Plant Methods最新文献

Bakanae is a fungal rice disease that is threatening global rice production, causing severe yield losses. The plant microbiome plays a significant role in plant stress resistance, but its high-dimensional characteristics have not been fully exploited. Therefore, we integrated the microbiome and machine learning (ML) to diagnose bakanae disease in this study. We found significant correlations between Gammaproteobacteria and Bacteroidia and the severity of bakanae disease. We constructed different diagnosis models based on random forests (RF), support vector machines (SVM), and convolutional neural networks (CNN) on 88 biological replicates with an independent test set. We found that the RF model demonstrated strong performance across four taxonomic levels, with an accuracy of 88.9% and an F1 score of 94.1%. Notably, a Bray-Curtis dissimilarity-based extraction method was proposed to rapidly screen practical information from the original microbial community, which can enhance the model performance to a certain extent. According to phenotypic data, the disease severity of infected samples was classified into two levels (high and low infected levels) using the K-means clustering method. In the diagnosis of infection severity based on the family level, the model's prediction accuracy reached 77.8%. Collectively, these findings highlight that the combination of microbiome with ML can advance diagnostic strategies for bakanae disease, providing new avenues for precision agriculture.

Genomic selection (GS) in plant breeding aims to identify individuals with superior genetic merit while maintaining genetic diversity within populations. In plant breeding, considering multiple traits simultaneously makes optimizing selection complex, especially under genetic relatedness constraints. In this study, we propose a binary quadratic programming framework for constructing a multi-trait selection index that maximizes genetic gain while minimizing average pairwise relatedness appropriate for identifying superior candidates for advancement in the breeding pipeline. The approach combines estimated breeding values (EBVs) across multiple traits by applying trait-specific economic weights, while simultaneously accounting for coancestry through the genomic relationship matrix. By formulating the selection problem as a constrained Quadratic Programing Multi-trait Selection Index (QPMSI), our method enables the identification of a fixed number of candidate individuals that jointly optimize selection index values and control genetic relatedness. We evaluated the performance of the proposed method using five real genomic datasets and demonstrated that it provides a more effective balance between selection response and control of genetic relatedness than the Linear Programming Multi-trait Selection Index (LPMSI). In particular, the QPMSI consistently outperformed the LPMSI in terms of the MV metric (gain-to-degree of relatedness ratio), achieving improvements of at least 53.8%. This framework offers a practical and computationally efficient tool for sustainable breeding strategies in multi-trait selection contexts.

Timely and precise insect pest detection is critical in areas with high agricultural intensity and climates that favor continuous pest activity. Traditional pest identification methods, such as manual inspection or expert guided analysis, are labor intensive and time consuming. These approaches lack scalability and hinder timely intervention, particularly in resource-constrained settings. Furthermore, the high visual similarity between pest species and intra-species variability across developmental stages further challenge detection efforts in real-world agricultural conditions. To address these limitations, we propose Attention-PestNet, a novel one-stage object detection network designed for insect pest detection. Our method consist of two key attention-based modules to enhance feature extraction and improve detection performance. First, the Hierarchical Scaled Dot-Product Attention module leverages a multi-level attention mechanism to capture salient pest features at different scales. Second, the Multi-Scale Spatial Attention module refines spatial feature representations by incorporating horizontal and vertical attention pathways with multi-scale max-pooling operation to enhance contextual understanding. Extensive experiments were conducted on two public benchmarks, IP102 and R2000 datasets, which represent agricultural conditions in Asia. The results demonstrate that Attention-PestNet outperforms state-of-the-art models in both visualization outputs and quantitative metrics. Attention-PestNet shows strong potential as a scalable and cost-effective solution for intelligent pest monitoring in modern precision agriculture. Our code and data for this paper are made available at: https://github.com/thinhdoanvu/HSDPA .

Peanut leaf diseases have a major impact on peanut yield and quality. Timely, rapid, and accurate early diagnosis and control of peanut leaf diseases are key to ensuring high quality and yield of peanuts. This work focuses on the early diagnosis of peanut diseases and pests and conducts systematic research on the hardware system for imaging and spectral sensing of peanut plant leaves, as well as the software for deep learning classification algorithms. First, we designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases, including scab, scorch spot, and anthracnose. Second, we constructed a convolutional neural network to extract the basic features of spectral images. Third, an adaptive channel attention mechanism is introduced to update the weights of different channels. Fourth, a sparse second-order attention mechanism driving network is constructed to enhance the discriminative ability of deep feature information. Finally, the classification is completed utilizing the Softmax classifier. The experimental results demonstrate that the spectral image information improves the robustness of deep learning models to data transformation and achieves a high-precision classification score of 98.45% for asymptomatic peanut leaf diseases. Compared to traditional optical devices and software algorithms, the proposed multispectral imaging system and deep learning algorithm significantly improve detection ability and classification accuracy, which can assist botanists in making more accurate diagnoses of peanut leaf diseases.

Background: Plant extracellular vesicles (EVs), hold significant therapeutic potential due to their roles in intercellular communication and cross-kingdom regulation, primarily mediated by their microRNA (miRNA) cargo. However, isolating high-purity plant EVs from complex plant tissues, such as the tuberous roots of Ophiopogon japonicus, is challenging due to the dense cell wall matrix and high content of contaminants like polysaccharides. Existing isolation methods, including differential ultracentrifugation (DUC) and density gradient ultracentrifugation (DGUC), involve trade-offs between yield, purity, and vesicle integrity, necessitating the development of optimized protocols.

Results: We developed and systematically optimized an integrated protocol for isolating high-purity EVs from O. japonicus roots. Key optimizations included: (1) refining the DUC protocol by incorporating a double ultracentrifugation step; (2) implementing a modified DGUC approach with a pre-clearing step for superior debris removal; and (3) evaluating enzymatic pre-treatment with cellulase and pectinase to enhance EVs release. Comparative analysis demonstrated that the optimized method, particularly utilizing enzymatic pre-processing and double ultracentrifugation, significantly improved plant EVs yield and purity. Small RNA (sRNA) sequencing of the resulting high-purity EVs successfully characterized their functional miRNA cargo profile, validating the efficacy of the isolation strategy.

Conclusions: This study establishes a robust and adaptable pipeline for isolating high-quality, functionally intact plant EVs from challenging plant root tissues. The optimized protocol effectively addresses the critical methodological challenges of yield and purity, enabling reliable downstream functional characterization and advancing therapeutic investigations of plant-derived EVs.

Background: Extraction of plant genomic DNA is a critical step for PCR-based genotyping, mapping, and breeding applications. Conventional CTAB protocols and commercial kits provide reliable DNA but are labour-intensive, costly, and generate substantial plastic waste. Simplified crude-extract methods are available, yet their performance is often compromised by PCR inhibition from salts and cellular debris. A rapid, low-cost, and high-throughput method is therefore needed for routine molecular applications.

Results: We developed a single-tube DNA extraction protocol that eliminates supernatant transfers, thereby reducing handling errors, plastic consumption, and processing time. The method consistently produces DNA of sufficient yield and purity for PCR-based assays. Validation in wheat and wheat-wild relative introgression lines demonstrated robust amplification in KASP assays. Cross-species testing in maize, Arabidopsis, and tomato using two Tris-salt extraction buffers confirmed broad applicability, supported by NanoDrop and Qubit measurements. Freeze-dried and frozen tissue produced higher yields than fresh samples, confirming their suitability for high-throughput and large-scale studies.

Conclusions: This streamlined protocol provides a cost-effective, reliable, and scalable approach for extracting plant genomic DNA suitable for PCR-based genotyping, marker development, and diversity analysis. Its simplicity and throughput make it particularly valuable for breeding programmes, although it is not intended for applications requiring highly pure DNA, such as whole-genome resequencing.

Background: Protoplasts, which are plant cells devoid of cell walls, are valuable tools in plant biotechnology. However, they are highly sensitive to mechanical and osmotic stress during isolation and early culture, often leading to significant loss of viability. Reliable and efficient methods for monitoring protoplast quality are essential for downstream applications.

Results: We applied impedance flow cytometry to assess the viability, cell size, and early division of freshly isolated protoplasts from Arabidopsis thaliana, Brassica napus, and Beta vulgaris. This label-free technique enables fast, objective, and high-throughput assessment of individual protoplasts, allowing reliable monitoring of viability and early division in large populations. Importantly, IFC-derived viability metrics strongly correlated with microcallus formation, demonstrating their predictive value for culture competence.

Conclusions: Impedance flow cytometry provides a robust, efficient and reproducible method for characterizing protoplast cultures. It enables rapid assessment of viability and growth potential, supporting quality control and optimization in plant cell culture workflows.

Background: Abiotic stresses are detrimental factors for germination, organ development, and other growth activities in maize, which could reduce yield and quality. The analysis of leaf movement is a simple and efficient method to identify stresses as early as possible. This study developed a wireless leaf movement sensor system (WLMS) using a digital inertial measurement unit (IMU) to measure maize leaf movement in real-time and detect abiotic stresses quickly.

Results: The IMU was designed as a lightweight sensor structure that the IMU was separated from the MCU (microcontroller unit) and connected via flexible cables. This lightweight sensor attached to maize leaves easily and measured leaf movement in real-time with high resolution (measured error of ± 0.25°). The IMU collected leaf movement data and transmitted the data wirelessly to the data receiving terminal (host computer). Meanwhile, the data receiving terminal performed linear fitting on the daily leaf movement data to extract the movement characteristics of maize leaves. The WLMS detected abiotic stress in maize based on the leaf movement characteristics under different stress conditions. The results indicated that the WLMS could detect whether maize was under stress within one day of being stressed and identify the specific type of stress within the following 5-7 days, providing a lead time of 2 days compared to other non-destructive methods (including RGB imaging, hyperspectral analysis, and chlorophyll meters).

Conclusions: This sensor system enables the rapid and early detection and identification of abiotic stresses in maize as a low-cost tool for plant phenotype measurement and plant movement measurement.

Background: With the intensification of global climate change, extreme weather events have become increasingly frequent, severely impacting the growth cycles and yield stability of crops. Against this backdrop, cultivating new crop varieties with high stress resistance has become a core task for achieving sustainable agriculture and ensuring food security. Root length, as a critical phenotypic trait that reflects a plant's ability to absorb water and nutrients, is closely related to the crop's capacity to withstand adversities, such as drought, high temperatures and salinisation. However, root length measurement technology remains a significant bottleneck in plant science research. Traditional manual methods are inefficient and prone to human-induced variability (e.g. subjective standard discrepancies, operational errors, and potential contamination or damage to seeds). Meanwhile, existing automated measurement models face challenges in large-scale practical applications due to their high deployment costs.

Results: This study developed a seed germination image acquisition system and constructed a pea root dataset. Based on the YOLOv8-Seg-n instance segmentation model, a lightweight automatic root measurement (ARM) model was then developed using feature distillation, structured pruning techniques, and a series of post-processing procedures for root length calculation. Experimental results demonstrated that the ARM model had only 1.81 M parameters, with 8.3 GFLOPs and a weight file size of 4.2 MB, and achieved 70.4 FPS. It realised outstanding performance with mAP@0.5 and AP_root scores of 90.3% and 81.2%, respectively, showing a high consistency with manual measurement results (R² = 0.993). Compared to existing models, the ARM model significantly reduces parameter scale and computational complexity, making it more accommodating to device performance and computational requirements while also decreasing the workload associated with root sample processing. Furthermore, the application of the ARM model in a 72-hour full time-series analysis of pea root length under drought conditions validated its potential for practical use in real-world scenarios.

Conclusions: The ARM model offers an efficient and cost-effective technological solution for high-throughput root length measurement in peas. It achieves a favorable balance between accuracy, speed, and computational resource requirements, demonstrating broad application potential in agricultural production and breeding research. The model offers critical technical support for ensuring food security and enhancing crop stress resistance.

Background: In modern plant breeding, anther culture is an important biotechnological tool that shortens the breeding cycle and improves efficiency, thereby playing a crucial role in genetic improvement and cultivar development. To date, there have been few studies on anther culture in daylily (Hemerocallis spp.). The present study aimed to investigate the effects of the microspore developmental stage, low-temperature pretreatment duration, and phytohormone combinations on callus induction and plant regeneration from daylily anthers.

Results: We first studied the morphological characteristics of flower buds and anthers at different microspore developmental stages, and then used anthers from different developmental stages for callus induction. The results showed that microspores at the late uninucleate stage were optimal for callus induction. Low-temperature pretreatment at 4°C for 24 h could effectively promote the formation of callus tissue in daylily anthers. In the (L9(3⁴)) orthogonal array experiment, the callus induction rate was highest (45.57%) in the medium containing MS (Murashige and Skoog) + 70 g/L sucrose + 2 mg/L Kn (Kinetin) + 2 mg/L 2,4-D (2,4-dichlorophenoxyacetic acid) + 0.1 mg/L NAA (1-naphthaleneacetic acid). Among the nine media for callus bud differentiation, the highest adventitious bud induction rate was achieved with MS + 30 g/L sucrose + 2 mg/L 6-BA (N6-benzyladenine) + 0.1 mg/L NAA (43.33%). The optimal rooting medium was MS + 30 g/L sucrose + 0.05 mg/L NAA + 0.1 mg/L IBA (Indole- 3-butyric acid) (93.33%). Flow cytometry and Simple sequence repeats (SSR) analysis showed that all 55 intact plantlets derived from anthers were diploid.

Conclusion: This study optimized the anther culture technique for daylily and proposed a comprehensive anther culture method for callus induction and plant regeneration. For the first time, plant regeneration was achieved via anther culture in daylily, providing relevant theoretical and technical support for genetic research and daylily breeding.