The production of facility vegetables is of great significance but there are still limitations to this production in terms of yield and quality. Optical sensing technology offers a rapid and non-destructive solution for phenotypic analysis, which is superior to traditional destructive methods. This article reviews and analyzes nine optical sensing technologies, including RGB imaging, and introduces the application of various algorithms in combination with detection principles throughout the entire growth cycle as well as key phenotypic characteristics of facility vegetables. Each technology has its advantages. For example, RGB and multi/high-spectrum technologies are the most frequently used while thermal imaging is particularly suitable for early detection of non-biological and biological stress responses, and these technologies can effectively obtain physiological, biochemical, yield, and quality information about crops. However, current research mainly focuses on laboratory verification and there is still a significant gap when it comes to practical production. Future progress will depend on the integration of multiple sensing technologies, data analysis based on artificial intelligence, and improvements in model interpretability. These developments will be crucial for ultimately achieving precise breeding and intelligent greenhouse management systems, and will gradually transition from basic phenotypic analysis to comprehensive decision support systems.

Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection.

In Chinese solar greenhouses, row spacing of 40–80 cm and persistent 80–95% RH impose two fundamental constraints: conventional wheeled platforms exceed the inter-row turning radius, and unprotected electronics show 15 FPS on <15 W hardware); closed-loop latency compensation (1 ha greenhouses.