Plant Methods

Reflectance spectroscopy, like IR, VIS–NIR, combined with chemometric, has been widely used in plant leaf chemical analysis. But less studies have been made on the application of NIR reflectance spectroscopy to plant leaf color and pigments analysis and the possibility of using it for genetic breeding selection. Here, we examine the ability of NIR reflectance spectroscopy to determine the plant leaf color and chlorophyll content in Sassafras tzumu leaves and use the prediction results for genetic selection. Fresh and living tree leaves were used for NIR spectra collection, leaf color parameters (a*, b* and L*) and chlorophyll content were measured with standard analytical methods, partial least squares regression (PLSR) were used for model construction, the coefficient of determination (R2) [cross-validation ( $${\text{R}}^{2}_{\text{CV}}$$ ) and validation ( $${\text{R}}^{2}_{\text{V}}$$ )] and root mean square error (RMSE) [cross-validation (RMSECV) and validation (RMSEV)] were used for model performance evaluation, significant Multivariate Correlation algorithm was applied for model improvement, to find out the most important region related to the leaf color parameters and chlorophyll model, which have been simulated 100 times for accuracy estimation. Leaf color parameters (a*, b* and L*) and chlorophyll content were well predicted by NIR reflectance spectroscopy on fresh leaves in vivo. The mean $${\text{R}}^{2}_{\text{CV}}$$ and RMSECV of a*, b*, L* and chlorophyll content were (0.82, 4.43), (0.63, 3.72), (0.61, 2.35) and (0.86, 0.13%) respectively. Three most important NIR regions, including 1087, 1215 and 2219 nm, which were highly related to a*, b*, L* and chlorophyll content were found. NIR reflectance spectra technology can be successfully used for genetic breeding program. High heritability of a*, b*, L* and chlorophyll content (h2 = 0.77, 0.89, 0.78, 0.81 respectively) were estimated. Several families with bright red color or bright yellow color were selected. NIR spectroscopy is promising for the rapid prediction of leaf color and chlorophyll content of living fresh leaves. It has the ability to simultaneously measure multiple plant leaf traits, potentially allowing for quick and economic prediction in situ.

Plant Methods is a new journal for plant biologists, specialising in the rapid publication of peer-reviewed articles with a focus on technological innovation in the plant sciences. The aim of Plant Methods is to stimulate the development and adoption of new and improved techniques and research tools in plant biology. We hope to promote more consistent standards in the plant sciences, and make readily accessible laboratory and computer-based research tools available to the whole community. This will be achieved by publishing Research articles, Methodology papers and Reviews using the BioMed Central Open Access publishing model. The journal is supported by a prestigious editorial board, whose members all recognise the importance of technological innovation as a driver for basic science.

Effective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Nevertheless, traditional image-based methods for obtaining high-throughput soybean seed phenotype are not robust and practical. Although deep learning-based algorithms can achieve accurate training and strong generalization capabilities, it requires a large amount of ground truth data which is often the limitation step. We showed a novel synthetic image generation and augmentation method based on domain randomization. We synthesized a plenty of labeled image dataset automatedly by our method to train instance segmentation network for high throughput soybean seeds segmentation. It can pronouncedly decrease the cost of manual annotation and facilitate the preparation of training dataset. And the convolutional neural network can be purely trained by our synthetic image dataset to achieve a good performance. In the process of training Mask R-CNN, we proposed a transfer learning method which can reduce the computing costs significantly by finetuning the pre-trained model weights. We demonstrated the robustness and generalization ability of our method by analyzing the result of synthetic test datasets with different resolution and the real-world soybean seeds test dataset. The experimental results show that the proposed method realized the effective segmentation of individual soybean seed and the efficient calculation of the morphological parameters of each seed and it is practical to use this approach for high-throughput objects instance segmentation and high-throughput seeds phenotyping.

Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquisition of huge amount of labeled image datasets for each crop is expensive and time-consuming. Thus, to catalyze the widespread usage of automatic object detection for crop phenotyping, a cost-effective method to develop such automated systems is essential. We propose a point supervision based active learning approach for panicle detection in cereal crops. In our approach, the model constantly interacts with a human annotator by iteratively querying the labels for only the most informative images, as opposed to all images in a dataset. Our query method is specifically designed for cereal crops which usually tend to have panicles with low variance in appearance. Our method reduces labeling costs by intelligently leveraging low-cost weak labels (object centers) for picking the most informative images for which strong labels (bounding boxes) are required. We show promising results on two publicly available cereal crop datasets—Sorghum and Wheat. On Sorghum, 6 variants of our proposed method outperform the best baseline method with more than 55% savings in labeling time. Similarly, on Wheat, 3 variants of our proposed methods outperform the best baseline method with more than 50% of savings in labeling time. We proposed a cost effective method to train reliable panicle detectors for cereal crops. A low cost panicle detection method for cereal crops is highly beneficial to both breeders and agronomists. Plant breeders can obtain quick crop yield estimates to make important crop management decisions. Similarly, obtaining real time visual crop analysis is valuable for researchers to analyze the crop’s response to various experimental conditions.

The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears. Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms.

Grapevine, as an essential fruit crop with high economic values, has been the focus of molecular studies in diverse areas. Two challenges exist in the grapevine research field: (i) the lack of a rapid, user-friendly and effective RNA isolation protocol for mature dark-skinned berries and, (ii) the lack of validated reference genes that are stable for quantification of gene expression across desired experimental conditions. Successful isolation of RNA with sufficient yield and quality is essential for downstream analyses involving nucleic acids. However, ripe berries of dark-skinned grape cultivars are notoriously challenging in RNA isolation due to high contents of polyphenolics, polysaccharides, RNase and water. We have optimized an RNA isolation protocol through modulating two factors at the lysis step that could impact results of RNA isolation - 2-ME concentration and berry mass. By finding the optimal combination among the two factors, our refined protocol was highly effective in isolating total RNA with high yield and quality from whole mature berries of an array of dark-skinned wine grape cultivars. Our protocol takes a much shorter time to complete, is highly effective, and eliminates the requirement for hazardous organic solvents. We have also shown that the resulting RNA preps were suitable for multiple downstream analyses, including the detection of viruses and amplification of grapevine genes using reverse transcription-polymerase chain reaction (RT-PCR), gene expression analysis via quantitative reverse transcription PCR (RT-qPCR), and RNA Sequencing (RNA-Seq). By using RNA-Seq data derived from Cabernet Franc, we have identified seven novel reference gene candidates (CYSP, NDUFS8, YLS8, EIF5A2, Gluc, GDT1, and EF-Hand) with stable expression across two tissue types, three developmental stages and status of infection with grapevine leafroll-associated virus 3 (GLRaV-3). We evaluated the stability of these candidate genes together with two conventional reference genes (actin and NAD5) using geNorm, NormFinder and BestKeeper. We found that the novel reference gene candidates outperformed both actin and NAD5. The three most stable reference genes were CYSP, NDUFS8 and YSL8, whereas actin and NAD5 were among the least stable. We further tested if there would be a difference in RT-qPCR quantification results when the most stable (CYSP) and the least stable (actin and NAD5) genes were used for normalization. We concluded that both actin and NAD5 led to erroneous RT-qPCR results in determining the statistical significance and fold-change values of gene expressional change. We have formulated a rapid, safe and highly effective protocol for isolating RNA from recalcitrant berry tissue of wine grapes. The resulting RNA is of high quality and suitable for RT-qPCR and RNA-Seq. We have identified and validated a set of novel reference genes based on RNA-Seq dataset. We have shown that these new reference genes are superior over actin and NAD5, two of the conventional reference genes commonly used in early studies.

Prenylquinones are key compounds of the thylakoid membranes in chloroplasts. To understand the mechanisms involved in the response of plants to changing conditions such as high light intensity, the comprehensive analysis of these apolar lipids is an essential but challenging step. Conventional methods are based on liquid chromatography coupled to ultraviolet and fluorescence detection of a single or limited number of prenylquinones at a time. Here we present an original and rapid approach using ultra-high pressure liquid chromatography-atmospheric pressure chemical ionization-quadrupole time-of-flight mass spectrometry (UHPLC-APCI-QTOFMS) for the simultaneous profiling of eleven prenylquinones in plant tissues, including α-tocopherol, phylloquinone, plastochromanol-8 and plastoquinone-9. Mass spectrometry and chromatography parameters were optimized using pure standards. Sample preparation time was kept to minimum and different extraction solvents were evaluated for yield, ability to maintain the redox state of prenylquinones, and compatibility with chromatography. In addition to precise absolute quantification of 5 prenyllipids for which standards were available, relative quantification of 6 other related compounds was possible thanks to the high identification power of QTOFMS. Prenylquinone levels were measured in leaves of Arabidopsis grown under normal and high light intensities. Quantitatively, the obtained results were consistent with those reported in various previous studies, demonstrating that this new method can profile the full range of prenylquinones in a very short time. The new profiling method proves faster, more sensitive and can detect more prenylquinones than current methods based on measurements of selected compounds. It enables the extraction and analysis of twelve samples in only 1.5 h and may be applied to other plant species or cultivars.

Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential. In this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks. We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day. An efficient heading date estimation method has been described for rice crops using time series RGB images of crop under natural field conditions. This study demonstrated that our method can reliably be used as a replacement of manual observation to detect the heading date of rice crops.