The spread of infections and rot are crucial factors in the decrease in tomato production. Accurately segmenting the affected tomatoes in real-time can prevent the spread of illnesses. However, environmental factors and surface features can affect tomato segmentation accuracy. This study suggests an improved YOLOv8s-Seg network to perform real-time and effective segmentation of tomato fruit, surface color, and surface features. The feature fusion capability of the algorithm was improved by replacing the C2f module with the RepBlock module (stacked by RepConv), adding SimConv convolution (using the ReLU function instead of the SiLU function as the activation function) before two upsampling in the feature fusion network, and replacing the remaining conventional convolution with SimConv. The F1 score was 88.7%, which was 1.0%, 2.8%, 0.8%, and 1.1% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm, respectively. Meanwhile, the segment mean average precision (segment mAP_@0.5) was 92.2%, which was 2.4%, 3.2%, 1.8%, and 0.7% higher than that of the YOLOv8s-Seg algorithm, YOLOv5s-Seg algorithm, YOLOv7-Seg algorithm, and Mask RCNN algorithm. The algorithm can perform real-time instance segmentation of tomatoes with an inference time of 3.5 ms. This approach provides technical support for tomato health monitoring and intelligent harvesting.

In tomato producing fields, automated large-area detection method is critical for fruit health monitoring and harvesting. However, due to the limited feature information included in tomatoes, large-area detection across long distances results in more missing or incorrect detections. To address this issue, this research proposes an improved YOLOv8 network, RSR-YOLO, for long-distance identification of tomato fruits. Firstly, this paper designs a partial group convolution (PgConv) and furthermore an innovative FasterNet (IFN) module for feature extraction, taking into account the impact of split operations on the computational complexity of the backbone network. The IFN module is lightweight and efficient, which improves the detection accuracy and real-time detection performance of the model. Secondly, this research combines the Gather and Distribute mechanism (GD) and redesigns the feature fusion module to implement the extraction and fusion of various levels of tomato features, given the critical significance that low-dimensional features play in small target recognition and localization. Finally, Repulsion Loss is used in this paper to examine the impact of fruit overlap and leaf occlusion on detection outcomes. RSR-YOLO achieves precision, recall, F1 score, and mean average precision (mAP_@0.5) of 91.6%, 85.9%, 88.7%, and 90.7%, respectively, marking increases of 4.2%, 4%, 4.2%, and 3.6% compared to YOLOv8n. In addition, this paper designs a specialized Graphical User Interface (GUI) for the real-time detection task of tomatoes.

The drag reduction design of underwater vehicles is of great significance to saving energy and enhancing speed. In this paper, the drag reduction characteristics of Paramisgurnusdabryanus loach was explored using 3D ultra-depth field microscopy to observe the arrangement of the scales. Then, a geometric model was established and parameterized. A simulated sample was processed by computer numerical control (CNC) machining and tested through using a flow channel bench. The pressure drop data were collected by sensors, and the drag reduction rate was consequently calculated. The test results showed that the drag reduction rate of a single sample could reach 23% at a speed of 1.683 m/s. Finally, the experimental results were verified by numerical simulation and the drag reduction mechanism was explored. The boundary layer theory and RNG k-ε turbulence model were adopted to analyze the velocity contour, pressure contour and shear force contour diagrams. The numerical simulation results showed that a drag reduction effect could be achieved by simulating the microstructure of scales of the Paramisgurnusdabryanus loach, showing that the results are consistent with the flow channel experiment and can reveal the drag reduction mechanism. The bionic surface can increase the thickness of boundary layer, reduce the Reynolds number and wall resistance. The scales disposition of Paramisgurnusdabryanus loach can effectively reduce the surface friction, providing a reference for future research on drag reduction of underwater vehicles such as ships and submarines.

To enable mobile robots to effectively deal with the emergency of visual contamination, contingency planning based on case-based reasoning (CBR) was performed in this paper. First, for a wheeled mobile robot (WMR) equipped with a chameleon-inspired visual system, a target search model in chameleon-inspired binocular negative correlation movement (CIBNCM) mode was established. Second, a CBR-based contingency planning model of visual contamination for WMRs was established, where the reasoning process using CBR for visual contamination was analyzed in detail. Third, through the analysis of environment perception when visual contamination occurs, a perception model in chameleon-inspired visual contamination for WMRs was built. Finally, to validate the proposed approach, a contingency planning experiment scheme for visual contamination was designed based on the robot’s general planning of target tracking, and the experimental result is discussed. The proposed CBR-based contingency planning approach for visual contamination can reason out effective solutions corresponding to the contamination situations. The rationality of the approach was verified by experiments with satisfactory results. Moreover, compared with the contingency planning method based on rule-based reasoning, the accuracy of target retracking after the robot visual system is contaminated is significantly higher for the CBR-based contingent planning method used in this paper.

Densely sintered Mn–Zn ferrites with grain size varying between 1 and 6 μm were obtained. Then the effect of grain size was investigated in detail by power loss analysis and scanning electron microscopy examination of microstructure. It is found that the power loss is seen to be lowered from 260 to 120 kW/m³ at 500 kHz, 50 m T and 90 °C with grain size decreased in the order of 3 μm. It is assumed that the residual loss from domain wall resonance is a dominant factor of power loss from 500 kHz to 1 MHz. Therefore we conclude that Mn–Zn ferrite display a grain size dependent power loss with transition at grain size about 3 μm due to the absence of intragranular domain wall and these fine core materials can be prepared by low sintering temperature and the optimum control of atmosphere containing 0.5% O₂.