In the "one-film-for-two-years" mode of full-film double-ridge furrow sowing in the dry-farming agricultural areas of Northwest China, maize root stubble recognition faces challenges such as similar morphologies between residual plastic film and stubble, as well as complex backgrounds. Accurate detection of maize root stubble is a key step to realize intelligent stubble-avoiding sowing. To improve the precision and average detection accuracy of maize root stubble detection, this study proposes an improved TriLightNet-YOLO algorithm based on the YOLOv11n model. To address this, this study takes YOLOv11n as the benchmark and proposes an improved TriLightNet-YOLO model, which breaks through technical bottlenecks through three core modules: First, a context-guided module (Edge Fusion Stem) is designed, which reconstructs the traditional Sobel operator into 3D depthwise separable convolutions. The pooling branch adopts a special padding strategy to effectively alleviate the damage to edge information caused by conventional pooling operations. This not only retains the edge detection characteristics but also enhances the transmission of key details of small targets while reducing computational overhead. Second, the DSBNCSPELAN4 module is constructed to replace the original C3k2 structure: it first compresses channels via 1×1 convolutions and is split into two branches through the Split operation — the main branch retains original features, while the second branch expands the receptive field and fuses local features via the DSBNCSP module plus 3×3 convolutions, then splits into two sub-branches: one directly performs feature concatenation, and the other undergoes the DSBNCSP module plus 3×3 convolutions again for concatenation. The module incorporates Dilated Separable Convolution to adapt to soil-covered scenarios, and adopts reparameterization technology in the inference stage to balance real-time performance and texture feature capturing capability. Third, a grouped wavelet feature interaction module (Grouped VoVGSCSP HHF Fusion) is introduced. Based on Haar wavelet transform, it realizes the decomposition of low-frequency contours and high-frequency edges of features. After processing high-frequency information through residual blocks, the inverse transformation is performed for fusion, significantly reducing noise interference such as soil cracks and residual stalks. Experiments were conducted based on a dedicated corn stubble dataset: this dataset contains 2,033 images, collected by DJI Mini3 and Huawei Mate60 in plots around Lanzhou at heights such as 0.3 m and 0.9 m, as well as at a 60° tilt angle. It covers three types of targets: residual film, soil mulch layer, and stubbles (11081 annotations). After online enhancement such as motion blur, the dataset was divided into training, testing, and validation sets in a 7:2:1 ratio. The results show that the mAP@0.5 of the improved model reaches 92.8%, which is 1.7 percentage points higher than that of YOLOv11n, with precision and recall rates reaching 90.6% and 87.4% respectively; the floating-point operations (GFLOPs) are reduced to 5.9 G, the number of parameters only increases by 0.4M, and the frame rate is increased to 156 frames per second. Its comprehensive performance is superior to that of mainstream lightweight models such as YOLOv5n and YOLOv8n. In the deployment test on the Orange Pi 5 MAX edge device, the model still maintains a precision of 86.4%, a recall rate of 83.7%, and a frame rate of 25.8 frames per second. This provides an efficient visual solution for intelligent stubble-avoiding sowing in film-mulched agriculture, contributing to the upgrading of agricultural mechanization in arid areas.

In view of the problems in operation process of fixed rake-type residual recycling component, such as poor individual profiling effect in film picking, easy clogging of the compound of films, soil and maize stubbles, high power consumption in film picking, and strong disturbance to seedbed soil, in this study, an operation model of intermittent film-picking on full-film mulched double ditches was proposed and an intermittent film picking component was designed. The DEM-MBD coupled algorithm was adopted for numerical simulation on the operation process of the intermittent film-picking component on full-film mulched double ditches, and a comparative analysis was carried out on the seedbed disturbance effect and resistance variation characteristics in film-picking by fixed and intermittent film-picking components. By taking the forward speed in film-picking, cam arrangement angle of the film-picking component and rotating speed of the cam shaft as independent variables, film-picking rate as the response value, a mathematical model between test factors and the film-picking rate was established, to explore the influence order of the factors on film-picking rate, and the optimal working parameters of the intermittent film-picking component were obtained as follows: the forward speed in film-picking was 2 km/h, cam arrangement angle was 180°, rotating speed of the cam shaft was 120 r/min. Under the optimal parameter combination, the average film-picking rate of the simulation test was 96.1%. Field test showed that, the average film-picking rate of the intermittent film-picking component was 95.6%, and 0.5% higher than that of the simulation test. The working condition of the sample machine was basically consistent with the simulation process, and can accurately represent the operation mechanism of intermittent film-picking on full-film mulched double ditches, showing that the established discrete element simulation model and its parameters were accurate and reasonable.