To achieve efficient and accurate detection of blueberry fruit ripeness, this study proposes a lightweight yet high-performance object detection model—CES-YOLO. Designed for real-world blueberry harvesting scenarios, the model addresses key challenges such as significant visual differences across ripeness stages, complex occlusions, and small object sizes. CES-YOLO introduces three core components: the C3K2-Ghost module for efficient feature extraction and model compression, the SEAM attention mechanism to enhance the focus on critical fruit regions, and the EMA Head for improved detection of small and densely packed targets. Experiments on a blueberry ripeness dataset demonstrated that CES-YOLO achieved 91.22% mAP50, 69.18% mAP95, 89.21% precision, and 85.23% recall, while maintaining a lightweight structure with only 2.1 M parameters and 5.0 GFLOPs, significantly outperforming mainstream lightweight detection models. Extensive ablation and comparative studies confirmed the effectiveness of each component in improving detection accuracy and reducing false positives and missed detections. This research offers an efficient and practical solution for automated recognition of fruit and vegetable maturity, supporting broader applications in smart agriculture, and provides theoretical and engineering insights for the future design of agricultural vision models. To further demonstrate its practical deployment capability, CES-YOLO was successfully deployed on the NVIDIA Jetson Orin Nano platform, where it maintained real-time detection performance, with low power consumption and high inference efficiency, validating its suitability for embedded edge computing scenarios in intelligent agriculture.

Flexible sensors have emerged as essential components in next-generation technologies such as wearable electronics, smart healthcare, soft robotics, and human–machine interfaces, owing to their outstanding mechanical flexibility and multifunctional sensing capabilities. Despite significant advancements, challenges such as the trade-off between sensitivity and detection range, and poor signal stability under cyclic deformation remain unresolved. To overcome the aforementioned limitations, this work introduces a high-performance soft sensor featuring a dual-layered electrode system, comprising silver nanoparticles (AgNPs) and a composite of multi-walled carbon nanotubes (MWCNTs) with carbon black (CB), coupled with a laser-engraved crack-gradient microstructure. This structural strategy facilitates progressive crack formation under applied strain, thereby achieving enhanced sensitivity (1.56 kPa⁻¹), broad operational bandwidth (50–600 Hz), fine frequency resolution (0.5 Hz), and a rapid signal response. The synergistic structure also improves signal repeatability, durability, and noise immunity. The sensor demonstrates strong applicability in health monitoring, motion tracking, and intelligent interfaces, offering a promising pathway for reliable, multifunctional sensing in wearable health monitoring, motion tracking, and soft robotic systems.