When periodic and other piecewise linear functions are introduced in a chaotic system with two-dimensional offset boosting for extra feedback, more patterns of diagonal distribution from coexisting attractors can be organized. In this study, the periodic function is implanted for attractor self-reproducing, while the signum function and absolute value function are integrated for the attractor symmetrization. For the offset interlocking across dimensions, the coexisting attractors can be reproduced in phase space with the shapes of “V” and “X”. Based on the FPGA platform, all the patterns are validated in a digital hardware environment confirming the consistency with simulation.

In this work, the polarity balance of a novel two-dimensional hyperchaotic map is considered, and thus the corresponding manifold of conditional symmetry is coined. The unique map has a simple structure but provides direct 2-D offset boosting, which brings the possibility for the construction of conditional symmetry by introducing an absolute value function. The corresponding evolution of the discrete sequences from the system is verified by the circuit implementation based on the microcontroller of CH32V307. The pseudorandom data from the map increases its adaptability for applications in information security. The hyperchaotic sequence-injected Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) show their improved performance in the optimization algorithm. Robot path planning experiments confirm that all three algorithms exhibit superior convergence performance, global search capability, and path smoothness compared with the original algorithms.

To solve the problem of limited generalization ability that is widely existing in lightweight models used for leaf disease detection, this paper puts forward a lightweight detection model named CE-Fusion Botanic, which is based on the adaptive control of local–global information fusion. Therefore, this model includes a globally guided dynamic gating fusion mechanism that dynamically adjusts fusion weights between local features, such as spot lesions, and global semantic features, such as symptoms of systemic infection, thus realizing adaptive perception of the dual characteristics of plant diseases. Hence, the local information extraction branch combines an improved MobileNetV3-Small structure and a CBAM attention mechanism, while the global information extraction branch uses a lightweight Vision Transformer (ViT) design called EffiViT. Comprehensive contrast experiments were carried out by using seven mainstream lightweight models on the PlantVillage tomato disease subset, the full-category PlantVillage leaf disease dataset, and the Grapevine leaf disease dataset. Models were divided into large-scale, medium-scale, and small-scale groups according to the number of parameters. The results show that CE-Fusion Botanic is significantly better than comparative methods in both detection accuracy and generalization performance, and at the same time, it keeps a lightweight profile, which demonstrates superior cross-dataset adaptation capabilities.

Accurate detection of cotton top buds is a key prerequisite for precision topping. However, because of the small size of cotton top buds and their high visual similarity to complex canopy backgrounds, high-efficiency detection in natural fields still faces challenges. To address these challenges, we propose an advanced lightweight, real-time, and accurate detection model, MC-RT-DETR. Specifically, we replace the original ResNet18 backbone network of RT-DETR-r18 with MobileNetV3, which significantly reduces the model parameters and computational complexity. Then, we integrate the C2f module into the feature fusion network, which effectively enhances the multiscale feature representation. We employ Wise-IoU to optimize bounding box regression, achieving high-IoU localization accuracy for small targets. Finally, strategies such as increasing the bounding box regression loss function and using MixUp and Copy-Paste data enhancement for decoupled optimization are combined, which improves the robustness of the model in the context of complex crowns. Experiments show that MC-RT-DETR achieves mAP@50 of 96.00% and mAP@50–95 of 83.59% while reducing the number of parameters and GFLOPs by 47.2% and 57.3%, respectively. Edge deployment on Jetson Orin Nano demonstrates that the model achieves a frame rate of 65.9 FPS, fully satisfying real-time field requirements.