Ensuring the healthy growth of eggplants requires the precise detection of leaf diseases, which can significantly boost yield and economic income. Improving the efficiency of plant disease identification in natural scenes is currently a crucial issue. This study aims to provide an efficient detection method suitable for disease detection in natural scenes. A lightweight detection model, YOLOv5s-BiPCNeXt, is proposed. This model utilizes the MobileNeXt backbone to reduce network parameters and computational complexity and includes a lightweight C3-BiPC neck module. Additionally, a multi-scale cross-spatial attention mechanism (EMA) is integrated into the neck network, and the nearest neighbor interpolation algorithm is replaced with the content-aware feature recombination operator (CARAFE), enhancing the model’s ability to perceive multidimensional information and extract multiscale disease features and improving the spatial resolution of the disease feature map. These improvements enhance the detection accuracy for eggplant leaves, effectively reducing missed and incorrect detections caused by complex backgrounds and improving the detection and localization of small lesions at the early stages of brown spot and powdery mildew diseases. Experimental results show that the YOLOv5s-BiPCNeXt model achieves an average precision (AP) of 94.9% for brown spot disease, 95.0% for powdery mildew, and 99.5% for healthy leaves. Deployed on a Jetson Orin Nano edge detection device, the model attains an average recognition speed of 26 FPS (Frame Per Second), meeting real-time requirements. Compared to other algorithms, YOLOv5s-BiPCNeXt demonstrates superior overall performance, accurately detecting plant diseases under natural conditions and offering valuable technical support for the prevention and treatment of eggplant leaf diseases.

In natural materials, thermal expansion is typically positive, and negative thermal expansion is rarely observed. The tunable thermal expansion properties of mechanical metamaterials offer a promising solution to challenges caused by rapid temperature fluctuations. Therefore, this study proposes a dual-material double-trapezoidal hexagonal mechanical metamaterial (DTH), and derives the thermoelastic equations that build the relationship between temperature, external force, and displacement. Through theoretical analysis and numerical simulation, the intrinsic mechanism between the CTE and geometric parameters of DTH is revealed. Through the synergistic effect of dual materials and structural design, this metamaterial not only achieves thermal expansion regulation but also enhanced lightweight performance. The results show that by controlling the geometric parameters of DTH, the adjustment of effective CTE and elastic modulus can be realized, and the metamaterial composed of positive CTE materials can achieve a range of thermal expansion behaviors, including near-zero CTE and negative CTE. The tunable thermal expansion range extends from +39.92 ppm/°C to −3640.6191 ppm/°C. The metamaterials proposed in this study are not only superior to traditional materials in terms of thermal expansion performance but also have the characteristics of light weight and simple structure. This multifunctional material achieves higher performance and adaptability in applications.