Highly sensitive SERS detection of pesticide residues based on multi-hotspot buckypaper modified with gold nanoparticles.

To effectively extract target analytes from complex sample surfaces is of great significance for the practical application of surface-enhanced Raman scattering (SERS) spectroscopy. A plasmonic substrate with multiple "hotspots" for highly sensitive detection of pesticide residues were prepared successfully by assembling gold nanoparticles on buckypaper (AuNPs-BP). The substrate exhibited high SERS enhancement and excellent detection sensitivity, with a detection limit (LOD) of 2.03 × 10-11 M and 6.88 × 10-12 M for the probe molecule R6G and MB, respectively. Combined with 3D finite-difference time-domain (3D-FDTD) simulation, the excellent SERS performance of the substrate was attributed to the enhancement of the electromagnetic field around the "hotspots". Additionally, the substrates exhibited excellent flexibility, allowing easy contact with irregular surfaces and facilitating the collection of target molecules on the sample surface. Using a portable Raman spectrometer, the substrate achieved in situ analysis of chlorpyrifos residues on peach, with a LOD as low as 6.8 × 10-11 M. The method showed high accuracy, with a recovery value ranging from 94.2 % to 115.5 %. The results indicate that the substrate has great potential for rapid and highly sensitive detection of pollutants, especially on non-planar surfaces.

N6-methyladenosine RNA modification regulates cotton drought response in a Ca2+ and ABA-dependent manner.

N6 -methyladenosine (m6 A) is the most prevalent internal modification present in mRNAs, and is considered to participate in a range of developmental and biological processes. Drought response is highly regulated at the genomic, transcriptional and post-transcriptional levels. However, the biological function and regulatory mechanism of m6 A modification in the drought stress response is still poorly understood. We generated a transcriptome-wide m6 A map using drought-resistant and drought-sensitive varieties of cotton under different water deficient conditions to uncover patterns of m6 A methylation in cotton response to drought stress. The results reveal that m6 A represents a common modification and exhibit dramatic changes in distribution during drought stress. More 5'UTR m6 A was deposited in the drought-resistant variety and was associated with a positive effect on drought resistance by regulating mRNA abundance. Interestingly, we observed that increased m6 A abundance was associated with increased mRNA abundance under drought, contributing to drought resistance, and vice versa. The demethylase GhALKBH10B was found to decrease m6 A levels, facilitating the mRNA decay of ABA signal-related genes (GhZEP, GhNCED4 and GhPP2CA) and Ca2+ signal-related genes (GhECA1, GhCNGC4, GhANN1 and GhCML13), and mutation of GhALKBH10B enhanced drought resistance at seedling stage in cotton. Virus-induced gene silencing (VIGS) of two Ca2+ -related genes, GhECA1 and GhCNGC4, reduced drought resistance with the decreased m6 A enrichment on silenced genes in cotton. Collectively, we reveal a novel mechanism of post-transcriptional modification involved in affecting drought response in cotton, by mediating m6 A methylation on targeted transcripts in the ABA and Ca2+ signalling transduction pathways.

CropPainter: an effective and precise tool for trait-to-image crop visualization based on generative adversarial networks.

BACKGROUND: Virtual plants can simulate the plant growth and development process through computer modeling, which assists in revealing plant growth and development patterns. Virtual plant visualization technology is a core part of virtual plant research. The major limitation of the existing plant growth visualization models is that the produced virtual plants are not realistic and cannot clearly reflect plant color, morphology and texture information. RESULTS: This study proposed a novel trait-to-image crop visualization tool named CropPainter, which introduces a generative adversarial network to generate virtual crop images corresponding to the given phenotypic information. CropPainter was first tested for virtual rice panicle generation as an example of virtual crop generation at the organ level. Subsequently, CropPainter was extended for visualizing crop plants (at the plant level), including rice, maize and cotton plants. The tests showed that the virtual crops produced by CropPainter are very realistic and highly consistent with the input phenotypic traits. The codes, datasets and CropPainter visualization software are available online. CONCLUSION: In conclusion, our method provides a completely novel idea for crop visualization and may serve as a tool for virtual crops, which can assist in plant growth and development research.

Highly sensitive gold nanoparticles-modified silver nanorod arrays for determination of methyl viologen.

Gold nanoparticles-modified silver nanorod (AuNPs@AgNR) arrays were fabricated as surface-enhanced Raman spectroscopy (SERS) substrates. The coffee ring effect of the AuNPs@AgNR was explored as a preconcentration method for enriching the target analytes and increasing the "hot spots." Furthermore, methyl viologen (MV) as a toxic herbicide used in agricultural production was successfully determined to investigate the application of the coffee ring effect on AuNPs@AgNR arrays and density functional theory (DFT) was employed to calculate its vibrational modes of corresponding characteristic peaks. Good linearity was obtained in the range 0.10-100 mg/L, and the limit of detection (LOD) of MV was estimated to be 0.01 mg/L, which was lower than the US maximum residue limits (MRLs). This method was also applied to practical detection of MV in river water and apple peel with LODs of 0.10 mg/L and 0.05 mg/L, respectively. SERS results suggest that the coffee ring on AuNPs@AgNR arrays provides a promising way for monitoring environmental pollution and food safety caused by pesticides.

An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation.

High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed 'TPanicle-RCNN,' and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.

Field rice panicle detection and counting based on deep learning.

Panicle number is directly related to rice yield, so panicle detection and counting has always been one of the most important scientific research topics. Panicle counting is a challenging task due to many factors such as high density, high occlusion, and large variation in size, shape, posture et.al. Deep learning provides state-of-the-art performance in object detection and counting. Generally, the large images need to be resized to fit for the video memory. However, small panicles would be missed if the image size of the original field rice image is extremely large. In this paper, we proposed a rice panicle detection and counting method based on deep learning which was especially designed for detecting rice panicles in rice field images with large image size. Different object detectors were compared and YOLOv5 was selected with MAPE of 3.44% and accuracy of 92.77%. Specifically, we proposed a new method for removing repeated detections and proved that the method outperformed the existing NMS methods. The proposed method was proved to be robust and accurate for counting panicles in field rice images of different illumination, rice accessions, and image input size. Also, the proposed method performed well on UAV images. In addition, an open-access and user-friendly web portal was developed for rice researchers to use the proposed method conveniently.

Graphene-embedded oblique V-shaped silver nanoarrays for hydrophobic pollutants pre-concentration and high-sensitivity SERS detection.

A surface enhanced Raman scattering (SERS) substrate of silver nanorod modified with graphene and silver nanorod (AgNR@Graphene@AgNR) has been fabricated to improve the sensitivity of SERS detection of hydrophobic pollutants, in which, graphene is an interlayer and AgNR is arranged on both sides of the graphene. The embedded graphene could help the oblique V-shaped AgNR structure to improve the sensitivity of SERS detection with a significant electric field enhancement effect. The annealing treatment of the substrate, shortening the nanometer gap between the graphene and AgNR, is benefit for producing a large number of "hot spots" at the fold, which has been verified by the finite difference time domain (FDTD) simulation. The enhancement factor (EF) of AgNR@Graphene@AgNR could reach up to 1.6 × 108 with a good reproducibility. The substrate could achieve high-sensitivity detection of 4-chlorobiphenyl (PCB-3) and 3, 3', 4, 4'-tetrachlorobiphenyl (PCB-77) with the limit of detections (LODs) of 1.72 × 10-10 M and 2.11 × 10-8 M, and the effective identification of PCBs mixture has been realized through principal component analysis (PCA), which means that the AgNR@Graphene@AgNR substrate has a potential significance for the detection and analysis of hydrophobic pollutant mixtures in the environment.

A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits.

Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits. When manual and automatic measurements were compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm, and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R2 values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and tillering stages, and the R2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.

Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton.

Drought resistance (DR) is a complex trait that is regulated by a variety of genes. Without comprehensive profiling of DR-related traits, the knowledge of the genetic architecture for DR in cotton remains limited. Thus, there is a need to bridge the gap between genomics and phenomics. In this study, an automatic phenotyping platform (APP) was systematically applied to examine 119 image-based digital traits (i-traits) during drought stress at the seedling stage, across a natural population of 200 representative upland cotton accessions. Some novel i-traits, as well as some traditional i-traits, were used to evaluate the DR in cotton. The phenomics data allowed us to identify 390 genetic loci by genome-wide association study (GWAS) using 56 morphological and 63 texture i-traits. DR-related genes, including GhRD2, GhNAC4, GhHAT22 and GhDREB2, were identified as candidate genes by some digital traits. Further analysis of candidate genes showed that Gh_A04G0377 and Gh_A04G0378 functioned as negative regulators for cotton drought response. Based on the combined digital phenotyping, GWAS analysis and transcriptome data, we conclude that the phenomics dataset provides an excellent resource to characterize key genetic loci with an unprecedented resolution which can inform future genome-based breeding for improved DR in cotton.

Thin layer chromatography combined with surface-enhanced raman spectroscopy for rapid sensing aflatoxins.

A fast and facile method was developed for on-site detection of aflatoxins (AFs) in moldy agricultural products using thin layer chromatography combined with surface-enhanced Raman spectroscopy (TLC-SERS). Four different AFs were successfully separated by TLC and then a small portable Raman spectrometer, with gold colloids as the SERS-active substrate, was applied to identify the separated spots. TLC-SERS application to on-site detection of AFs was systematically investigated. Qualitative and quantitative AF detection was found to be easily accomplished and limits of detection were estimated to be 1.5 × 10-6, 1.1 × 10-5, 1.2 × 10-6, and 6.0 × 10-7 M for AFB1, AFB2, AFG1, and AFG2, respectively. The proposed method was also highly selective, enabling successful AF identifications in complex extracts from moldy peanuts. The study showed that TLC-SERS could be effectively used for separation and detection of these four AFs, demonstrating good prospects for on-site qualitative screening of agricultural products.

Novel Digital Features Discriminate Between Drought Resistant and Drought Sensitive Rice Under Controlled and Field Conditions.

Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future.

Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization.

BACKGROUND: Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field's complex background, rice panicle segmentation in the field is a very large challenge. RESULTS: In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online. CONCLUSIONS: In conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth.

With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize (Zea mays) recombinant inbred line population (n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction.

Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice.

Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies.

The functional analysis of the rice genome has entered into a high-throughput stage, and a project named RICE2020 has been proposed to determine the function of every gene in the rice genome by the year 2020. However, as compared with the robustness of genetic techniques, the evaluation of rice phenotypic traits is still performed manually, and the process is subjective, inefficient, destructive and error-prone. To overcome these limitations and help rice phenomics more closely parallel rice genomics, reliable, automatic, multifunctional, and high-throughput phenotyping platforms should be developed. In this article, we discuss the key plant phenotyping technologies, particularly photonics-based technologies, and then introduce their current applications in rice (wheat or barley) phenomics. We also note the major challenges in rice phenomics and are confident that these reliable high-throughput phenotyping tools will give plant scientists new perspectives on the information encoded in the rice genome.

A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice.

The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics research. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and subsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday.

High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography.

Tillering is one of the most important agronomic traits because the number of shoots per plant determines panicle number, a key component of grain yield. The conventional method of counting tillers is still manual. Under the condition of mass measurement, the accuracy and efficiency could be gradually degraded along with fatigue of experienced staff. Thus, manual measurement, including counting and recording, is not only time consuming but also lack objectivity. To automate this process, we developed a high-throughput facility, dubbed high-throughput system for measuring automatically rice tillers (H-SMART), for measuring rice tillers based on a conventional x-ray computed tomography (CT) system and industrial conveyor. Each pot-grown rice plant was delivered into the CT system for scanning via the conveyor equipment. A filtered back-projection algorithm was used to reconstruct the transverse section image of the rice culms. The number of tillers was then automatically extracted by image segmentation. To evaluate the accuracy of this system, three batches of rice at different growth stages (tillering, heading, or filling) were tested, yielding absolute mean absolute errors of 0.22, 0.36, and 0.36, respectively. Subsequently, the complete machine was used under industry conditions to estimate its efficiency, which was 4320 pots per continuous 24 h workday. Thus, the H-SMART could determine the number of tillers of pot-grown rice plants, providing three advantages over the manual tillering method: absence of human disturbance, automation, and high throughput. This facility expands the application of agricultural photonics in plant phenomics.