Considering the growing demand for modern facility agriculture, it is essential to develop non-destructive technologies for assessing lettuce nutritional status. To overcome the limitations of traditional methods, which are destructive and time-consuming, this study proposes a multimodal non-destructive nitrogen detection method for lettuce based on multi-source imaging. The approach integrates terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI) to achieve rapid and non-invasive nitrogen detection. Spectral imaging data of lettuce samples under different nitrogen gradients (20–150%) were simultaneously acquired using a THz-TDS system (0.2–1.2 THz) and a NIR-HSI system (1000–1600 nm), with image segmentation applied to remove background interference. During data processing, Savitzky–Golay smoothing, MSC (for THz data), and SNV (for NIR data) were employed for combined preprocessing, and sample partitioning was performed using the SPXY algorithm. Subsequently, SCARS/iPLS/IRIV algorithms were applied for THz feature selection, while RF/SPA/ICO methods were used for NIR feature screening, followed by nitrogen content prediction modeling with LS-SVM and KELM. Furthermore, small-sample learning was utilized to fuse crop feature information from the two modalities, providing a more comprehensive and effective detection strategy. The results demonstrated that the THz-based model with SCARS-selected power spectrum features and an RBF-kernel LS-SVM achieved the best predictive performance (R² = 0.96, RMSE = 0.20), while the NIR-based model with ICO features and an RBF-kernel LS-SVM achieved the highest accuracy (R² = 0.967, RMSE = 0.193). The fusion model, combining SCARS and ICO features, exhibited the best overall performance, with training accuracy of 96.25% and prediction accuracy of 95.94%. This dual-spectral technique leverages the complementary responses of nitrogen in molecular vibrations (THz) and organic chemical bonds (NIR), significantly enhancing model performance. To the best of our knowledge, this is the first study to realize the synergistic application of THz and NIR spectroscopy in nitrogen detection of facility-grown lettuce, providing a high-precision, non-destructive solution for rapid crop nutrition diagnosis.

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.

Electrostatic spraying reduces the pesticide loss and environmental pollution caused by droplet drift, reduces the health risk of workers in the process of spraying, and has positive significance for agricultural sustainable development. This is because the electrostatic spray charged droplets will deflect around the target leaf, increasing the amount of pesticide deposition on the surface of the target leaf. In this paper, the optimum charging voltage is +10 kV under the spray pressure of 0.3 MPa by charge-mass ratio measurement and droplet particle size measurement experiments. The charge-to-mass ratio is the highest (3.06 mC/kg) and the Volume Median Diameter (VMD) is the smallest (92.86 μm) when the charging voltage is +10 kV in the charging range from 0 to +12 kV. After the spray pressure (0.3 MPa) and charging voltage (+10 kV) were selected, the droplet movement track, droplet quantity and droplet deposition quality (coverage rate and distribution uniformity) near tomato leaves were compared between electrostatic spray and non-electrostatic spray through observation experiment of droplet deposition movement and indoor sedimentation experiment. Finally, through these two experiments, it was proved from many angles that electrostatic spraying could effectively improve the deposition quality of pesticides on the surface of tomato leaves and reduce the loss of pesticides caused by droplet drift.

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.