The main purpose of this study is to investigate the dynamic mixed elastohydrodynamic lubrication (DMEHL) behavior of the water-lubricated bearing with unbalanced rotor during start-up. To achieve this, a DMEHL model that couples the rotor dynamic model considering the unbalanced load with the transient mixed lubrication model is established. The validity of the calculation procedure is verified by comparisons with published experimental and numerical results. Using numerical studies, the optimal accelerating mode for reducing the transient asperity contact during start-up is identified. Numerical results indicate that during start-up, the rotor dynamic characteristic and hydrodynamic force are more sensitive to the unbalanced load than the transient contact force, and the rotor imbalance eccentricity will affect the transient contact behaviors if the surface roughness is relatively large. In addition, the DMEHL responses are affected by the unbalanced load only when the accelerating time is small. Finally, the DMEHL responses during start-up using three different water-lubricated materials, namely, polymer, Thordon, and ceramic, are compared. It is found that DMEHL performance at the start-up stage highly depends on the elastic modulus and hardness of the water-lubricated material.

In response to the challenges of detecting rice pests and diseases at different scales and the difficulties associated with deploying and running models on embedded devices with limited computational resources, this study proposes a multi-scale rice pest and disease recognition model (RGC-YOLO). Based on the YOLOv8n network, which includes an SPPF layer, the model introduces a structural reparameterization module (RepGhost) to achieve implicit feature reuse through reparameterization. GhostConv layers replace some standard convolutions, reducing the model’s computational cost and improving inference speed. A Hybrid Attention Module (CBAM) is incorporated into the backbone network to enhance the model’s ability to extract important features. The RGC-YOLO model is evaluated for accuracy and inference time on a multi-scale rice pest and disease dataset, including bacterial blight, rice blast, brown spot, and rice planthopper. Experimental results show that RGC-YOLO achieves a precision (P) of 86.2%, a recall (R) of 90.8%, and a mean average precision at Intersection over Union 0.5(mAP50) of 93.2%. In terms of model size, the parameters are reduced by 33.2%, and GFLOPs decrease by 29.27% compared to the base YOLOv8n model. Finally, the RGC-YOLO model is deployed on an embedded Jetson Nano device, where the inference time per image is reduced by 21.3% compared to the base YOLOv8n model, reaching 170 milliseconds. This study develops a multi-scale rice pest and disease recognition model, which is successfully deployed on embedded field devices, achieving high-accuracy real-time monitoring and providing valuable reference for intelligent equipment in unmanned farms.

The prediction of crop yield plays a crucial role in national economic development, encompassing grain storage, processing, and grain price trends. Employing multiple sensors to acquire remote sensing data and utilizing machine learning algorithms can enable accurate, fast, and nondestructive yield prediction for maize crops. However, current research heavily relies on single-type remote sensing data and traditional machine learning methods, resulting in the limited robustness of yield prediction models. To address these limitations, this study introduces a field-scale maize yield prediction model named the convolutional neural network–attention–long short-term memory network (CNN-attention-LSTM) model, which utilizes multimodal remote sensing data collected by multispectral and light detection and ranging (LIDAR) sensors mounted on unmanned aerial vehicles (UAVs). The model incorporates meteorological data throughout the crop reproductive stages and employs the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) for the initial part of the vegetative stage (initial part of the V period), the later part of the vegetative stage (later part of the V period), the reproductive stage (R period), and the maturity stage (M period), along with LIDAR data for Point_75–100 in the later part of the V period, Point_80–100 in the R period, and Point_50–100 in the M period, complemented by corresponding meteorological data as inputs. The resulting yield estimation demonstrates exceptional performance, with an R² value of 0.78 and an rRMSE of 8.27%. These results surpass previous research and validate the effectiveness of multimodal data in enhancing yield prediction models. Furthermore, to assess the superiority of the proposed model, four machine learning algorithms—multiple linear regression (MLR), random forest regression (RF), support vector machine (SVM), and backpropagation (BP)—are compared to the CNN-attention-LSTM model through experimental analysis. The outcomes indicate that all alternative models exhibit inferior prediction accuracy compared to the CNN-attention-LSTM model. Across the test dataset within the study area, the R² values for various nitrogen fertilizer levels consistently exceed 0.75, illustrating the robustness of the proposed model. This study introduces a novel approach for assessing maize crop yield and provides valuable insights for estimating the yield of other crops.