To solve the challenges of low detection accuracy of maize pests and diseases, complex detection models, and difficulty in deployment on mobile or embedded devices, an improved YOLOv8 algorithm was proposed. Based on the original YOLOv8n, the algorithm replaced the Conv module with the DSConv module in the backbone network, which reduced the backbone network parameters and computational load and improved the detection accuracy at the same time. Additionally, BiFPN was introduced to construct a bidirectional feature pyramid structure, which realized efficient information flow and fusion between different scale features and enhanced the feature fusion ability of the model. At the same time, the Wise-IoU loss function was combined to optimize the training process, which improved the convergence speed and regression accuracy of the loss function. The experimental results showed that the precision, recall, and mAP0.5 of the improved algorithm were improved by 1.4, 1.1, and 1.5%, respectively, compared with YOLOv8n, and the model parameters and computational costs were reduced by 6.6 and 7.3%, respectively. The experimental results demonstrate the effectiveness and superiority of the improved YOLOv8 algorithm, which provides an efficient, accurate, and easy-to-deploy solution for maize leaf pest detection.

This research centers around cast steel 20Mn, which is the material utilized for the ear-picking roller of a corn harvester. The study delves into methods of enhancing its hydrophobicity and wear resistance. Fiber laser-processing technology was employed to fabricate pangolin bionic micro-textures on the material surface, and PVD technology was utilized to deposit a TiN coating. The wear resistance of the modified surface was investigated via the reciprocating dry sliding wear method, while its hydrophobicity was concurrently examined. The results demonstrate that the laser texturing technology and TiN coating managed to reduce the friction coefficient of the sample surface by 20% and 30.9%, respectively. This can be chiefly attributed to the significant effects of the modified surface in augmenting hardness, diminishing the contact area of the friction surface, lowering shear stress, and entrapping wear debris. When the pangolin texture and TiN coating work in concert, the abrasive and fatigue wear between the two surfaces is conspicuously mitigated, and the friction coefficient is reduced by 38.09%. Moreover, the experiment also reveals that a superhydrophobic surface can be achieved by fabricating the pangolin micro-textures.

This study proposes an improved object detection model, Agri-YOLO, based on the YOLOv8n baseline model, addressing the issues of insufficient detection accuracy and high model deployment costs for various types and scales of obstacles in agricultural fields. Three core optimization strategies are implemented to enhance model performance: replacing traditional convolution with the Wavelet Transform Convolution module to improve multi-scale feature perception with only a slight increase in parameters; utilizing the Wise-IoU loss function to optimize bounding box regression, enhancing the localization accuracy of irregular obstacles and effectively improving the convergence speed and regression accuracy of the loss function; and integrating the Dynamic Upsample module to reduce computational load while ensuring detection accuracy of agricultural obstacles, thereby improving feature recovery accuracy. Experimental results demonstrate that Agri-YOLO significantly outperforms baseline algorithms such as Faster R-CNN, SSD, and YOLOv8n in terms of precision, recall, and mAP50 metrics, with improvements of 0.4, 1.7, and 1.2% in accuracy, recall, and mAP0.5, respectively, while also enhancing model robustness and stability. This research provides an efficient technical solution for detecting obstacles in agricultural fields.

This paper focuses on the hydraulic header leveling system of grain combine harvesters, and comprehensively uses methods of theoretical analysis, simulation optimization and experimental research to deeply explore its dynamic characteristics. By establishing mathematical models of key components of the system, conducting simulation analysis with AMESim software, and carrying out prototype tests, the laws of dynamic characteristics of each component in the system are clarified, and the effectiveness of the optimization scheme is verified. This provides an important basis for the design and improvement of the hydraulic system of grain combine harvesters.

This chapter addresses the challenges posed by traditional rigid sensing devices, which cannot effectively adhere to plant leaves, and the complexity of current flexible sensing device fabrication processes. A rapid method for fabricating flexible sensing devices is proposed. By utilizing screen printing technology, conductive ink is screen-printed onto a flexible polydimethylsiloxane substrate to obtain a flexible interdigitated electrode. Subsequently, graphene oxide, a sensing material, is drop-coated onto the electrode’s active area to produce a flexible sensing device. Performance tests of this flexible sensing device reveal its good anti-interference capability. Furthermore, experiments have further verified the sensitivity of the flexible sensing device within the humidity detection range of plant leaves, confirming its feasibility. This provides an important basis for the design and improvement of a flexible plant humidity sensor detection system.