Plant Methods最新文献

Stomata regulate gas and water exchange in plants and are crucial for plant productivity and survival, making their trait analysis essential for advancing plant biology research. While current machine learning methods enable automated stomatal trait extraction, existing approaches face significant limitations that require extensive manual labeling for training and additional human annotation when applied to new species. This study presents an automated system for extracting stomatal traits from Pisum sativum (pea) leaves that addresses these challenges through generative artificial intelligence. Our pipeline integrates imaging, detection, segmentation, and synthetic data generation processes. A nail polish impression technique was employed to prepare leaf microscopic images, followed by the application of deep learning networks to identify and segment stomata in these images. By including generative AI-produced synthetic data, our system achieves high segmentation accuracy across species, reducing manual relabeling requirements. This approach enables seamless cross-species model adaptation for many cases, alleviating the annotation bottleneck that often limits machine learning applications in plant biology. Our results demonstrate the pipeline's effectiveness for automated stomatal trait extraction and highlight generative AI's transformative potential in advancing stomatal detection methodologies, offering a scalable solution for broad-scale comparative stomatal analysis.

Background: Variety mixtures combining crop varieties with different root system properties have the potential to improve soil exploration through belowground niche complementarity, which can improve soil resource acquisition and crop productivity. However, there is a lack of appropriate methods to distinguish and quantify roots of different varieties, which limits our ability to elucidate belowground processes that underpin soil exploration and resource uptake by plants in variety mixtures.

Results: In the present study, we developed a method to quantify root biomass and distribution patterns of different barley varieties grown together in mixtures using DNA extraction and quantitative PCR with variety-specific genetic markers. Two field experiments, one in Sweden and one in Denmark, were conducted that included two barley varieties grown either alone in pure stands or together in the same plot. The genetic markers were highly variety-specific, enabling accurate detection of the roots of each individual variety in the mixture. We found that the contribution of varieties to total root biomass in the mixture differed between the two locations, indicating the effects of the environment on root distribution patterns in variety mixtures.

Conclusions: The method presented here opens new possibilities for rapid quantification of root biomass and can provide new insights into belowground processes underpinning the functioning of mixed variety systems. Ultimately, such understanding is needed to assess the potential to adopt mixed variety systems in practical agriculture.

Field trials play an essential role in developing genetically modified and genome-edited biotechnology plants, as they assess plant growth, yield, and potential unintended effects. Australian biotechnology field trials are regulated by federal protocols that mandate accurate forecasting of flowering times. Currently, this relies on labour-intensive and subjective visual field inspections of individual wheat plants at defined growth stages (Zadoks growth stages Z37, Z39, and Z41). To enable automatic forecasting, hyperspectral and red-green-blue (RGB) images were captured in the greenhouse, and hyperspectral reflectance data were acquired in a semi-natural environment. Support Vector Machine classification achieved F1 scores (0.832) for pre-anthesis growth stage classification through the combined use and systematic comparison of three spectral transformations, including Standard Normal Variate, Hyper-hue, or Principal Component Analysis, which together outperformed reliance on any single transformation. After feature selection, F1 scores (0.752) could be achieved with only five wavelengths. Furthermore, the SNV transformation demonstrated robust performance under limited training conditions, maintaining high classification accuracy and strong generalizability across varying data sizes. These findings highlight the effectiveness of transformation-enriched data and optimized feature selection for accurate growth stage classification, providing a low-cost approach to reduce manual inspection burdens and strengthen biosafety during biotechnology field trial practices.

Efficient regeneration protocols are essential for large-scale propagation and genetic manipulation of recalcitrant medicinal species such as Cannabis sativa. Existing direct and indirect regeneration methods are highly genotype and explant-dependent, limiting broader applicability. Here, we report a five-stage (S₀-S₄) optimised protocol that is reproducible and achieves high-efficiency direct de novo regeneration using cotyledonary node explants from both hemp and medicinal cannabis genotypes. A 1% (v/v) H₂O₂-based sterilisation method significantly improved seed germination and reduced endophyte contamination. Among embryo-derived explants, the cotyledonary node attached to the cotyledon showed superior regeneration efficiency through two distinct pathways: axillary shoot initiation and de novo regeneration, the latter achieving ~ 70-90% efficiency in six hemp cultivars and three medicinal cannabis lines on TDZ and NAA containing shoot regeneration medium. Histological analysis confirmed true de novo shoot formation from peripheral cortical cells, independent of pre-existing meristems or callus. De novo shoots were initiated within 2 d of shoot regeneration medium treatment, indicating rapid cellular commitment to organogenesis, with optimal regeneration between 7 and 14 d. Prolonged exposure proved detrimental, causing excessive callusing and vitrification. Repeated subculturing during proliferation stage enabled scalable shoot multiplication, yielding an average of 7 shoots per responding explant (~ 11.4 shoots per seed), outperforming previously published cotyledon-based (~ 2-fold) and hypocotyl-based (~ 5-fold) methods under comparable conditions. Regenerated plantlets developed healthy roots (with IAA or IBA) and acclimatised readily, exhibiting normal vegetative and reproductive growth. The protocol's reproducibility across diverse cannabis genotypes and its applicability to other medicinal angiosperm species in this study highlights its value for both research and commercial applications.

Background: Seed quality analysis using X-rays is increasingly explored due to its non-invasive and rapid nature. Yet, the current absence of reliable and standardised imaging protocols has led to contradictory effects of X-ray exposure in previous studies. Our work systematically investigated the effect of low-energy X-rays (peak energy ≲25 keV) with limited doses (< 3 mGy) on a wide range of plant materials.

Results: The baseline of three germination categories was established across seven species before the application of low-dose X-ray exposure under controlled standard germination conditions. The high inter-varietal and inter-lot variabilities, in addition to the strong interaction between X-ray exposure with both variety and lot, reinforced the need to consider genetic and seed quality aspects while evaluating the impacts of low-dose, low-energy X-rays (< 3 mGy, peak energy ≲25 keV). A slight stimulative effect was observed on most of the species (bean, carrot, fennel, maize, radish, and ryegrass), notably, with a repeated reduction in ungerminated seeds led to an increase in normal germination (1.7 ± 1.9%). Intrinsic physical quality holds a crucial value where the minor negative impact observed in soybean originated from its degraded physical quality and not from X-ray exposure; hence, no destructive effects were detected. To understand whether seed size plays a significant role in a seed's response to exposure, linear regression models were built to predict 3D seed traits (volume) from 2D X-ray images. Yet, seed size did not explain the variation in responses to low doses of X-rays. However, the average density of the seven species explained both their natural germination (p < 0.01; R² = 0.82) and their germination outcomes after exposure (p < 0.01; R² = 0.88). Among all species, fennel with notably low density (0.7 g/cm³) demonstrated the most pronounced gains in germination after exposure (4.6 ± 6.3%) due to the stimulative effect.

Conclusion: Low-dose X-ray exposure is non-destructive with a beneficial effect on germination, but can be strongly influenced by underlying genetics and the physical quality of the tested seeds. This work addressed important gaps in evaluating X-ray impacts and proposed a robust design and well-examined radiography protocol for a proven non-destructive seed quality analysis.

Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.

Cotton diseases and pests pose significant threats to cotton production, necessitating accurate and efficient classification methods. Despite existing advanced methods, there is a research gap in utilizing both local feature extraction and global context capture for enhanced classification accuracy. Hence, this study developed and evaluated three advanced models for cotton disease and pest classification: a convolutional neural network (CNN)-based model, a Vision Transformer (ViT)-based model, and a hybrid CNN-ViT model. These models were trained on a dataset comprising eight classes of cotton diseases and pests, namely aphids, armyworm, bacterial blight, cotton boll rot, green cotton boll, healthy, powdery mildew, and target spot. The results demonstrated that the hybrid CNN-ViT model achieved the highest overall performance with an average test accuracy of 98.5%. The CNN model showed strong performance with an average accuracy of 97.9%. The ViT models, while having self-attention mechanisms to capture context and dependencies, exhibited improved performance with increased depth. The ViT model having four transformer layers outperformed the two-layer variant, achieving an average accuracy of 97.2% compared to 96.3%. The hybrid model effectively combined the strengths of CNN's local feature extraction and ViT's global feature capture, resulting in superior classification accuracy across most classes. Future research should focus on expanding the dataset to include more diverse diseases and pests and integrating the models with autonomous platforms for spraying the chemicals, thus facilitating real-world adoption and application in agricultural settings.

The application of Fourier transform infrared (FTIR) spectroscopy for non-structural carbohydrates (NSC) prediction as a tool for pre-breeding screening has immense potential but remains to be unexplored, because of technical challenges associated with these measurements. This study investigated the potential of employing FTIR spectroscopy as a high-throughput tool for forecasting NSC content, including total soluble sugar (TSS) and starch content, of 30 rice accessions from the Rice Diversity Panel 1 (RDP1) germplasm and RiceTec hybrids grown in 2019 (320 genotypes) and 2020 cropping (312 genotypes). Partial Least Squares (PLS) regression analysis was used to construct predictive models to estimate NSC content in flag leaves and stem of rice exposed to elevated and ambient nighttime air temperature during the flowering stage of rice. The TSS model exhibited a coefficient of determination (R²) value of 0.63 and root mean square error of prediction (RMSEP) values of 3.62 mg g^- 1. Notably, the NSC model demonstrated a superior metric performance, with R² = 0.66 and RMSEP of 5.58 mg g^- 1. The predictive model created in this research effectively measured the NSC composition present in the flag leaves of rice. Expanding the sample size and incorporating additional principal components may enhance the model's predictive accuracy. The FTIR technique can produce fast accurate results and resolve the high analytical costs. Overall, the use of FTIR in conjunction with PLS regression analysis provides a potential tool to advance our understanding of various rice genotypes, particularly concerning their ability to withstand abiotic stress such as HNT.

Tomato leaf diseases pose a significant threat to global food security, necessitating accurate and efficient detection methods. This paper introduces the Tomato Leaf Disease Visual Language Model (TLDVLM), a novel approach based on the BLIP-2 architecture enhanced with Low-Rank Adaptation (LoRA), for precise classification of 10 distinct tomato leaf diseases. Our methodology integrates a sophisticated image preprocessing pipeline, utilizing GroundingDINO for robust leaf detection and SAM-2 for pixel-level segmentation, ensuring that the model focuses solely on relevant plant tissue. The TLDVLM leverages the powerful multimodal understanding of BLIP-2, with LoRA applied to its Q-Former module, enabling parameter-efficient fine-tuning without compromising performance. Comparative experiments demonstrate that the TLDVLM significantly outperforms baseline models, including CLIP-LoRA and ConvNeXT-tiny, achieving an accuracy of 97.27%, a precision of 0.9587, a recall of 0.9789, and an F1-score of 0.9681. Beyond classification, the finetuned TLDVLM checkpoints are integrated into a practical application for new image inference. This application displays the raw and segmented images, the predicted disease, and offers functionalities to fetch comprehensive information on disease causes and remedies using external APIs (e.g., OpenAI), with an option to download a PDF summary for offline access on a portable device. This research highlights the potential of LoRA-adapted Vision-Language Models in developing highly accurate, efficient, and user-friendly agricultural diagnostic tools.

Plant diseases cause approximately 220 billion USD in annual agricultural losses, driving demand for automated detection systems. This systematic review analyzes deep learning approaches for plant disease detection using RGB and hyperspectral imaging, examining their evolution from classical image processing to modern neural architectures. We evaluate state-of-the-art models across 11 benchmark datasets, revealing significant performance gaps between laboratory conditions (95-99% accuracy) and field deployment (70-85% accuracy). Transformer-based architectures demonstrate superior robustness, with SWIN achieving 88% accuracy on real-world datasets compared to 53% for traditional CNNs. Our analysis identifies three critical deployment constraints: environmental variability sensitivity, economic barriers (500-2000 USD for RGB vs. 20,000-50,000 USD for hyperspectral systems), and interpretability requirements for farmer adoption. Case studies of successful platforms (Plantix with 10+ million users) highlight the importance of offline functionality and multilingual support. We establish evidence-based guidelines prioritizing deployment viability over laboratory optimization and identify key research directions including lightweight model design, cross-geographic generalization, and explainable multimodal fusion. This review provides a comprehensive framework for advancing plant disease detection from research prototypes to practical agricultural tools that can improve global food security.