This study addresses challenges in agricultural pest detection, such as false positives and missed detections in complex environments, by proposing an enhanced Mask-RCNN model integrated with a Convolutional Block Attention Module (CBAM). The framework combines three innovations: (1) a CBAM attention mechanism to amplify pest features while suppressing background noise; (2) a feature-enhanced pyramid network (FPN) for multi-scale feature fusion, enhancing small pest recognition; and (3) a dual-channel downsampling module to minimize detail loss during feature propagation. Evaluated on a dataset of 14,270 pest images from diverse Chinese agricultural regions (augmented to 7,000 samples and split into 6:1:3 training/validation/test sets), the model achieved precision, recall, and F1 scores of 95.91%, 95.21%, and 95.49%, respectively, outperforming ResNet, Faster-RCNN, and Mask-RCNN by up to 2.67% in key metrics. Ablation studies confirmed the CBAM module improved F1 by 5.5%, the FPN increased small-target recall by 6%, and the dual-channel downsampling boosted AP@50 by 3.1%. Despite its compact parameter size (63.87 MB, 1.39 MB lighter than Mask-RCNN), limitations include reduced accuracy in low-contrast scenarios (e.g., foggy fields) and GPU dependency. Future work will focus on lightweight deployment for edge devices and domain adaptation, offering a robust solution for intelligent pest monitoring systems that balance accuracy with computational efficiency.

Aiming to address the wear and replacement inconvenience of concrete mixer liners, this study utilizes a laser cladding system to clad Fe60 alloy powder on the liner. It investigates the influence of different process parameters on the forming quality of the Fe60 alloy powder cladding layer. The optimal process parameters were obtained by weighted comprehensive evaluation, and single-layer multi-pass cladding experiments were carried out under the optimal process parameters to investigate the effects of a 30%, 40%, and 50% lap rate on the surface flatness and forming quality of the cladding layer. Using a metallographic microscope, a scanning electron microscope analysis of the macro morphology and microstructure of the cladding layer was conducted, a DPT-5 penetration flaw detector was used to observe the cracks on the surface of the multi-channel cladding, a microhardness tester and friction and wear experimental machine were used for the hardness of the cladding layer, and an abrasive wear resistance test was conducted. The results show that under the process parameters of a laser power of 900 W, powder feeding speed of 7 g/min, scanning speed of 600 mm/min, and 50% lap rate, the average microhardness of the fused cladding layer reaches 742 HV, which is 1.8 times higher than that of the liner plate, and the coefficient of friction is 0.57, which improves the liner plate’s wear resistance performance and service life.

Rotary tillage blades, as critical components of soil tillage machinery, encounter significant challenges in mountainous agricultural operations, where excessive wear and high energy consumption are persistent issues. To address these problems, this study proposes an integrated strategy combining structural optimization with surface reinforcement. A blade–soil interaction model based on Smoothed Particle Hydrodynamics (SPH) was developed to optimize blade geometry, reducing power consumption to 0.106 kW with a simulation error of only 2.83%. In parallel, Fe60–WC composite coatings containing 30%, 35%, and 40% WC were fabricated on 65Mn substrates using laser cladding. Microstructural analysis revealed significant grain refinement with increasing WC content, while tribological tests showed that the 35% WC coating blades exhibited superior wear resistance, with a mass loss of 1.9 mg, and a relatively low friction coefficient of 0.362. Field trials further confirmed that the blades resulted in a 45.75% reduction in average wear, after structural enhancement and the application of the optimized coating, with a measured loss of 2.259 g compared to the uncoated blades. These findings demonstrate the synergistic benefits of structural optimization and advanced surface engineering, providing an effective pathway to improve the durability and efficiency of rotary tillage blades in demanding field conditions.

Orah mandarin (Citrus reticulata cv. Orah) lacks systematic grading treatment after harvesting, resulting in a high fresh fruit loss rate and affecting the economic value. There are many drawbacks to traditional manual and mechanical sorting. Therefore, intelligent, rapid, non-destructive surface defect detection technology is significant. In addition to the fruit size, surface defects (e.g., canker, sunburn) are another important criterion for grading fruit. To overcome the challenges in detecting surface defects of orah mandarin, like multi-scale features, significant size differences, and slow convergence speed, we propose the YOLOv8-Orah detection model based on YOLOv8n. Path Aggregation Network (PANet) is replaced by a Focusing Diffusion Pyramid Network (FDPN), and the Diffusion and Spatial Interaction (DASI) module is introduced to effectively fuse and enhance features of different scales and improve detection accuracy. The Bottleneck in the C2f module is replaced by the Hybrid Dilated Residual Attention Block (HDRAB) module to reduce missed detections and false detections. We also introduce the NWD-CIoU joint bounding box loss to accelerate the convergence speed and improve the detection accuracy of small defects. The experimental results show that the improved YOLOv8-Orah model performs well in terms of precision, recall, and average precision, reaching 81.9%, 78.8%, and 84.2%, respectively. Compared with the original YOLOv8n, the improved model increased by 4.0%, 1.7%, and 3.0%, respectively. Meanwhile, the parameter count decreased by 7.76%. Compared with other mainstream models, YOLOv8-Orah achieves a good balance between detection accuracy and computational efficiency. The results technically support defect detection in postharvest orah mandarin and real-time grading of their quality. Meanwhile, it can promote the intelligent development of the bergamot industry.

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.