Precise, non-destructive detection of fruit maturity is a cornerstone of modern precision agriculture, directly impacting harvest scheduling and post-harvest quality control. In the case of strawberries (Fragaria × ananassa), in-field automated assessment is persistently hampered by the fruit’s diminutive size, subtle physiological colour transitions, and frequent occlusion by foliage. To overcome these limitations, we developed SMLO-YOLO, a specialised lightweight vision system designed to reliably detect different maturity stages on edge devices under complex orchard conditions. The proposed architecture incorporates a Cross-Scale Aggregation Neck (HDP-Neck) driven by entropy-guided dynamic sampling, which effectively concentrates computational resources on fruit regions while filtering background noise. Additionally, we introduce a Shape-aware Intersection-over-Union (ShapeIoU) loss and a Boundary- and Class-aware Knowledge Distillation (BCKD) strategy to specifically address the challenge of detecting overlapping clusters and low-maturity fruits. Validation on custom datasets collected from commercial orchards in Sichuan and Shanxi demonstrated that the final SMLO-YOLO model, after BCKDloss-based knowledge distillation, achieved an mAP50 of 92.4% at an inference speed of 256.41 FPS, with 6.49 M parameters and 15.0 GFLOPs. These metrics indicate that the system successfully balances high-throughput detection with the non-harvestable low-maturity fruits of agricultural robotics, offering a robust tool for objective, real-time maturity monitoring.

Since deep learning has been applied to medical image analysis, convolutional neural networks (CNNs) and Vision Transformers (ViTs) have become core architectures for medical image classification tasks. However, CNNs are constrained by local receptive fields and cannot effectively model long-range context-dependent correlations in medical images; ViTs face deployment challenges in high-resolution medical image tasks due to the quadratic computational complexity of self-attention. While State Space Models (SSMs) represented by Mamba offer a new solution with linear complexity, they suffer from redundant directional modeling and low parameter efficiency in direct medical image applications. This study proposes the efficient state space enhancement framework (MedMamba-ESS), integrating the SS2D-Top2 adaptive directional scanning mechanism (reducing SSM submodule FLOPs by ~37%) and the G-SSM grouped parameter sharing module (achieving 3–4% parameter compression and 4.4% accuracy improvement via regularization). Validated on 14 public datasets (9 imaging modalities, 9 anatomical regions, >240,000 images), MedMamba-ESS achieves superior performance at 2.0G FLOPs: its Overall Accuracy (OA) is 3% higher than the baseline MedMamba-Tiny on non-MedMNIST datasets (3.13% higher than other mainstream models) and 3% higher on MedMNIST datasets (1% higher than others). Ablation experiments confirm that the two modules synergistically reduce parameters by 1.05% and boost accuracy by 4.6%. This study overcomes the technical limitations of traditional SSMs in medical imaging applications, achieving synergistic improvements in both model performance and computational efficiency. It provides an architecture optimization solution that combines practicality and generalizability for the implementation of SSMs in medical image analysis.