Surface-enhanced Raman scattering (SERS) and tip-enhanced Raman scattering (TERS) are fast, convenient, and non-destructive molecular detection techniques, which provide a practical method for studying interfacial reactions with high resolution and accuracy. Both techniques are able to provide quantitative and qualitative information on the chemical properties, conformational changes, order state, and molecular orientation of various surfaces. This paper aims at summarizing the research efforts in the field of SERS and TERS related to tribological systems with a special emphasis on thin film and nanoparticles. This overview starts with a brief introduction for both techniques. Afterwards, it summarizes pros and cons of both techniques related to the advanced characterization of tribologically induced reactions layers. Moreover, the feasibility of both techniques to evaluate the friction and wear performance of new lubricant additives including solid lubricants is discussed. At the end of this review article, the main challenges and future directions in this field are prospected to emphasize the development direction of SERS and TERS in tribology and lubricants.

Accurate classification of fruit maturity is essential for automated grading and robotic manipulation in modern greenhouse cultivation. Most existing methods rely on crop-specific models, severely restricting their scalability in multi-crop scenarios. To overcome this limitation, this study presents SCEA-YOLO, a unified and efficient instance segmentation framework built on YOLOv11s-seg, for simultaneous maturity classification of tomatoes and sweet peppers. To boost feature discrimination, reduce computational redundancy, and alleviate class imbalance, SCEA-YOLO integrates spatial-channel reconstruction convolution and an efficient multi-scale attention mechanism, while replacing the original detection head with the proposed EA-Head. The model is evaluated on a hybrid dataset captured under diverse greenhouse conditions, including varying illumination, fruit occlusion, and overlapping canopies. Its robustness to different viewing angles and camera distances is further validated via deployment on an automated grading robot. Compared with the baseline, SCEA-YOLO enhances classification precision and mAP_50–95 by 5.3% and 2.3% for tomatoes, and 1.2% and 1.4% for sweet peppers, respectively. With only 33.2 GFLOPs, the model satisfies real-time inference demands. Benefiting from its lightweight structure and real-time performance, SCEA-YOLO can be readily deployed on embedded systems and robotic platforms. It offers a practical, unified, and scalable solution for intelligent fruit maturity evaluation in multi-crop greenhouse production.