Plant Methods最新文献

Background: Ripeness is a phenotype that significantly impacts the quality of fruits, constituting a crucial factor in the cultivation and harvesting processes. Manual detection methods and experimental analysis, however, are inefficient and costly.

Results: In this study, we propose a lightweight and efficient melon ripeness detection method, MRD-YOLO, based on an improved object detection algorithm. The method combines a lightweight backbone network, MobileNetV3, a design paradigm Slim-neck, and a Coordinate Attention mechanism. Additionally, we have created a large-scale melon dataset sourced from a greenhouse based on ripeness. This dataset contains common complexities encountered in the field environment, such as occlusions, overlapping, and varying light intensities. MRD-YOLO achieves a mean Average Precision of 97.4% on this dataset, achieving accurate and reliable melon ripeness detection. Moreover, the method demands only 4.8 G FLOPs and 2.06 M parameters, representing 58.5% and 68.4% of the baseline YOLOv8n model, respectively. It comprehensively outperforms existing methods in terms of balanced accuracy and computational efficiency. Furthermore, it maintains real-time inference capability in GPU environments and demonstrates exceptional inference speed in CPU environments. The lightweight design of MRD-YOLO is anticipated to be deployed in various resource constrained mobile and edge devices, such as picking robots. Particularly noteworthy is its performance when tested on two melon datasets obtained from the Roboflow platform, achieving a mean Average Precision of 85.9%. This underscores its excellent generalization ability on untrained data.

Conclusions: This study presents an efficient method for melon ripeness detection, and the dataset utilized in this study, alongside the detection method, will provide a valuable reference for ripeness detection across various types of fruits.

Background: Stalk lodging (the premature breaking of plant stalks or stems prior to harvest) is a persistent agricultural problem that causes billions of dollars in lost yield every year. Three-point bending tests, and rind puncture tests are common biomechanical measurements utilized to investigate crops susceptibility to lodging. However, the effect of testing rate on these biomechanical measurements is not well understood. In general, biological specimens (including plant stems) are well known to exhibit viscoelastic mechanical properties, thus their mechanical response is dependent upon the rate at which they are deflected. However, there is very little information in the literature regarding the effect of testing rate (aka displacement rate) on flexural stiffness, bending strength and rind puncture measurements of plant stems.

Results: Fully mature and senesced maize stems and wheat stems were tested in three-point bending at various rates. Maize stems were also subjected to rind penetration tests at various rates. Testing rate had a small effect on flexural stiffness and bending strength calculations obtained from three-point bending tests. Rind puncture measurements exhibited strong rate dependent effects. As puncture rate increased, puncture force decreased. This was unexpected as viscoelastic materials typically show an increase in resistive force when rate is increased.

Conclusions: Testing rate influenced three-point bending test results and rind puncture measurements of fully mature and dry plant stems. In green stems these effects are expected to be even larger. When conducting biomechanical tests of plant stems it is important to utilize consistent span lengths and displacement rates within a study. Ideally samples should be tested at a rate similar to what they would experience in-vivo.

Purpose: Wood comprises different cell types, such as fibers, tracheids and vessels, defining its properties. Studying cells' shape, size, and arrangement in microscopy images is crucial for understanding wood characteristics. Typically, this involves macerating (soaking) samples in a solution to separate cells, then spreading them on slides for imaging with a microscope that covers a wide area, capturing thousands of cells. However, these cells often cluster and overlap in images, making the segmentation difficult and time-consuming using standard image-processing methods.

Results: In this work, we developed an automatic deep learning segmentation approach that utilizes the one-stage YOLOv8 model for fast and accurate segmentation and characterization of macerated fiber and vessel form aspen trees in microscopy images. The model can analyze 32,640 x 25,920 pixels images and demonstrate effective cell detection and segmentation, achieving a ${\text{mAP}}_{0.5-0.95}$ of 78 %. To assess the model's robustness, we examined fibers from a genetically modified tree line known for longer fibers. The outcomes were comparable to previous manual measurements. Additionally, we created a user-friendly web application for image analysis and provided the code for use on Google Colab.

Conclusion: By leveraging YOLOv8's advances, this work provides a deep learning solution to enable efficient quantification and analysis of wood cells suitable for practical applications.

Background: CRISPR Cas9 and Cas12a are the two most frequently used programmable nucleases reported in plant systems. There is now a wide range of component parts for both which likely have varying degrees of effectiveness and potentially applicability to different species. Our aim was to develop and optimise Cas9 and Cas12a based systems for highly efficient genome editing in the monocotyledons barley and wheat and produce a user-friendly toolbox facilitating simplex and multiplex editing in the cereal community.

Results: We identified a Zea mays codon optimised Cas9 with 13 introns in conjunction with arrayed guides driven by U6 and U3 promoters as the best performer in barley where 100% of T0 plants were simultaneously edited in all three target genes. When this system was used in wheat > 90% of T0 plants were edited in all three subgenome targets. For Cas12a, an Arabidopsis codon optimised sequence with 8 introns gave the best editing efficiency in barley when combined with a tRNA based multiguide array, resulting in 90% mutant alleles in three simultaneously targeted genes. When we applied this Cas12a system in wheat 86% & 93% of T0 plants were mutated in two genes simultaneously targeted. We show that not all introns contribute equally to enhanced mutagenesis when inserted into a Cas12a coding sequence and that there is rationale for including multiple introns. We also show that the combined effect of two features which boost Cas12a mutagenesis efficiency (D156R mutation and introns) is more than the sum of the features applied separately.

Conclusion: Based on the results of our testing, we describe and provide a GoldenGate modular cloning system for Cas9 and Cas12a use in barley and wheat. Proven Cas nuclease and guide expression cassette options found in the toolkit will facilitate highly efficient simplex and multiplex mutagenesis in both species. We incorporate GRF-GIF transformation boosting cassettes in wheat options to maximise workflow efficiency.

Background: Chinese Cymbidium orchids, cherished for their deep-rooted cultural significance and significant economic value in China, have spawned a rich tapestry of cultivars. However, these orchid cultivars are facing challenges from insufficient cultivation practices and antiquated techniques, including cultivar misclassification, complex identification, and the proliferation of counterfeit products. Current commercial techniques and academic research primarily emphasize species identification of orchids, rather than delving into that of orchid cultivars within species.

Results: To bridge this gap, the authors dedicated over a year to collecting a cultivar image dataset for Chinese Cymbidium orchids named Orchid2024. This dataset contains over 150,000 images spanning 1,275 different categories, involving visits to 20 cities across 12 provincial administrative regions in China to gather pertinent data. Subsequently, we introduced various visual parameter-efficient fine-tuning (PEFT) methods to expedite model development, achieving the highest top-1 accuracy of 86.14% and top-5 accuracy of 95.44%.

Conclusion: Experimental results demonstrate the complexity of the dataset while highlighting the considerable promise of PEFT methods within flower image classification. We believe that our work not only provides a practical tool for orchid researchers, growers and market participants, but also provides a unique and valuable resource for further exploring fine-grained image classification tasks. The dataset and code are available at https://github.com/pengyingshu/Orchid2024 .

Background: Structural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown.

Results: We used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.

Conclusions: Our study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.

As a typical bulb flower, lily is widely cultivated worldwide because of its high ornamental, medicinal and edible value. Although breeding efforts evolved over the last 10000 years, there are still many problems in the face of increasing consumer demand. The approach of biotechnological methods would help to solve this problem and incorporate traits impossible by conventional breeding. Target traits are dormancy, development, color, floral fragrance and resistances against various biotic and abiotic stresses, so as to improve the quality of bulbs and cut flowers in planting, cultivation, postharvest, plant protection and marketing. Genetic transformation technology is an important method for varietal improvement and has become the foundation and core of plant functional genomics research, greatly assisting various plant improvement programs. However, achieving stable and efficient genetic transformation of lily has been difficult worldwide. Many gene function verification studies depend on the use of model plants, which greatly limits the pace of directed breeding and germplasm improvement in lily. Although significant progress has been made in the development and optimization of genetic transformation systems, shortcomings remain. Agrobacterium-mediated genetic transformation has been widely used in lily. However, severe genotypic dependence is the main bottleneck limiting the genetic transformation of lily. This review will summarizes the research progress in the genetic transformation of lily over the past 30 years to generate the material including a section how genome engineering using stable genetic transformation system, and give an overview about recent and future applications of lily transformation. The information provided in this paper includes ideas for optimizing and improving the efficiency of existing genetic transformation methods and for innovation, provides technical support for mining and identifying regulatory genes for key traits, and lays a foundation for genetic improvement and innovative germplasm development in lily.

Background: Oak wilt disease, caused by Bretziella fagacearum is a significant threat to oak (Quercus spp.) tree health in the United States and Eastern Canada. The disease may cause dramatic damage to natural and urban ecosystems without management. Early and accurate diagnosis followed by timely treatment increases the level of disease control success.

Results: A rapid assay based on loop mediated isothermal amplification (LAMP) was first developed with fluorescence detection of B. fagacearum after 30-minute reaction time. Six different primers were designed to specifically bind and amplify the pathogen's DNA. To simplify the use of this assay in the field, gold nanoparticles (AuNPs) were designed to bind to the DNA amplicon obtained from the LAMP reaction. Upon inducing precipitation, the AuNP-amplicons settle as a red pellet visible to the naked eye, indicative of pathogen presence. Both infected and healthy red oak samples were tested using this visualization method. The assay was found to have high diagnostic sensitivity and specificity for the B. fagacearum isolate studied. Moreover, the developed assay was able to detect the pathogen in crude DNA extracts of diseased oak wood samples, which further reduced the time required to process samples.

Conclusions: In summary, the LAMP assay coupled with oligonucleotide-conjugated gold nanoparticle visualization is a promising method for accurate and rapid molecular-based diagnosis of B. fagacearum in field settings. The new method can be adapted to other forest and plant diseases by simply designing new primers.

Background: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions.

Results: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass ( $R^2=0.82$ ) and root surface area ( $R^2=0.8$ ). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits.

Conclusion: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.