Composite materials prepared via laser cladding technology are widely used in die production and other fields. When a composite material is used for heat dissipation and heat transfer, thermal conductivity becomes an important parameter. However, obtaining effective thermal conductivity of composite materials prepared via laser cladding under different parameters requires a large number of samples and experiments. In order to improve the research efficiency of thermal conductivity of composite materials, a mathematical model of Cu/Ni composite materials was established to study the influence of cladding-layer parameters on the effective thermal conductivity of composite materials. The comparison between the model and the experiment shows that the model’s accuracy is 86.7%, and the error is due to the increase in thermal conductivity caused by the alloying of the joint, so the overall effective thermal conductivity deviation is small. This study provides a mathematical model method for studying the thermodynamic properties of laser cladding materials. It provides theoretical and practical guidance for subsequent research on the thermodynamic properties of materials during die production.

This study proposes a grading algorithm for Orah sorting lines based on machine vision and deep learning. The original ShuffleNet V2 network was modified by replacing the ReLU activation function with the Mish activation function to alleviate the neuron death problem. The ECA attention module was incorporated to enhance the extraction of Orah appearance features, and transfer learning was applied to improve model performance. As a result, the ShuffleNet_wogan model was developed. Based on the operational principles of the sorting line, a time-sequential grading algorithm was designed to improve grading accuracy, along with a multi-sampling diameter algorithm for simultaneous Orah diameter measurement. Experimental results show that the ShuffleNet_wogan model achieved an accuracy of 91.12%, a 3.92% improvement compared to the original ShuffleNet V2 network. The average prediction time for processing 10 input images was 51.44 ms. The sorting line achieved a grading speed of 10 Orahs per second, with an appearance grading accuracy of 92.5% and a diameter measurement compliance rate of 98.3%. The proposed algorithm is characterized by high speed and accuracy, enabling efficient Orah sorting.

Fruit occlusion, dense clustering, platform vibration, and cross-frame re-identification errors reduce the reliability of video-stream yield estimation in high-density dwarf apple orchards. To address these limitations, this study developed a vision-based framework for apple detection, tracking, dynamic counting, and yield estimation. Apple detection was improved by embedding a Shuffle Attention (SA) module into YOLOv12n and introducing SlideLoss for hard-sample reweighting, yielding the YOLOv12n-SA-SlideLoss detector. Following detection, the DeepSORT tracking module was also improved by augmenting it with global motion compensation (GMC) and a dual-criteria association strategy based on motion consistency and appearance similarity. Yield estimation was formulated as a counting-based compensation model using dynamic fruit counts, mean fruit mass, and an occlusion compensation factor, and was compared with a pixel-geometric regression model based on detection boxes. Field experiments were conducted in a high-density dwarf ‘Fuji’ apple orchard in Aksu, Xinjiang, China. YOLOv12n-SA-SlideLoss achieved an mAP@0.5 of 95.7%, exceeding the YOLOv12n baseline by 2.3%. The improved DeepSORT reached 87.4% multiple object tracking accuracy and 86.7% multiple object tracking precision, while reducing identification switches by 22.2% relative to the original algorithm. The dynamic counting framework increased average counting accuracy from 85.6% to 93.5%. For yield estimation, the counting-based compensation model achieved a mean prediction accuracy of 85.2%, an RMSE of 18.5 kg, and a mean bias of −17.2 kg across six validation samples (n = 6), each covering multiple trees. Overall, the results indicate that combining detection, motion-compensated tracking, and counting-based correction improves stability of single-view video-stream yield estimation in high-density dwarf apple orchards.