuracy and a detection speed of 25.86 ms by segmenting the red region of tomatoes using HSV within the YOLOv4 detection frame, using the segmented area exceeding a certain percentage as the output. Similarly, for the task of picking peppers in natural environments, [PERSON] et al. [17] introduced a deformable convolution and coordinate attention module in YOLOv5, improving the mAP by 4.6% compared to the original model and achieving a real-time detection speed of 89.3 frames per second on a mobile pickingplatform. However, the complex structure and large parameters of these models make deployment to mobile hardware devices challenging for real-time detection. Many researchers have realized the difficulty of deploying large models to mobile devices and have begun exploring lightweight models. [PERSON] et al. [18] incorporated a 76 \(\times\) 76 detection head with a CBAM attention mechanism network into the YOLOv4-tiny network, reducing the number of model parameters while effectively addressing occlusion and improving the accuracy of small tomato recognition. [PERSON] et al. [19] added CBAM to FPN to learn the correlation of features between different channels by assigning weights to the features of each channel, enhancing the transmission of deep information within the network structure and reducing the interference of complex backgrounds on target recognition. Although this approach reduces model size, it does not substantially change the underlying structure. In contrast, [PERSON] et al. [20] developed a small baseline model based on YOLOv5s by adding phantom structures and adjusting the overall width of the feature map, introducing transfer learning to achieve fast and accurate identification of apple chilis while occupying fewer computational resources. Similarly, [PERSON] et al. [21] proposed a classification model for pepper quality detection by combining transfer learning and convolutional neural networks, achieving fast convergence and improved performance in pepper detection. However, these methods do not achieve significant model lightweighting, focusing more on resource reduction for model training. [PERSON] et al. [22] addressed equipment requirements by eliminating feature mappings used for detecting large targets in the YOLOX model, sampling small target feature mappings through the nearest neighbor value, and optimizing the loss function at the output, reducing model parameters by 44.8% and increasing detection speed by 63.9%. [PERSON] et al. [23] implemented a GhostNet feature extraction network with a coordinate attention module in YOLOv4, introducing deeply differentiable convolution to reconstruct the neck and YOLO head structure, creating a lightweight apple detection model. However, these methods have limited parameter reductions and some degradation in model performance.