The circadian clock orchestrates photosynthetic and metabolic processes in plants, but the molecular mechanisms underlying the photoperiodic regulation of photosynthetic yield remain poorly understood. Here, we integrated computational modeling and experimental validation to investigate how the skeletal photoperiod modulates photosynthetic efficiency in celery (Apium graveolens L.). Our model revealed that endogenous circadian rhythms dynamically regulate photosynthetic gene expression (e.g., Lhcb1, psbA, RbcS1, and atpA) and photosynthetic parameters (net photosynthetic rate and stomatal conductance) through interactions between clock components (CCA1/LHY and PRR9/PRR7) and light signaling. In particular, the 3L:3D skeleton photoperiod induced the highest 24 h photosynthetic accumulation (a 32% and 22% increase in chlorophyll and nitrogen content, respectively, vs. 12L:12D), outperforming continuous light (LL) and longer photoperiods. Rhythmic peaks of photosynthetic genes aligned with circadian-driven oscillations in the photosynthetic parameters, while a strong negative correlation between the net photosynthetic rate (Pn) and intercellular CO₂ concentration (Ci) emerged under 3L:3D cycles. Model simulations demonstrated robustness in capturing phase-specific gene expression and parameter dynamics across photoperiods, highlighting the role of the circadian clock in optimizing energy use. These results demonstrate that abnormal L/D cycles, particularly 3L:3D, increase photosynthetic yield by enhancing circadian-regulated metabolic coordination, providing a low-energy, high-efficiency strategy for agricultural productivity. This work advances our understanding of photoperiodic manipulation in crop systems and provides a predictive framework for circadian-informed crop management.

Light is a key environmental factor regulating plant photosynthesis, yet the coordinated effects of photoperiod and light intensity on photosynthetic dynamics in non-heading Chinese cabbage remain unclear. Using experimental data, we developed a circadian clock-controlled photosynthesis model integrating photoperiod and light-intensity inputs. The model was calibrated and evaluated against experimental observations and was then used to analyze circadian gene expression, diurnal net photosynthetic rate ($Pn$), light-period net CO₂ assimilation, and the marginal carbon benefit of increasing daily light integral (DLI). Under a constant DLI of 9.216 mol·m⁻²·d⁻¹, extending the photoperiod from 8 to 20 h while reducing light intensity from 320 to 128 µmol·m⁻²·s⁻¹ lowered and delayed the instantaneous $Pn$ peak but progressively increased light-period net CO₂ assimilation. Among the treatments examined, 20L:4D produced the highest net CO₂ assimilation. Moreover, across the simulated DLI range of 5–19 mol·m⁻²·d⁻¹, the 20-h photoperiod consistently produced the highest net assimilation, whereas the marginal carbon benefit of additional light declined by approximately 62%. The results suggest that distributing a fixed daily light input over a longer photoperiod at lower instantaneous light intensity can enhance daily carbon gain within the tested range, whereas increasing DLI produces progressively diminishing photosynthetic returns. This study provides a modeling framework for evaluating photoperiod–light intensity coordination and provides a quantitative basis for optimizing light–environment management in controlled-environment agriculture.

Background/Objectives: Fruit quality in melon is determined by complex traits including sugar accumulation, aroma, flesh color, ripening behavior and disease resistance. Conventional genomic selection provides limited mechanistic interpretation, especially for regulatory and de novo variants. We adapted the EVEE interpretable embedding–probing framework to melon using PlantCAD2 as a frozen DNA language model backbone. Methods: The model was not fine-tuned or further pre-trained. Instead, lightweight probes were trained on fixed embeddings derived from sequence windows surrounding genetic variants. The pipeline integrated supervised variant-effect prediction, functional perturbation profiling, training-free masked log-likelihood ratio scoring, and cis-regulatory element generation. We applied the framework to 32,268 public genotyping-by-sequencing (GBS) SNPs from a melon diversity panel. A total of 30,052 variants were successfully encoded and analyzed. Results: Of these, 31.9% were classified as high predicted functional effect under the GWAS-proximal probe. We adopted a rigorous independent split based on chromosome and locus blocks to ensure test variants and training variants occupy distinct linkage-disequilibrium blocks. In this setting, the covariance probe yields an AUROC of approximately 0.51 with a 95% confidence interval covering 0.50, indicating no predictive advantage over random guessing. The full-cohort AUROC value of 0.748 reported earlier is attributed to linkage-disequilibrium leakage and overlapping positive samples between datasets, and thus does not reflect true independent generalization. The 17-class structural annotation probe reached 95.97% per-position accuracy. Functional perturbation signals were enriched in intronic, coding, and proximal promoter regions, consistent with known regulatory architectures in melon. Using the masked language model head with Gibbs sampling, we generated 76 candidate 400 bp cis-regulatory elements associated with sugar metabolism pathways. A posterior consistency check against 18 published GWAS/QTL loci showed effect-score patterns broadly concordant with reported loci; this is a consistency analysis against known loci, not an independent prospective validation. Variants in the CmTST2 region exhibited elevated mean effect scores of 0.7922 compared with the genome-wide background of 0.406. Conclusions: Overall, this study provides a computational, hypothesis-generating framework in which a frozen plant DNA language model combined with lightweight probes prioritizes melon variants and generates candidate regulatory sequences; all outputs are unverified in-silico predictions requiring experimental validation before any functional or breeding use. Because every variant is scored independently from its local sequence context, the framework necessarily treats genetically linked, network-dependent traits as single-locus effects and does not model epistasis, pathway-level interactions, or genotype-by-environment effects; the outputs are sequence-level priors, not phenotype predictions. The results support a generalizable framework for model-guided functional genomics and precision breeding in Cucurbitaceae crops.