Background: Oilseed rape is an excellent candidate for phytoremediation of cadmium (Cd) contaminated soils given its advantages of high biomass, fast growth, moderate metal accumulation, ease of harvesting, and metal tolerance, but the cadmium response pathways in this species (Brassica napus) have yet to be fully elucidated. A combined analysis of miRNA and mRNA expression to infer Cd-induced regulation has not been reported inB. napus.Results: We characterized concurrent changes in miRNA and mRNA profiles in the roots and shoots ofB. napusseedlings after 10 days of 10 mg/L Cd²⁺treatment. Cd treatment significantly affected the expression of 22 miRNAs belonging to 11 families in the root and 29 miRNAs belonging to 14 miRNA families in the shoot. Five miRNA families (MIR395, MIR397, MIR398, MIR408 and MIR858) and three novel miRNAs were differentially expressed in both tissues. A total of 399 differentially expressed genes (DEGs) in the root and 389 DEGs in the shoot were identified, with very little overlap between tissue types. Eight anti-regulation miRNA-mRNA interaction pairs in the root and eight in the shoot were identified in response to Cd and were involved in key plant stress response pathways: for example, four genes targeted by miR398 were involved in a pathway for detoxification of superoxide radicals. Cd stress significantly impacted the photosynthetic pathway. Transcription factor activation, antioxidant response pathways and secondary metabolic processes such as glutathione (GSH) and phenylpropanoid metabolism were identified as major components for Cd-induced response in both roots and shoots.Conclusions: Combined miRNA and mRNA profiling revealed miRNAs, genes and pathways involved in Cd response which are potentially critical for adaptation to Cd stress inB. napus. Close crosstalk between several Cd-induced miRNAs and mRNAs was identified, shedding light on possible mechanisms for response to Cd stress in underground and aboveground tissues inB. napus. The pathways, genes, and miRNAs identified here will be valuable targets for future improvement of cadmium tolerance inB. napus.

Although the seed oil content in canola is a crucial quality determining trait, the regulatory mechanisms of its formation are not fully discovered. This study compared the silique and seed physiological characteristics including fresh and dry weight, seed oil content, chlorophyll content, and carbohydrate content in a high oil content line (HOCL) and a low oil content line (LOCL) of canola derived from a recombinant inbred line in 2010, 2011, and 2012. The aim of the investigation is to uncover the physiological regulation of silique and seed developmental events on seed oil content in canola. On average, 83% and 86% of silique matter while 69% and 63% of seed matter was produced before 30 days after anthesis (DAA) in HOCL and LOCL, respectively, over three years. Furthermore, HOCL exhibited significantly higher fresh and dry matter at most developmental stages of siliques and seeds. From 20 DAA, lipids were deposited in the seed of HOCL significantly faster than that of LOCL, which was validated by transmission electron microscopy, showing that HOCL accumulates considerable more oil bodies in the seed cells. Markedly higher silique chlorophyll content was observed in HOCL consistently over the three consecutive years, implying a higher potential of photosynthetic capacity in siliques of HOCL. As a consequence, HOCL exhibited significantly higher content of fructose, glucose, sucrose, and starch mainly at 20 to 45 DAA, a key stage of seed lipid deposition. Moreover, seed sugar content was usually higher than silique indicating the importance of sugar transportation from siliques to seeds as substrate for lipid biosynthesis. The much lower silique cellulose content in HOCL was beneficial for lipid synthesis rather than consuming excessive carbohydrate for cell wall. Superior physiological characteristics of siliques in HOCL showed advantage to produce more photosynthetic assimilates, which were highly correlated to seed oil contents.

Delayed planting date of rapeseed is an important factor affecting seed yield. However, regulation of the leaf carbohydrate metabolism in rapeseed by a late planting date at the reproductive stage is scarcely investigated. A two-year field experiment was conducted to assess the effect of planting dates, including early (15 September), optimal (1 October), late (15 October), and very late (30 October), on leaf growth and carbohydrate biosynthetic and catabolic metabolism at the reproductive stage. The results showed that leaf dry matter decreased linearly on average from 7.48 to 0.62 g plant⁻¹ with an early planting date, whereas it increased at first and peaked at 14 days after anthesis (DAA) with other planting dates. Leaf dry matter was the lowest at the very late planting date during the reproductive stage. For leaf chlorophyll content, rapeseed planted at an optimal date maximized at 14 DAA with an average content of 1.51 mg g⁻¹ fresh weight, whereas it kept high and stable at a very late planting date after 28 DAA. For the carbohydrate catabolic system, acid and neutral invertase (AI and NI, respectively) showed higher activity before 14 DAA, whereas both sucrose synthase (SS) and starch phosphorylase (SP) showed higher activity after 14 DAA. For the carbohydrate biosynthetic system, the activity of sucrose phosphate synthase (SPS) was the highest at the late planting date after 14 DAA, whereas it was at the lowest at the very late planting date. However, the activity of ADP-glucose pyrophosphorylase (AGPase) at the late and very late planting dates was significantly higher than that of the early and optimal plant dates after 21 DAA, which is in accordance with the leaf total soluble sugar content, suggesting that leaf carbohydrate metabolism is governed by a biosynthetic system. The current study provides new insights on leaf carbohydrate metabolism regulation by late planting in rapeseed at the reproductive stage.

Early-stage, accurate and high-throughput phenotyping‌ through leaf area estimation is ‌critical‌ for future rapeseed breeding, but faces

Early-stage, accurate and high-throughput phenotyping‌ through leaf area estimation is ‌critical‌ for future rapeseed breeding, but faces ‌two key constraints‌: expensive data annotation and persistent challenge of leaf occlusion. To address these issues, we present a ‌data-efficient‌ deep learning framework using smartphone-captured top-down RGB images for rapeseed leaf area quantification. Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated corpus of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning method. This pre-trained model is then fine-tuned on a custom rapeseed dataset using a novel Canopy-Mix data augmentation technique to handle fragmented views analogous to occlusion, and a hybrid loss function combining Smooth L1 and Log-Cosh for robust convergence. Through rigorous 5-fold cross-validation, our proposed model achieved state-of-the-art predictive performance (Coefficient of Determination, R$^2$=0.805). What’s more, the predicted leaf area demonstrated a remarkably strong correlation with both fresh weight (r=0.900) and dry weight (r=0.885). The model significantly outperformed a range of baselines, including models trained from scratch, those pre-trained on ImageNet, and a heuristic method based on manually annotated bounding boxes. Ablation studies confirmed the essential contribution of each component, while qualitative analysis of attention maps demonstrated the model's ability to precisely localize the leaf canopy and ignore background distractors. This study demonstrates that domain-specific self-supervised pre-training offers a powerful solution to overcome data limitations in agricultural vision, providing a robust and scalable tool for non-destructive phenotyping that can potentially accelerate the rapeseed breeding cycle.

Accurate, high-throughput estimation of Above-Ground Biomass (AGB), a key predictor of yield, is a critical goal in rapeseed breeding. However, this is constrained by two key challenges: (1) traditional measurement is destructive and laborious, and (2) modern deep learning approaches require vast, costly labeled datasets. To address these issues, we present a data-efficient deep learning framework using smartphone-captured top-down RGB images for AGB estimation (Fresh Weight, FW, and Dry Weight, DW). Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning (SSL) method. Subsequently, this pre-trained model is fine-tuned on a small, custom-labeled rapeseed dataset (N = 833) using a Multi-Task Learning (MTL) framework to simultaneously regress both FW and DW. This MTL approach acts as a powerful regularizer, forcing the model to learn robust features related to the 3D plant structure and density. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance for both Fresh Weight (Coefficient of Determination, R² = 0.842) and Dry Weight (R² = 0.829). The model significantly outperformed a range of baselines, including models trained from scratch and those pre-trained on the generic ImageNet dataset. Ablation studies confirmed the critical and synergistic contributions of both domain-specific SSL (vs. ImageNet) and the MTL framework (vs. single-task training). This study demonstrates that an SSL+MTL framework can effectively learn to infer complex 3D plant attributes from 2D images, providing a robust and scalable tool for non-destructive phenotyping to accelerate the rapeseed breeding cycle.