This study was conducted to investigate the effects of various irrigation water (W) and nitrogen (N) levels on growth, root-shoot morphology, yield, and irrigation water use efficiency of greenhouse tomatoes in spring–summer and fall–winter. The experiment consisted of three irrigation water levels (W: 100% of crop evapotranspiration (ETc), 80%, and 60% of full irrigation) and three N application levels (N: 100%, 75%, and 50% of the standard nitrogen concentration in Hoagland’s solution treatments equivalent to 15, 11.25, 7.5 mM). All the growth parameters of tomato significantly decreased (p < 0.05) with the decrease in the amount of irrigation and nitrogen application. Results depicted that a slight decrease in irrigation and an increase in N supply improved average root diameter, total root length, and root surface area, while the interaction was observed non-significant at average diameter of roots. Compared to the control, W₈₀ N₁₀₀ was statistically non-significant in photosynthesis and stomatal conductance. The W₈₀ N₁₀₀ resulted in a yield decrease of 2.90% and 8.75% but increased irrigation water use efficiency (IWUE) by 21.40% and 14.06%. Among interactions, the reduction in a single factor at W₈₀ N₁₀₀ and W₁₀₀ N₇₅ compensated the growth and yield. Hence, W₈₀ N₁₀₀ was found to be optimal regarding yield and IWUE, with 80% of irrigation water and 15 mM of N fertilization for soilless tomato production in greenhouses.

Nitrogen and phosphorus limitation affect the growth, development, and productivity of lettuce, which exert a marked influence on metabolites. To compare the influence of low-nitrogen and low-phosphorus stresses on various metabolites of lettuce leaves, experiments were performed under three conditions of treatment—low-nitrogen stress, low-phosphorus stress, and normal samples. Metabolomic analyses were conducted based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. Principle components analysis yielded distinctive clustering information among the holistic samples; fold change analysis, t-test and orthogonal partial least square discriminant analysis were used for the selection of metabolic biomarkers. Ten pathways were selected which were significantly enriched by metabolic biomarkers. Metabolic biomarkers were screened by fold change (FC) value, p-value and variable importance in the projection (VIP) value, low-nitrogen and low-phosphorus stresses caused an increase in 16 metabolites (FC > 2, p-value < 0.05, VIP > 1) and a decrease in 26 metabolites (FC < 0.5, p-value < 0.05, VIP > 1). Outside of these, our results showed that inositol, p-hydroxybenzoic acid, stachyose, dinoseb, and 7, 8-dihydroxycoumarin increase in low-nitrogen stress samples. Low-phosphorus stress caused accumulation of citrate, isocitrate, l-5-oxoproline, succinate, and histamine, which may be considered potential metabolic biomarkers. The metabolites could be used to monitor the nitrogen and phosphorus status of lettuce and to guide appropriate fertilization regimens.

The aim of this study was to describe the sigmoidal growth behaviour of a lettuce canopy using three nonlinear models. Gompertz, Logistic and grey Verhulst growth models were established for the top projected canopy area (TPCA), top projected canopy perimeter (TPCP) and plant height (PH), which were measured by two machine vision views and 3D point clouds data. Satisfactory growth curve fitting was obtained using two evaluation criteria: the coefficient of determination (R²) and the mean absolute percentage error (MAPE). The grey Verhulst models produced a better fit for the growth of TPCA and TPCP, with higher R² ($R_{TPCA}^2=0.9097, R_{TPCP}^2=0.8536$) and lower MAPE ($MAP{E}_{TPCA}^{}=0.0284, MAP{E}_{TPCP}^{}=0.0794$) values, whereas the Logistic model produced a better fit for changes in PH ($R_{PH}^2=0.8991,MAP{E}_{PH}^{}=0.0344$). The maximum growth rate point and the beginning and end points of the rapid growth stage were determined by calculating the second and third derivatives of the models, permitting a more detailed description of their sigmoidal behaviour. The initial growth stage was 1–5.5 days, and the rapid growth stage lasted from 5.6 to 26.2 days. After 26.3 days, lettuce entered the senescent stage. These inflections and critical points can be used to gain a better understanding of the growth behaviour of lettuce, thereby helping researchers or agricultural extension agents to promote growth, determine the optimal harvest period and plan commercial production.

Lettuce is a commercially significant leafy vegetable worldwide. Potassium (K) is an essential macronutrient for lettuce growth and development and significantly impacts its metabolites. Biomarkers that are indicative of variations in the K status of lettuce before the occurrence of biophysical changes (e.g., leaf or canopy morphological, textural and color features), can be adopted to determine the early K status of lettuce. To understand the effect of low K on diverse metabolites, we examined the metabolic response of lettuce in a closed cultivation room under controlled conditions. The evaluation was based on large-scale untargeted metabolomics assay of the K group using liquid chromatography-mass spectrometry. Data were analyzed with a fold-change (FC) analysis, t-test, and orthogonal partial least square-discriminant analysis. Fifty-two metabolites were classified into two groups by the FC, p, and the variable importance in projection (VIP). Low K led to an increment in 40 metabolites (FC > 2, p < 0.05, VIP > 1) and a decrease in 12 metabolites (FC < 0.5, p < 0.05, VIP > 1). Ten pathways were significantly enriched with metabolic biomarkers. In light of the complex interactive relationships among N, P, and K, the top five biomarkers were screened further by VIP > 4.00. Adenosine, FA 18:1+3O, uridine, cis-aconitate, and D(-)-gulono-gamma-lactone showed an increase in low-K stress samples, and may be considered potential metabolic biomarkers. This study validates the impact of low K on lettuce metabolism, and identifies biomarkers that can be used to monitor the K status in lettuce.

To mitigate the challenges posed by the confined spatial environment of greenhouses and various obstacles that frequently cause non-line-of-sight (NLOS) communication issues in ultra-wideband (UWB) localization systems, leading to localization difficulties and low accuracy, we propose a real-time greenhouse localization system that recognizes UWB ranging values prior to correction. First, the initial ranging value is obtained through double-sided two-way ranging (DS-TWR). Subsequently, a communication state identifier is designed based on the residual distribution of ranging values across two UWB communication modes. A correction model is then established by analyzing the causes of ranging value deviations. Finally, the NLOS localization deviation is corrected using an improved unscented Kalman filter (IUKF) algorithm. Experimental results in the greenhouse environment demonstrate that the proposed algorithm enhances positioning accuracy by 68% compared to the uncorrected localization method, offering a valuable reference for localization services in greenhouse settings.

This study presents a neural-network-based method for on-demand design of terahertz metasurface sensors, aimed at detecting plant endogenous and exogenous molecules. The approach uses target performance indicators (constructed via fingerprint peaks) as inputs and structural parameters as outputs, employing a neural network to map the complex relationship between them. Two single-resonant-peak metasurface sensors were developed to detect abscisic acid and gibberellic acid. The abscisic acid metasurface sensor achieved an average MSE of 5.66 × 10⁻⁶ and R_ER of 0.167%, while the gibberellic acid metasurface sensor had an average MSE of 8 × 10⁻⁷ and R_ER of 0.086%. Their resonant peaks highly matched the substance fingerprint peaks, enabling specific detection. Metasurface sensors’ sensitivities were effectively controlled using correlation analysis and neural networks, achieving remarkable levels of 156.7 and 150.1 GHz/RIU, allowing trace detection. Three dual-resonant-peak metasurface sensors were designed to improve the detection specificity for chlorophyll and folpet and to detect chlorophyll and folpet simultaneously. These metasurface sensors exhibited average MSEs of 1.4 × 10⁻⁵, 1.6 × 10⁻⁶, 1.35 × 10⁻⁵ and R_ERs of 0.27%, 0.088%, 0.20%. The model also worked for four other plant-related molecules, proving its strong generalization ability. Overall, for different application scenarios of exogenous and endogenous molecules in plants, the on-demand design methodology offers a whole new set of ideas for quickly designing and widely applying metasurface sensors with suitable performance indicators.