Plant Methods最新文献

Rice diseases pose a severe threat to global food security, while traditional detection methods suffer from low efficiency and dependence on manual expertise. To address the challenges of insufficient feature extraction and poor multi-scale disease adaptability in existing deep learning approaches under complex field environments, this study proposes ADAM-DETR, a rice disease detection algorithm based on improved RT-DETR. We constructed the RiDDET-5 dataset containing 9,303 images covering five major disease categories. The algorithm innovatively designs three core modules: the AdaptiveVision Network (AVN) backbone for enhanced feature extraction, the Dual-Domain Enhanced Transformer (DDET) module for spatiotemporal-frequency domain collaboration, and the Adaptive Multi-scale Feature Model (AMFM) for improved feature fusion. Experimental results demonstrate that ADAM-DETR achieves 94.76% mAP@50 on the RiDDET-5 dataset, representing a 3.25% improvement over the baseline, and 83.32% mAP@50 on the public Kamatis dataset with a 2.19% enhancement, validating its cross-domain generalization capability. The algorithm requires only 42.8G FLOPs with 14.3M parameters, achieving an optimal balance between accuracy and efficiency, providing an effective technical solution for disease monitoring in smart agriculture.

Background: Genotype-by-sequencing (GBS) is a cost-effective method for large-scale genotyping, widely used across various species, particularly those with large genomes. A critical aspect of GBS lies in the selection of restriction enzymes for genome digestion and the optimization of data analysis pipelines. However, few studies have comprehensively examined the combined effects of enzyme choice and pipeline configuration.

Results: In this study, we created GBS libraries using three commonly used restriction enzyme combinations (HindIII-NlaIII, PstI-MspI, and ApeKI) and assessed multiple SNP-calling pipelines in 15 soybean varieties. We tested four aligners (BWA-MEM, Bowtie2, BBMap, and Strobealign) and seven SNP callers (Bcftools, Stacks, DeepVariant, FreeBayes, VarScan, BBCallVariants, and GATK). Our finding reveal that enzyme choice significantly influences the number of identified SNP, gene localization preferences, and accuracy. Furthermore, the performance of SNP callers varied markedly in terms of SNP count, precision, recall, and false discovery rate (FDR). DeepVariant exhibited the highest accuracy, with 76.0% of its SNPs intersecting with whole-genome sequencing (WGS)-derived SNPs and an FDR of 0.0095, compared to FreeBayes, which had 47.8% intersection and an FDR of 0.6321.

Conclusions: Our findings underscore the importance of optimizing both enzyme selection for sequencing libraries and data analysis pipelines to ensure robust and reproducible results. This study provides a general framework for designing large-scale genotyping experiments aimed to specific quality and quantity requirements in various plant species.

Deep learning demonstrates strong generalisation capabilities, driving substantial progress in plant disease recognition systems. However, current methods are predominantly optimised for offline implementation. Real-time crop surveillance systems encounter streaming images containing novel disease classes in few-shot conditions, demanding incrementally adaptive models. This capability is called few-shot class-incremental learning (FSCIL). Here, we introduce VCPV-virtual contrastive constraints with prototype vector calibration-enabling sustainable plant disease classification under FSClL conditions. Specifically, our method consists of two phases: the base class training phase and the incremental training phase. During the base class training phase, the virtual contrastive class constraints (VCC) module is utilised to enhance learning from base classes and allocate sufficient embedding space for new plant disease images. In the incremental training phase, the prototype calibration embedding (PCE) module is introduced to distinguish newly arriving plant disease categories from previous ones, thereby optimising the prototype space and enhancing the recognition accuracy of new categories. We evaluated our approach on the PlantVillage dataset, and the experimental results under both 5-way 5-shot and 3-way 5-shot settings demonstrate that our method achieves state-of-the-art accuracy. At the same time, we achieved promising performance on the publicly available CIFAR-100 dataset. Furthermore, the visualisation results validate that our strategy effectively supports fine-grained, sustainable disease recognition, highlighting the potential of our approach to advance FSCIL in the field of plant disease monitoring.

Herbs have historically been central to medicinal practices, representing one of the earliest forms of therapeutic intervention. While synthetic drugs are often highly effective in treating acute conditions, their use is frequently accompanied by adverse side effects. In addition, the growing dependence on synthetic pharmaceuticals has raised concerns regarding affordability, thereby fostering a renewed interest in herbal medicine as a cost-effective and holistic alternative. In response to this need, the current study introduces a computer vision framework for accurate herb identification. A novel dataset, Herbify, was compiled from two different herb datasets and refined through rigorous cleaning, preprocessing, and quality control procedures. The resulting dataset underwent standardization via the Preprocessing Algorithm for Herb Detection (PAHD), producing a refined dataset of 6104 images, representing 91 distinct herb species, with an average of about 67 images per species. Utilizing transfer learning, the research harnessed pre-trained Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), then integrated these models into an ensemble framework that leverages the unique strengths of each architecture. Experimental results indicate that EfficientNet v2-Large achieved a noteworthy F₁-score of 99.13%, while the ensemble of EfficientNet v2-Large and ViT-Large/16, termed EfficientL-ViTL, attained an even higher F₁-score of 99.56%. Additionally, the research also introduces 'Herbify' application, an AI-driven framework designed to identify herbs using the developed model. By directly tackling the principal obstacles in herb identification, the proposed system achieves a highly accurate and operationally viable classification mechanism. The experimental outcomes showcase top-tier performance in herb identification and emphasize the transformative potential of AI-based solutions in supporting botanical applications.

Background: Mitochondria are central to plant growth, development, and stress resilience. Despite their importance, mitochondrial research in desiccation-tolerant mosses remains underexplored. To unravel the stress resistance mechanisms of the extremotolerant desert moss, establishing a method to isolate highly active and pure mitochondria is critical. This study pioneered the use of low-temperature immersion combined with differential centrifugation and discontinuous percoll density gradient centrifugation to isolate mitochondria from Syntrichia caninervis, a model desiccation-tolerant moss. The purity, structural integrity, and functional activity of the isolated mitochondria were systematically evaluated using western blot analysis, Janus Green B staining, JC-1 membrane potential assays, and electron transport chain (ETC) complex activity measurements.

Results: From 50 g of S. caninervis tissue, approximately 56.7 mg of mitochondria were isolated with high purity, effectively removing non-mitochondrial contaminants (e.g., chloroplasts and cytoplasmic debris). Functional assays and membrane potential analysis confirmed no significant damage to mitochondrial activity or structural integrity during the purification process. Notably, room temperature storage (25 °C) induced rapid functional decay, whereas cryogenic storage at - 20 °C maintained ≥ 70% mitochondrial viability over 10 days, sufficient for downstream applications including proteomic profiling and bioenergetic studies.

Conclusion: The optimized mitochondrial isolation protocol presented here is both time efficient and highly reproducible, yielding mitochondria of exceptional purity suitable for mechanistic studies in desiccation-tolerant mosses. The isolated mitochondria exhibit robust functional activity and structural integrity, providing a reliable platform for investigating stress resistance mechanisms in S. caninervis and other extremophytic species. By establishing a standardized workflow for mitochondrial isolation in desiccation-tolerant plants, this method addresses a critical technical gap and paves the way for advanced investigations into mitochondrial biology under extreme environmental conditions.

Apple Marssonina blotch (AMB) is a major disease causing pre-mature defoliation. The occurrence of AMB will lead to serious production decline and economic losses. The precise identification of AMB outbreaks and the measurement of its severity are essential for limiting the spread of the disease, yet this issue remains unaddressed to this day. Given these, we conducted experiments in Qian County, Shaanxi, China, to develop an Apple Marssonina Blotch Index (AMBI) based on hyperspectral imaging, aimed to quantify disease severity at the leaf scale and to monitor infection at the canopy scale. Based on the separability and combination of individual band, characteristic wavelengths were identified in green band, red edge band and near-infrared band to construct AMBI = (R_762nm $-$ R_534nm)/(R_534nm $+$ R_690nm). The results demonstrated that AMBI exhibited high overall accuracies (R² = 0.89, RMSE = 9.67%) in estimating the disease ratio at the leaf scale compared to commonly used indices. At the canopy scale, AMBI enabled effective classification of healthy and diseased trees, yielding an overall accuracy (OA) of 89.09% and a Kappa coefficient of 0.78. Furthermore, analysis of unmanned aerial vehicle (UAV) acquired hyperspectral imagery using AMBI enabled the spatial mapping of diseased tree distribution, highlighting its potential as a scalable and timely tool for precision orchard disease surveillance.

Improving peach cultivars with superior traits is a primary objective of breeding initiatives. In this study, we aimed to elucidate the genetic basis of key agronomic and fruit-related traits using a reproducible ddRAD-seq protocol applied to a discovery panel of 90 peach accessions. Our customized workflow (available at https://github.com/najlaksouri/GWAS-Workflow ) integrates three variant callers and tests up to seven models to perform a robust genome-wide association analysis (GWAS). This approach produced 13,045 high-confidence SNPs and identified Blink as the most suitable model, effectively balancing false positive and negative associations. A total of 16 significant associations signals were unveiled for six highly heritable traits (H² > 0.5), including harvest date, fruit weight, flesh firmness, contents of flavonoids, anthocyanins and sorbitol. By assessing the allelic effect of significant markers on phenotypic attributes, nine SNP alleles were deemed favorable. Notably, a promising marker (SNC_034014.1_7012470) demonstrated simultaneous association with harvest date and fruit firmness, displaying a positive allelic effect on both traits. We anticipate that this marker can serve as a good predictor of firmer varieties. Candidate causal genes were shortlisted when fulfilling the following criteria: (i) position within the linkage disequilibrium block, (ii) functional annotation and (iii) expression pattern. A comprehensive bibliographic review of previously reported QTLs mapping nearby the associated markers allowed us to benchmark the accuracy of our approach. Despite the moderate germplasm size, ddRAD-seq allowed us to produce an accurate representation of the peach genome, resulting in SNP markers suitable for empirical association studies. Together with candidate genes, they lay the foundation for further genetic dissection of peach key traits.

Background: Xylophilus ampelinus is a plant pathogenic bacterium that causes bacterial blight in grapevines, which can lead to severe yield losses and economic damage. Owing to its fastidious growth on culture media, detection is primarily based on molecular methods. However, existing tests have produced inconsistent results, particularly when used to detect latent infections and non-validated matrices. There is a risk of false-positive results, with economic consequences such as restrictions on international trade. To enhance the diagnostics of X. ampelinus, a genome-informed approach was utilised to identify new potential targets for specific detection. On the basis of these sequences, multiple real-time PCR assays were designed, and their specificity and sensitivity were assessed, as well as their performance validated across three different grapevine tissues, including leaves, roots, and xylem.

Results: The newly designed real-time PCR assays were evaluated via high throughput testing for specificity and sensitivity and compared with a reference assay. The most promising assays were selected and validated in different grapevine tissues and included in a test performance study to validate their reproducibility and robustness. Three new assays (Xamp_BA_2, TXmp22.4, and Xamp_BA_7) demonstrated high specificity and sensitivity for X. ampelinus detection. The Xamp_BA_2 assay exhibited the best overall performance, offering high diagnostic sensitivity and robustness across diverse plant matrices. Importantly, the assays exhibited no cross-reactivity with non-target bacterial species and maintained high detection accuracy across diverse grapevine tissue types.

Conclusions: The newly developed real-time PCR assays provide an enhanced diagnostic framework for the detection of X. ampelinus in various plant matrices, significantly improving the applicability of molecular testing. The Xamp_BA_2 assay demonstrates superior performance and is recommended for routine diagnostics, with other validated assays being employed for confirmation of identification. The development of these new assays represents a significant expansion of our toolkit for the precise detection of X. ampelinus in grapevines, with the potential to contribute to the mitigation of grapevine bacterial blight, the prevention of yield losses, and the protection of international trade in grapevine material. Further implementation of these assays will support regulatory and phytosanitary efforts to mitigate the spread of X. ampelinus.