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.

Introduction: The accurate and rapid detection of ginseng fruits in natural environments is crucial for the development of intelligent harvesting equipment for ginseng fruits. Due to the complexity and density of the growth environment of ginseng fruits, some newer visual detection methods currently fail to meet the requirements for accurate and rapid detection of ginseng fruits. Therefore, this study proposes the YOLO-Ginseng detection method.Methods: Firstly, this detection method innovatively proposes a plug-and-play deep hierarchical perception feature extraction module called C3f-RN, which incorporates a sliding window mechanism. Its unique structure enables the interactive processing of cross-window feature information, expanding the deep perception field of the network while effectively preserving important weight information. This addresses the detection challenges caused by occlusion or overlapping of ginseng fruits, significantly reducing the overall missed detection rate and improving the long-distance detection performance of ginseng fruits; Secondly, in order to maintain the balance between YOLO-Ginseng detection precision and speed, this study employs a mature channel pruning algorithm to compress the model.Results: The experimental results demonstrate that the compressed YOLO-Ginseng achieves an average precision of 95.6%, which is a 2.4% improvement compared to YOLOv5s and only a 0.2% decrease compared to the uncompressed version. The inference time of the model reaches 7.4ms. The compressed model exhibits reductions of 76.4%, 79.3%, and 74.2% in terms of model weight size, parameter count, and computational load, respectively.Discussion: Compared to other models, YOLO-Ginseng demonstrates superior overall detection performance. During the model deployment experiments, YOLO-Ginseng successfully performs real-time detection of ginseng fruits on the Jetson Orin Nano computing device, exhibiting good detection results. The average detection speed reaches 24.9 fps. The above results verify the effectiveness and practicability of YOLO-Ginseng, which creates primary conditions for the development of intelligent ginseng fruit picking equipment.

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.

High-efficiency and precise detection of crop ears in the field is a core component of intelligent agricultural yield estimation. However, challenges such as overlapping ears caused by dense planting, complex background interference, and blurred boundaries of small targets severely limit the accuracy and practicality of existing detection models. This paper introduces LiteFocus-YOLO(LF-YOLO), an efficient small-object detection model. By synergistically enhancing feature expression through cross-scale texture optimization and attention mechanisms, it achieves high-precision identification of maize tassels and wheat ears. The model innovatively incorporates the following: The Lightweight Target-Aware Attention Module (LTAM) strengthens high-frequency feature expression for small targets while reducing background interference, enhancing robustness in densely occluded scenes. The Cross-Feature Fusion Module (CFFM) addresses semantic detail loss through deep-shallow feature fusion modulation, optimizing small target localization accuracy. The experiment validated performance on the drone-based maize tassel dataset. Results show that LF-YOLO achieved an mAP50 of 97.9%, with mAP50 scores of 94.6% and 95.7% on the publicly available maize tassel and wheat ear datasets, respectively. It achieves generalization across different crops while maintaining high accuracy and recall. Compared to current mainstream object detection models, LF-YOLO delivers higher precision at lower computational cost, providing efficient technical support for dense small object detection tasks in agricultural fields.