Although phosphate (Pi) is a necessary nutrient for the growth of aquatic organisms, the presence of excess Pi leads to water eutrophication; thus, it is necessary to accurately determine the content of Pi in water. A method for the determination of trace Pi in aquaculture water was developed based on surface-enhanced Raman spectroscopy (SERS) combined with rhodamine 6G (R6G)-modified silver nanoparticles (AgNPs) as the active substrate. The adsorption of R6G on the AgNP surfaces led to a strong SERS signal. However, in the presence of Pi and ammonium molybdate, phosphomolybdic acid formed, which further associated with R6G to form a stable R6G-PMo₁₂O₄₀³⁻ association complex, thereby hindering the adsorption of R6G on the AgNPs, and reducing the SERS intensity; this sequence formed the basis of Pi detection. The decrease in the SERS intensity was linear with respect to the Pi concentration (0.2–20 μM), and the limit of detection was 29.3 nM. Upon the application of this method to the determination of Pi in aquaculture water, a recovery of 94.4–107.2% was obtained (RSD 1.77–6.18%). This study provides an accurate, rapid, and sensitive method for the trace determination of Pi in aquaculture water, which is suitable for on-site detection.

This article discusses the salt stress in strawberry seedlings under greenhouse conditions in summer. Spectral acquisition equipment was used to obtain spectral data, and the ambient and leaf temperatures were combined to model and analyze the relative chlorophyll content in the strawberry seedling leaves. Four different salt gradients were employed to culture the strawberry seedings: S1 (0 mmol/L NaCl), S2 (50 mmol/L NaCl), S3 (100 mmol/L NaCl), and S4 (150 mmol/L NaCl). The results indicated that the spectral curves of the strawberry seedlings in groups S3 and S4 began to differentiate after day 3 (D3), and their average canopy temperature increased by 2.5 °C and 3.1 °C, respectively. The performance of traditional machine learning models integrating leaf temperature improved by more than 80%. Under each stress treatment, the one-dimensional ResNet model integrated with leaf temperature performed the best, with root mean square and mean absolute errors below 1.7 and 1.5, respectively. These results highlight the potential of incorporating temperature as an additional factor to improve the accuracy of plant stress assessments. By integrating temperature with spectral data, the model enhances the ability to monitor plant health dynamically and provides a more comprehensive understanding of how environmental factors influence plant physiology.