Sparassis spp., commonly known as the cauliflower mushroom, are prized for their unique aromas and nutraceutical value, yet their volatile profiles and chemical markers remain uncharacterized. This study aimed to characterize the volatile profiles of seven Chinese Sparassis mushrooms and identify region-related differences in aroma profiles using HS-SPME-GC-MS. The aroma characteristics of each Sparassis species were investigated through the analysis of volatile compounds. A total of 474 volatile organic compounds (VOCs) were identified, primarily including terpenoids, esters, and heterocyclic compounds, among which the eight-carbon VOCs were particularly notable. Specific compounds, such as 3-octanol, phenylethyl alcohol, and benzeneacetaldehyde,.alpha.-ethylidene- were identified as potential contributors to aroma based on known odor descriptors. Notably, emerged as a potential marker for differentiating cultivated and wild samples for differentiating cultivated from wild varieties, alongside pinocarvone and cedrol. Multivariate statistical analysis highlighted significant regional variations in aroma profiles, influenced by environmental factors, hosts, and genetic differences. Comparative analyses revealed differences in aroma-related profiles, with S. latifolia showing diverse regional aromas and S. subalpina exhibiting dominance in exhibiting dominance in compounds associated with sweet and fruity odors. These findings enhance the understanding of Sparassis mushrooms chemical diversity and sensory traits, supporting their culinary, cultivation, and commercial potential.

Powdery mildew is a pervasive fungal disease that poses a significant threat to a wide range of crops globally. Although the MLO (Mildew Locus O) is involved in regulating powdery mildew resistance, the mechanism underlying mlo-mediated resistance to powdery mildew remains unclear. In this study, we identified a recessive gene confers powdery mildew resistance in TS-65 by inhibiting production of secondary spores, a mechanism which differs from that mediated by the tomato ol-2 locus. Through bulk segregant analysis and gene editing technology, we identified a 1-bp deletion in the SlMLO1 coding region of TS-65. This frameshift mutation causes the loss of C-terminal intracellular domain and is responsible for the observed resistance phenotype in TS-65. Furthermore, treatment with CaCl2 was found to promoted both conidia formation and SlMLO1 expression, whereas treatment EGTA inhibited conidia formation. Additionally, the calcium ion content in the leaves was reduced in slmlo1 and TS-65 compared to AC and TS-623, respectively. These findings indicated that SlMLO1 functions to promote susceptibility to powdery mildew, presumably by elevating calcium levels in leaves. This research offers novel insights into the mechanism underlying mlo-mediated resistance to powdery mildew and presents valuable implication for the development of powdery mildew resistant crop varieties through breeding programs.

Botrytis cinerea is a devastating necrotrophic pathogen threatening global agriculture. Although modulating light intensity holds promise for sustainable disease management, the regulatory strategies and underlying mechanisms remain largely undefined. Three light intensity levels (200, 500 and 800 µmol·m-2·s-1) were applied in this study. By integrating multilevel pathological and cytological observations with transcriptional and metabolic pathway analyses, we systematically investigated how light intensity regulates defence responses against B. cinerea in tomato (Solanum lycopersicum). The results revealed that 200 µmol·m-2·s-1 light most effectively attenuated disease progression by orchestrating a multilayered defence network. This network encompasses stomatal closure to restrict pathogen invasion, phenylalanine-derived lignin biosynthesis for cell wall reinforcement, upregulation of pathogenesis-related proteins to facilitate pathogen degradation and maintenance of redox homeostasis to mitigate oxidative damage. These coordinated responses, rather than direct pathogen inhibition by light intensity, collectively restrict B. cinerea invasion. Application of exogenous phenylalanine and the PAL inhibitor 2-aminoindan-2-phosphonic acid (AIP) further validated that light intensity dictates metabolic flux partitioning within the phenylpropanoid pathway, a core mechanism for enhanced resistance. This study provides the first evidence that low-light-induced resistance to B. cinerea stems from phenylpropanoid metabolic reprogramming and ROS homeostasis, offering novel insights into light-modulated plant defence against necrotrophic pathogens.

Abscission is a developmentally programmed cell separation process initiated by the peptide INFLORESCENCE DEFICIENT IN ABSCISSION (IDA), which promotes organ shedding through reactive oxygen species (ROS) signaling. While the IDA receptor HAESA and downstream mitogen-activated protein kinases (MAPKs) are conserved across plants, the mechanism linking IDA perception to ROS production has remained enigmatic. Here, we identify L-3,4-dihydroxyphenylalanine (L-DOPA) as a pH-sensitive redox effector that directly couples SlIDL6-HAESA-like-1/2/3 (SlHSL1/2/3) signaling to a localized H2O2 burst during pedicel abscission in tomato (Solanum lycopersicum). Activation of the SlIDL6-SlHSL1/2/3 module recruits SlMAPK1/2/3, which phosphorylate the transcription factor SlWRKY73 at Ser-178, stabilizing it and driving expression of SlCYP76B23, a rate-limiting enzyme in L-DOPA biosynthesis. Concurrently, SlMAPK1/2/3 activity induces cytoplasmic alkalinization in the abscission zone, creating a permissive environment for the non-enzymatic oxidation of L-DOPA into H2O2. This spatially restricted oxidative signal activates cell wall-modifying enzymes, culminating in organ separation. Our findings reveal an RBOH-independent ROS-generating pathway and establish L-DOPA not as a passive metabolic intermediate, but as a developmentally deployed redox messenger that translates peptide signals into redox-driven cell separation.

To enhance the resilience of Chinese solar greenhouses (CSGs) against extreme cold, the synergistic mechanism between the passive thermal curtain (TC) and active earth-air heat exchanger (EAHE) is investigated. Full-scale unsteady computational fluid dynamics (CFD) models were developed for four configurations (control, EAHE, TC, TC-EAHE) to quantify their thermal environmental effects. Results demonstrate that the standalone EAHE increased the average nighttime temperature by only 0.64 °C during the heating period. In contrast, TC and TC-EAHE increased the minimum temperature by 2.35 °C and 2.91 °C, and average heating-period temperature by 3.90 °C and 4.32 °C, respectively. The integration of TC reduced EAHE soil heat extraction efficiency by 31.1% while decreasing shallow soil heat release. The heat load reduction rate of the TC-EAHE increased with decreasing outdoor air temperature, achieving a maximum heat loss reduction of 15.49%. During consecutive overcast conditions, TC-EAHE elevated average and minimum temperatures by 2.86 °C and 3.28 °C after heat preservation quilt deployment. EAHE airflow velocity had negligible overall impact, when the airflow velocity increased from 1 m·s−1 to 5 m·s−1, the average air temperature only rose by 0.22 °C. An airflow velocity of 3 m·s−1 was found to be optimal, compared with the 1 m·s−1 condition, the extracted heat increased by 96.95%, and the minimum air temperature rose by 10.49%. Over an entire winter production cycle, the total carbon emission of the TC-EAHE reached 0.24 t CO2. This study confirms the application potential of the TC-EAHE for CSGs in cold regions.

The daylighting performance of solar greenhouses is fundamentally governed by the synergistic interaction between environmental conditions and structural parameters, critically determining overall productivity. While conventional solar radiation models typically assume geometrically invariant, smooth-arc profiles to assess light interception, practical installations exhibit wave-like surface distortions from east-west deformations. These deformations induce spatiotemporal heterogeneity in light incidence angles and transmittance characteristics, consequently diminishing light interception capacity. To address this limitation, this study develops an innovative solar radiation numerical model for Photonic-Film Synergistic Transmission Dynamics using vector-based computational methods, enabling precise quantification of solar incidence angles, transmittance dynamics, and interception efficiency across deformed surfaces. We further propose two novel deformation-specific metrics: the Deformation-induced Light Loss Index (DLLI) and Cumulative Transmittance Loss Rate (CTLR). Results demonstrate a 4.30% reduction in average diurnal equivalent transmittance, Solar radiation interception for the overall greenhouse, ground surface, back wall, and back slope decreased by 2.13%, 2.37%, 2.90%, and 2.02%, respectively. Light transmittance losses induced by deformation exhibit positive proportionality to cloud cover fraction, Annual variations revealed DLLI and CTLR ranges of 0.62-1.63% and 0.87-2.81%, respectively. This work establishes a Multiphysics framework that couples radiation dynamics with structural deformation mechanics, providing universally applicable computational tools for flexible transparent materials. The PFSTD model and associated metrics collectively establish a theoretical toolkit and quantitative foundation for dynamic, precision regulation of winter light environments in high-latitude cold regions.

The influence of crops on the microenvironment of naturally ventilated solar greenhouses is substantial. Recent advances in greenhouse ventilation research have not been matched by sufficient quantitative evaluation of crop induced effects on the internal environment. A validated CFD model was developed for a modern solar greenhouse in Shenyang, China, to analyze microclimate distributions with and without crops under north-south and east-west ridge orientations, and to evaluate the effects of varying wind directions on the east-west layout. Results indicate that crop presence increases temperature heterogeneity along the east-west, north-south, and vertical directions, with standard deviations rising by 45.9%, 8.3%, and 49.3%, respectively. For the simulated scenarios, the north-south orientation provides superior summer cooling, yielding a canopy temperature 4.55 K lower than the east-west layout. In winter, the east-west orientation retains heat more effectively, maintaining a 3.23 K higher canopy temperature and a 26.8% lower temperature standard deviation. Easterly or westerly winds amplify horizontal temperature gradients under the east-west configuration, with the east-west canopy difference reaching 15.91 K. These findings offer a quantitative basis for evaluating crop impacts on greenhouse microclimate and for designing environmental regulation strategies. They also provide theoretical support for optimizing seasonal ventilation management and ridge orientation in cultivation systems.

Facility agricultural greenhouses are prone to forming low-temperature and high-humidity environments, which frequently induce the concurrent outbreak and spread of fungal diseases such as downy mildew, gray mold, and black spot disease. However, existing predictive studies often focus on individual diseases or environmental factors, making it difficult to meet the precise disease control needs in production settings with complex diseases. To address this issue, this study constructs an Infection Suitability Index (ISI) that quantifies humidity suitability using Beta functions and piecewise linear functions, enabling nonlinear coupling of temperature and humidity. With this as the core input, multiple linear regression (MLR), support vector regression (SVR), and MLR-SVR hybrid predictive models are developed. The results indicate that ISI is significantly negatively correlated with the incubation period of all three diseases. The MLR-SVR hybrid model demonstrated the best prediction performance, with a coefficient of determination (R2) ranging from 0.962 to 0.982 and prediction accuracy between 88.5% and 92.3%. It shows excellent applicability in greenhouse environments. This study provides a new method for multi-disease collaborative early warning in facility cucumber cultivation, and the proposed ISI index along with the hybrid model system has important practical value for intelligent pest control and precision agriculture.

Assembled solar greenhouses decouple traditional walls' thermal insulation and heat storage functions, failing to meet high-latitude overwintering production demands. Mounting an active solar water curtain (ASWC) on the north wall resolves low nighttime temperatures, yet systematic design guidelines remain lacking. Based on onedimensional steady-state heat transfer theory, this study analyzed the correlation between water flow rate and convective heat transfer coefficient, optimized the system hydraulically, and conducted winter experiments in Shenyang. Results showed indoor nighttime temperatures rose by 3.2 degrees C, with 63 % of tank-stored heat utilized overnight, temperatures above 8 degrees C accounted for 82 % of nighttime hours. Daily effective accumulated temperature increased by 19.8 %, and temperatures stayed above 7.5 degrees C even during consecutive extreme low temperatures. The ASWC's maximum applicable flow rate was 4.2 m3/h. After hydraulic optimization, costs dropped by 26 %, with the investment payback period shortened to 1.4 years.

Pseudosclerotia of Sparassis latifolia are an underutilized by-product and a promising source of functional polysaccharides. This study compared hot-water extraction (HWE) with ultrasound-assisted extraction (UAE) and optimized UAE using response surface methodology. The optimized process (480 W, 72 min, 58 mL/g) increased yield to 39.91%, compared with 29.89% for HWE. Fourier-transform infrared spectroscopy and X-ray diffraction indicated that UAE preserved the main structural features and retained a predominantly amorphous structure, while HPGPC and monosaccharide analysis indicated a lower molecular weight and slightly different monosaccharide molar ratios; both fractions were mainly glucose-rich. Microscopic and colloidal analyses showed a looser microstructure, smaller particle size, and improved dispersion stability (higher absolute zeta potential). Functionally, the ultrasound-derived polysaccharide displayed stronger in vitro antioxidant capacity and inhibited α-amylase and α-glucosidase at 3.0 mg/mL. These results demonstrate that UAE is an efficient, non-derivatizing strategy to valorize S. latifolia pseudosclerotia into functional polysaccharide ingredients for food-related applications.

High energy consumption in winter greenhouses challenges agricultural sustainability. This study integrates nonlinear cost-volume-profit (CVP) and data envelopment anal-ysis (DEA) to balance cucumber yields with energy costs in Northern China. Results show that while a 19°C treatment (T3) maximizes yield, it suffers from diminishing marginal returns (MR/MC = 0.72) and high sensitivity to energy price fluctuations. Conversely, the 16°C treatment (T2) emerged as the sole energy-efficient and econom-ically resilient configuration. We conclude that 16°C is the global optimal point for balancing biological potential and resource efficiency, providing a scientific basis for precision thermal management in cold-region greenhouses.

High energy consumption in winter greenhouses poses a challenge to agricultural sustainability in Northern China, where heating costs typically account for 40-60% of total operating expenses. This study integrated a non-linear cost-volume-profit (CVP) analysis and data envelopment analysis (DEA) to balance cucumber yields with escalating energy costs. A single-season, single-factor experiment was conducted using insulated greenhouse compartments to evaluate four night temperature gradients (10 degrees C, 13 degrees C, 16 degrees C, and 19 degrees C). Results showed that although the 19 degrees C treatment (T3) achieved the highest marketable yield, it was associated with lower economic return because heating costs increased disproportionately. Among the four tested nighttime temperatures, the 16 degrees C treatment (T2) showed the most favorable observed combination of yield, net profit, and DEA-based efficiency indicators under the present experimental conditions. However, because the experiment was conducted in a single season within a compartment-based greenhouse system and the CVP relationship was fitted using treatment-level means, this result should be interpreted as a preliminary and condition-specific finding rather than as definitive evidence of a universal optimum temperature. Accordingly, the integrated bio-economic framework presented here is best viewed as an analytical prototype that merits further validation across multiple seasons, cultivars, and greenhouse systems.

Soil alkalinity severely limits the productivity of strawberry, a high-value horticultural crop. The root endophytic fungus Piriformospora indica enhances plant stress resilience, yet the systemic mechanisms underlying its promotion of alkaline tolerance remain poorly understood. This study employed an integrated transcriptomic and metabolomic approach to elucidate these mechanisms in strawberry under alkaline stress. Inoculation with P. indica significantly alleviated stress-induced growth inhibition, improving biomass accumulation and leaf development. Metabolomic profiling identified 1,352 DAMs, predominantly flavonoids and phenolic acids. Transcriptome analysis revealed 19,689 DEGs enriched in oxidoreductase activity, hormone signaling, and secondary metabolism. Multi-omics integration highlighted coordinated changes in the metabolism of pyruvate,alanine, aspartate and glutamate. P. indica maintained the balance of carbon and nitrogen allocation in strawberry under alkaline stress, an effect linked to the downregulation of argG, asnB, and GLT1. These findings suggest a putative systemic metabolic mechanism by which P. indica may enhance alkaline tolerance, potentially through rebalancing primary metabolism. This offers molecular insights into fungal-mediated stress adaptation in strawberry and supports its potential as a sustainable bio-inoculant for improving productivity in alkaline soils.

Oxalic acid (OA) is a fungal organic acid closely associated with host acidification, oxidative stress, and defense modulation, yet its biochemical effects on conifer root defense metabolism remain poorly understood. Sparassis latifolia, a facultative parasitic fungus associated with pine root disease, accumulated extracellular OA up to approximately 6.08 mM in liquid culture. In this study, we used exogenous OA treatments to simulate fungus-relevant chemical stress and investigated the physiological, transcriptomic, and metabolomic responses of Pinus tabuliformis roots. Seedlings were exposed to two levels of OA stress (2 and 6 mM), representing moderate and high OA accumulation scenarios, respectively, to investigate dose-dependent defense responses. OA caused concentration and duration dependent root injury, including browning, shrinkage, ultrastructural collapse, and increased accumulation of H2O2, O2⁻, and malondialdehyde. Prolonged exposure to 6 mM OA reduced antioxidant enzyme activities and depleted ascorbate and glutathione, indicating impaired redox buffering capacity. Widely targeted metabolomics and transcriptomics revealed extensive reprogramming of phenylpropanoid biosynthesis, flavonoid biosynthesis, glutathione metabolism, and plant hormone signaling. In particular, high-concentration OA (6 mM) suppressed key phenylpropanoid and flavonoid pathway genes, including PAL, 4CL, CHS, CAD1, and LAR, and reduced defense-related metabolites such as catechin, epicatechin, and quercetin. WGCNA further identified gene modules associated with flavonoid metabolites, redox regulation, and hormone related compounds. These findings indicate that fungus-relevant OA exposure not only induces oxidative injury but also uncouples stress-responsive transcriptional reprogramming from the sustained production of phenylpropanoid-flavonoid defense metabolites, providing biochemical insight into the OA-associated chemical environment of S. latifolia-pine interactions.

Low-temperature stress significantly affects plant growth and development. NAM, ATAF, and CUC transcription factors (NAC TFs) play a crucial role in enhancing plant tolerance to low-temperature, drought, and salinity stresses by promoting the initiation of stress responses and signal transduction. In this study, we found that the NAC transcription factor gene NON RIPENING (SlNOR) is significantly induced by low-temperature and jasmonic acid (JA), and it promotes JA biosynthesis by directly targeting and regulating LIPOXYGENASE 10/LIPOXYGENASE 11 (SlLOX10/SlLOX11), thereby positively regulating low-temperature tolerance of tomato (Solanum lycopersicum). Both SlNOR- and SlLOX11-overexpressing lines exhibited a significant increase in JA accumulation under low-temperature stress, reducing excessive reactive oxygen species (ROS) accumulation. SlNOR interacted with V-MYB AVIAN MYELOBLASTOSIS VIRAL ONCOGENE HOMOLOG 60 (SlMYB60) at the protein level, and SlMYB60 directly targeted and regulated SlLOX10/SlLOX11. The phenotype of SlMYB60 overexpression was consistent with SlNOR overexpression. Additionally, MYELOCYTOMATOSIS 2 (SlMYC2), a key transcription factor in the JA signaling pathway, directly bound to SlNOR and promoted SlNOR expression. This study reveals the core function of the SlMYC2-SlNOR-SlMYB60-SlLOX10/11 module in regulating JA synthesis and clarifies the molecular mechanism through which this module regulates JA accumulation to participate in the low-temperature stress response in tomato.

Soil salinization severely limits plant growth and development, posing a significant threat to agriculture. NAC transcription factors are widely involved in the regulation of various abiotic stresses. In this study, we discovered that SlNAC63 responds to both saline-alkali and jasmonic acid (JA) signaling and enhances saline-alkali tolerance in tomato (Solanum lycopersicum L.) by improving the reactive oxygen species (ROS) scavenging capacity. The experiments of Y1H, EMSA, and ChIP-qPCR confirmed that SlNAC63 directly targets and regulates the expression of tomato SlAOS1 and superoxide dismutase SlSOD4. This, in turn, promotes JA biosynthesis and enhances ROS scavenging ability, thereby positively regulating saline-alkali tolerance in tomato. Phenotypic analysis demonstrated that overexpressing SlAOS1 indeed increases JA accumulation, while overexpressing SlSOD4 significantly improves ROS scavenging under saline-alkali stress. Through Y2H, pull-down, and Co-IP assays, we found that SlNAC63 interacts with SlbHLH71. Furthermore, SlbHLH71 enhances the regulatory effects of SlNAC63 on SlAOS1 and SlSOD4 by interacting with SlNAC63 to strengthen its binding affinity to the promoters of SlAOS1 and SlSOD4, thereby promoting JA accumulation and ROS scavenging, which ultimately strengthens saline-alkali tolerance in tomato. This study unveils the central role of the SlNAC63-SlbHLH71 module in the regulation of saline-alkali stress and clarifies the molecular mechanism by which this module participates in the response of tomato to saline-alkali stress through the regulation of JA accumulation and ROS scavenging.

Abstract The involvement of plant hormones in the graft union healing process is significant, yet how exogenous MeJA affects graft union healing and stress tolerance in cucurbit seedlings remains poorly understood at the molecular level. Exogenous MeJA treatment significantly accelerated the healing process of oriental melon grafted onto squash rootstocks. This treatment led to increased expression of CmWRKY23, CmWRKY69, and the cell adhesion-related gene CmGH9B3 at the graft union, providing evidence for the participation of the CmWRKY23/CmWRKY69-CmGH9B3 module in the MeJA-facilitated graft union-healing process. Furthermore, CmWRKY23 and CmWRKY69 function as transcriptional activators by directly binding to the promoter of CmAOC2, a key jasmonic acid (JA) biosynthesis gene, thereby upregulating CmAOC2 expression. Knockout mutants of CmWRKY23 or CmWRKY69 significantly decreased the low-temperature tolerance of grafted oriental melon seedlings, accompanied by decreased antioxidant enzyme (SOD, POD, CAT) activity, downregulated CmAOC2 expression, and reduced endogenous JA levels. These findings further supported the role of this transcriptional module in regulating low-temperature tolerance through the JA signaling pathway. Overall, our work established that exogenous MeJA promoted graft union healing and enhanced the low-temperature tolerance in grafted oriental melon seedlings. This coordination was achieved via the CmWRKY23/CmWRKY69-CmGH9B3 module for graft union healing and a CmWRKY23/CmWRKY69-CmAOC2 positive feedback loop for low-temperature adaptation. This study revealed a dual regulatory mechanism by which MeJA coordinated both graft union healing and low-temperature resistance, providing new theoretical foundations and practical targets for improving the quality and environmental adaptability of grafted oriental melon seedlings.

Accurate canopy photosynthesis modeling is essential for understanding and optimizing crop growth and yield in greenhouse agriculture. Current models have limited predictive capability due to inadequate responsiveness to dynamic environments and delays in parameter acquisition, making accurate predictions challenging under the complex conditions of solar greenhouses. This study aimed to develop a dynamic canopy photosynthesis model for greenhouse tomatoes, leveraging an IoT sensor network for real-time biological feedback and parameterization. By integrating real-time monitoring with dynamic feedback, the model facilitates precision management of greenhouse tomato cultivation, thereby optimizing plant growth, resource use efficiency, and yield predictability. To achieve this, a non-destructive inversion method based on a dual weighing system was developed, enabling accurate dynamic monitoring of tomato canopy leaf area index (LAI, R-2 >= 0.94) and the photosynthetic leaf area index (LAI(p), R-2 >= 0.91), continuously providing parameters for updating modelling (validated against destructive sampling and actual measurements for trait specifics). Based on accurate parameter acquisition, a dynamic canopy photosynthesis model was developed using LAI(p) as the core variable, integrating above-canopy radiation. A newly developed parameter, which integrates the radiation component of transpiration, serves as a key factor for estimating photosynthesis. This innovative approach allows for accurate daily prediction and assessment of assimilated biomass. Experimental results from 2022 and 2023 showed that the LAI(p) model performed better than the comparison model, showing higher accuracy and adaptability (R-2 = 0.87 and 0.89, NRMSE = 0.17 and 0.12 vs. R-2 = 0.70 and 0.80, NRMSE = 0.26 and 0.15). These results confirmed the reliability of the integrated modeling framework, which forms a closed-loop system connecting real-time plant monitoring, statistical parameter inversion, online model adaptation, and biomass feedback verification. This modeling approach provides a solid foundation for precise growth simulation, sustainably improving yield and quality in solar greenhouse tomatoes, and advancing digital twin-enabled intelligent production.

To meet the requirements of precise temperature regulation in solar greenhouses, traditional machine learning algorithms often suffer from poor adaptability, high energy consumption, and difficulties in integrating agronomic expertise. This study developed an intelligent greenhouse temperature regulation framework based on Model Predictive Control (MPC). The core components of the framework include: (1) an expert-experience-based simulator using a Sparrow Search Algorithm-optimized Random Forest (SSA-RF) model to digitize the temperature management strategies of high-yield farmers into dynamic reference trajectories and (2) a hybrid prediction model (CNN-BiLSTM-Attention) combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Permutation Entropy (CEEMDAN-PE) denoising with a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanism to achieve high-precision multi-step temperature forecasting. Validation in a cucumber solar greenhouse demonstrated that the SSA-RF model achieved an R2 of 0.976 on the test set, showing a significant improvement over the traditional RF model. Compared to the conventional LSTM model, the hybrid prediction model reduced the RMSE to 0.642 and 0.947 for 15 min and 30 min predictions, respectively, with a maximum R2 of 0.994 and excellent generalization capabilities. Finally, these two components were theoretically integrated into an MPC-oriented decision framework. The framework describes how expert reference trajectories, multi-step predictions, actuator constraints, and control increments can be combined in a receding-horizon optimization problem. Since online actuator control data were not available, the MPC module was formulated as a theoretical decision framework rather than a fully validated closed-loop controller. This study provides a modelling basis and technical path for future real-time greenhouse temperature control.

The temperature management models for solar greenhouses exhibit strong regional dependency. Their application in non-target environments often faces significant limitations, frequently resulting in severe temperature control deviations. To address this challenge, seven solar greenhouses located in Lingyuan (Liaoning Province) and Yinan (Shandong Province) were utilized as experimental platforms. Using real-time environmental data collected by the NEUT-80S IoT monitoring system, backpropagation (BP) neural network models were trained and validated. Multiple stepwise regression analysis identified total solar radiation and sunshine duration as the primary determinants of cucumber yield. Based on these findings, a dynamic weight matrix was constructed using a solar radiation clustering algorithm. By integrating similarity distance and similarity coefficient, a microclimate similarity determination logic was established, leading to the proposal of an automatic model selection strategy with an 11-day update cycle. Quantitative validation demonstrated that when the threshold conditions-a similarity coefficient (R) >= 0.6 and a similarity distance (D) <= 0.85-are met, triggering the optimally matched model significantly improves the simulation goodness-of-fit (R2) from 0.6716 in the unmatched state to 0.9851. This strategy effectively achieves the cross-regional adaptation of high-yield temperature management models, providing robust technical support for the advancement of precision protected agriculture.