Plant Phenotyping Analysis Research Articles

Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.

Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission.

Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

High Concentrations of Se Inhibited the Growth of Rice Seedlings.

Selenium (Se) is crucial for both plants and humans, with plants acting as the main source for human Se intake. In plants, moderate Se enhances growth and increases stress resistance, whereas excessive Se leads to toxicity. The physiological mechanisms by which Se influences rice seedlings' growth are poorly understood and require additional research. In order to study the effects of selenium stress on rice seedlings, plant phenotype analysis, root scanning, metal ion content determination, physiological response index determination, hormone level determination, quantitative PCR (qPCR), and other methods were used. Our findings indicated that sodium selenite had dual effects on rice seedling growth under hydroponic conditions. At low concentrations, Se treatment promotes rice seedling growth by enhancing biomass, root length, and antioxidant capacity. Conversely, high concentrations of sodium selenite impair and damage rice, as evidenced by leaf yellowing, reduced chlorophyll content, decreased biomass, and stunted growth. Elevated Se levels also significantly affect antioxidase activities and the levels of proline, malondialdehyde, metal ions, and various phytohormones and selenium metabolism, ion transport, and antioxidant genes in rice. The adverse effects of high Se concentrations may directly disrupt protein synthesis or indirectly induce oxidative stress by altering the absorption and synthesis of other compounds. This study aims to elucidate the physiological responses of rice to Se toxicity stress and lay the groundwork for the development of Se-enriched rice varieties.

PSTNet: Transformer for aggregating neighborhood features in 3D point cloud semantic segmentation of eggplant plants

Improving the quality of plant point cloud data and achieving precise segmentation is essential for effective plant phenotyping analysis and plant breeding. To ensure data quality and accuracy of segmentation, we employed a self-developed three-dimensional scanning device based on binocular vision to acquire eggplant plant point clouds with high spatial resolution. And we improved down-sampling algorithm Octree Farthest Point Sampling (OFPS) to process the original point cloud data. Furthermore, we proposed a neural network called Plant Segmentation Transformer Network (PSTNet) to achieve semantic segmentation of high spatial resolution eggplant plant datasets. PSTNet comprises the following components: (i) a Neighborhood Feature Aggregator (NPA) for storing and aggregating local neighborhood features of input points, and (ii) a cascaded Point Self-Attention module (PSA) for capturing contextual information. Experimental results demonstrate the remarkable performance of PSTNet in semantic segmentation tasks, with IoU, Precision, Recall, F1-score, and Accuracy values of 92.20 %, 95.30 %, 95.57 %, 95.43 %, and 95.15 %, respectively. Additionally, compared to the Point Cloud Transformer (PCT), which exhibited the second-best performance, PSTNet achieved improvements of 4.37, 1.47, 2.87, 2.17, and 4.22 percentage points in the abovementioned metrics. This method achieves high-precision segmentation of plant point clouds, expands the semantic segmentation method of plants, and lays a solid foundation for plant phenotypic analysis.

Win-Former: Window-Based Transformer for Maize Plant Point Cloud Semantic Segmentation

Semantic segmentation of plant point clouds is essential for high-throughput phenotyping systems, while existing methods still struggle to balance efficiency and performance. Recently, the Transformer architecture has revolutionized the area of computer vision, and has potential for processing 3D point clouds. Applying the Transformer for semantic segmentation of 3D plant point clouds remains a challenge. To this end, we propose a novel window-based Transformer (Win-Former) network for maize 3D organic segmentation. First, we pre-processed the Pheno4D maize point cloud dataset for training. The maize points were then projected onto a sphere surface, and a window partition mechanism was proposed to construct windows into which points were distributed evenly. After that, we employed local self-attention within windows for computing the relationship of points. To strengthen the windows’ connection, we introduced a Cross-Window self-attention (C-SA) module to gather the cross-window features by moving entire windows along the sphere. The results demonstrate that Win-Former outperforms the famous networks and obtains 83.45% mIoU with the lowest latency of 31 s on maize organ segmentation. We perform extensive experiments on ShapeNet to evaluate stability and robustness, and our proposed model achieves competitive results on part segmentation tasks. Thus, our Win-Former model effectively and efficiently segments the maize point cloud and provides technical support for automated plant phenotyping analysis.

Phenotype Analysis of Arabidopsis thaliana Based on Optimized Multi-Task Learning

Deep learning techniques play an important role in plant phenotype research, due to their powerful data processing and modeling capabilities. Multi-task learning has been researched for plant phenotype analysis, which can combine different plant traits and allow for a consideration of correlations between multiple phenotypic features for more comprehensive analysis. In this paper, an intelligent and optimized multi-task learning method for the phenotypic analysis of Arabidopsis thaliana is proposed and studied. Based on the VGG16 network, hard parameter sharing and task-dependent uncertainty are used to weight the loss function of each task, allowing parameters associated with genotype classification, leaf number counting, and leaf area prediction tasks to be learned jointly. The experiments were conducted on the Arabidopsis thaliana dataset, and the proposed model achieved weighted classification accuracy, precision, and Fw scores of 96.88%, 97.50%, and 96.74%, respectively. Furthermore, the coefficient of determination R2 values in the leaf number and leaf area regression tasks reached 0.7944 and 0.9787, respectively.

Point Cloud Registration Based on Fast Point Feature Histogram Descriptors for 3D Reconstruction of Trees

Three-dimensional (3D) reconstruction is an essential technique to visualize and monitor the growth of agricultural and forestry plants. However, inspecting tall plants (trees) remains a challenging task for single-camera systems. A combination of low-altitude remote sensing (an unmanned aerial vehicle) and a terrestrial capture platform (a mobile robot) is suggested to obtain the overall structural features of trees including the trunk and crown. To address the registration problem of the point clouds from different sensors, a registration method based on a fast point feature histogram (FPFH) is proposed to align the tree point clouds captured by terrestrial and airborne sensors. Normal vectors are extracted to define a Darboux coordinate frame whereby FPFH is calculated. The initial correspondences of point cloud pairs are calculated according to the Bhattacharyya distance. Reliable matching point pairs are then selected via random sample consensus. Finally, the 3D transformation is solved by singular value decomposition. For verification, experiments are conducted with real-world data. In the registration experiment on noisy and partial data, the root-mean-square error of the proposed method is 0.35% and 1.18% of SAC-IA and SAC-IA + ICP, respectively. The proposed method is useful for the extraction, monitoring, and analysis of plant phenotypes.

Genetic dissection of the roles of β-hydroxylases in carotenoid metabolism, photosynthesis, and plant growth in tetraploid wheat (Triticum turgidum L.)

Key messageFunctional redundancy and subfunctionalization of β-hydroxylases in tetraploid wheat tissues open up opportunities for manipulation of carotenoid metabolism for trait improvement.The genetic diversity provided by subgenome homoeologs in allopolyploid wheat can be leveraged for developing improved wheat varieties with modified chemical traits, including profiles of carotenoids, which play critical roles in photosynthesis, photoprotection, and growth regulation. Carotenoid profiles are greatly influenced by hydroxylation catalyzed by β-hydroxylases (HYDs). To genetically dissect the contribution of HYDs to carotenoid metabolism and wheat growth and yield, we isolated loss-of-function mutants of the two homoeologs of HYD1 (HYD-A1 and HYD-B1) and HYD2 (HYD-A2 and HYD-B2) from the sequenced ethyl methanesulfonate mutant population of the tetraploid wheat cultivar Kronos, and generated various mutant combinations. Although functional redundancy between HYD1 and HYD2 paralogs was observed in leaves, HYD1 homoeologs contributed more than HYD2 homoeologs to carotenoid β-ring hydroxylation in this tissue. By contrast, the HYD2 homoeologs functioned toward production of lutein, the major carotenoid in mature grains, whereas HYD1 homoeologs had a limited role. These results collectively suggested subfunctionalization of HYD genes and homoeologs in different tissues of tetraploid wheat. Despite reduced photoprotective responses observed in the triple hyd-A1 hyd-B1 hyd-A2 and the quadruple hyd-A1 hyd-B1 hyd-A2 hyd-B2 combinatorial mutants, comprehensive plant phenotyping analysis revealed that all mutants analyzed were comparable to the control for growth, yield, and fertility, except for a slight delay in anthesis and senescence as well as accelerated germination in the quadruple mutant. Overall, this research takes steps toward untangling the functions of HYDs in wheat and has implications for improving performance and consumer traits of this economically important global crop.

FlowerPhenoNet: Automated Flower Detection from Multi-View Image Sequences Using Deep Neural Networks for Temporal Plant Phenotyping Analysis

A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.

A Segmentation-Guided Deep Learning Framework for Leaf Counting.

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.

Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants’ responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.

KAT4IA: K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes.

High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system. The KAT4IA pipeline includes these main steps: self-supervised training set construction, plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant traits, and functional curve fitting of the extracted traits. To deal with the challenge of separating target plants from noisy backgrounds in field images, we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning, which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images. This approach is efficient and does not require human intervention. Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.

DEEP PHENOTYPING CONSIDERING TILE DRAINAGE FROM UAS-BASED MULTISPECTRAL IMAGERY BY CONVOLUTIONAL NEURAL NETWORKS

Abstract. Subsurface agriculture tile lines can greatly impact plant phenotypic characteristics through spatial variation of soil moisture, plant nutrient, and plant rooting depth. Therefore, location of subsurface tile lines plays a critical role in supporting the above ground plant phentoyping and needs to be considered in plant phenotyping analysis. Unnamed Aerial Systems (UAS) imagery together with deep learning methods can develop strong relations between the vegetation spectra and soil parameters.Here, we consider the capability of deep convolutional neural networks (CNN) to evaluate crop quality based on biomass production derived from soil moisture differences by using UAS-based multispectral imagery over soybean breeding fields. Results are still being evaluated, with particular attention to the temporal and spatial resolution of the data required to apply our approach.

Hierarchical Canonical Correlation Analysis Reveals Phenotype, Genotype, and Geoclimate Associations in Plants.

The local environment of the geographical origin of plants shaped their genetic variations through environmental adaptation. While the characteristics of the local environment correlate with the genotypes and other genomic features of the plants, they can also be indicative of genotype-phenotype associations providing additional information relevant to environmental dependence. In this study, we investigate how the geoclimatic features from the geographical origin of the Arabidopsis thaliana accessions can be integrated with genomic features for phenotype prediction and association analysis using advanced canonical correlation analysis (CCA). In particular, we propose a novel method called hierarchical canonical correlation analysis (HCCA) to combine mutations, gene expressions, and DNA methylations with geoclimatic features for informative coprojections of the features. HCCA uses a condition number of the cross-covariance between pairs of datasets to infer a hierarchical structure for applying CCA to combine the data. In the experiments on Arabidopsis thaliana data from 1001 Genomes and 1001 Epigenomes projects and climatic, atmospheric, and soil environmental variables combined by CLIMtools, HCCA provided a joint representation of the genomic data and geoclimate data for better prediction of the special flowering time at 10°C (FT10) of Arabidopsis thaliana. We also extended HCCA with information from a protein-protein interaction (PPI) network to guide the feature learning by imposing network modules onto the genomic features, which are shown to be useful for identifying genes with more coherent functions correlated with the geoclimatic features. The findings in this study suggest that environmental data comprise an important component in plant phenotype analysis. HCCA is a useful data integration technique for phenotype prediction, and a better understanding of the interactions between gene functions and environment as more useful functional information is introduced by coprojections of multiple genomic datasets.

Image Analysis Techniqueson Phenotype for Plant System

In recent years, there is a rapid advancement in computer vision technology which is much effective in extracting useful information from plant images in the field of plant phenomics. Phenomic approaches are widely used in the identification of relationship between phenotypic traits and genetic diversities among the plant species. The need for automation and precision in phenotyping have been accelerated by the significant advancement in genotyping. Regardless of its significance, the shortage of freely available research databases having plant imageries has significantly obstructed the plant image analysis advancement. There were several existing computer vision techniques employed in the analysis of plant phenotypes. Conversely, recent trends in image analysis with the use of machine learning and deep learning based approaches including convolutional neural networks have increased their expansion for providing high-efficiency phenotyping of plant species. Thus, to enhance the efficiency of phenotype analysis, various existing machine learning and deep learning algorithms have been reviewed in this paper along with their methods, advantages, and limitations.

Tomato WRKY81 acts as a negative regulator for drought tolerance by modulating guard cell H2O2–mediated stomatal closure

WRKY transcription factors (TFs) are key regulators in numerous plant biological processes and responses to stresses. Although a group III tomato WRKY, SlWKRY81, is induced by some biotic stressors, its role in drought response remains largely unknown. Here, we unveiled a critical role of SlWKRY81 in regulation of drought response by using agronomic, bioinformatics, genetic and pharmacological approaches. Drought gradually increased the transcript levels of SlWRKY81 and impaired leaf water potential and membrane stability in tomato. Analysis of plant phenotypes revealed that silencing of SlWRKY81 in tomato enhanced tolerance to drought, while its overexpression in Arabidopsis resulted in an opposite phenotype. Notably, the enhanced drought tolerance in SlWRKY81-silenced tomato plants was closely associated with a rapid and increased stomatal closure. Furthermore, such stomatal response in the SlWRKY81-silenced plants was sensitive to abscisic acid alongside drought-induced enhanced accumulation of H2O2 in the guard cells. The results suggest that SlWRKY81 acts as a negative regulator of stomatal closure by suppressing SlRBOH1-derived H2O2 accumulation, which attenuates plant tolerance to drought. These results may have potential implications on improving plant drought tolerance through genetic manipulation.

Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration

Plant morphological data are an important basis for precision agriculture and plant phenomics. The three-dimensional (3D) geometric shape of plants is complex, and the 3D morphology of a plant changes relatively significantly during the full growth cycle. In order to make high-throughput measurements of the 3D morphological data of greenhouse plants, it is necessary to frequently adjust the relative position between the sensor and the plant. Therefore, it is necessary to frequently adjust the Kinect sensor position and consequently recalibrate the Kinect sensor during the full growth cycle of the plant, which significantly increases the tedium of the multiview 3D point cloud reconstruction process. A high-throughput 3D rapid greenhouse plant point cloud reconstruction method based on autonomous Kinect v2 sensor position calibration is proposed for 3D phenotyping greenhouse plants. Two red–green–blue–depth (RGB-D) images of the turntable surface are acquired by the Kinect v2 sensor. The central point and normal vector of the axis of rotation of the turntable are calculated automatically. The coordinate systems of RGB-D images captured at various view angles are unified based on the central point and normal vector of the axis of the turntable to achieve coarse registration. Then, the iterative closest point algorithm is used to perform multiview point cloud precise registration, thereby achieving rapid 3D point cloud reconstruction of the greenhouse plant. The greenhouse tomato plants were selected as measurement objects in this study. Research results show that the proposed 3D point cloud reconstruction method was highly accurate and stable in performance, and can be used to reconstruct 3D point clouds for high-throughput plant phenotyping analysis and to extract the morphological parameters of plants.

Heterologous expression of TuCAX1a and TuCAX1b enhances Ca2+ and Zn2+ translocation in Arabidopsis.

TuCAX1a and TuCAX1b improved Ca2+ and Zn2+ translocation and TuCAX1b enhanced Ca2+, Zn2+, Mn2+ and Fe2+ content when exposed to Cd2+; Cd2+ translocation was inhibited under Ca2+ and Zn2+. Cation/H+ antiporters (CAXs) are involved in the translocation of Ca2+ and various metal ions in higher plants. In the present study, TuCAX1a and TuCAX1b, two cation/H+ antiporters, were isolated from the diploid wheat Triticum urartu, and their metal cation translocation functions investigated. TuCAX1a and TuCAX1b showed abundant tissue-specific expression in the internode and beard, respectively, and their expression levels were increased in shoots exposed to Cd2+, Zn2+ and Ca2+. Plant phenotype analysis showed that overexpression of TuCAX1a and TuCAX1b could improve the tolerance of Arabidopsis to exogenous Ca2+ and Zn2+. In the plant shoots and roots, the contents of Ca2+ and Zn2+ were higher than wild-type plants under Ca2+ and Zn2+ treatments, indicating that TuCAX1a and TuCAX1b can enhance Ca2+ and Zn2+ translocation. Ca2+, Zn2+, Mn2+ and Fe2+ contents showed higher accumulation in TuCAX1b-transgenic Arabidopsis shoots than in wild-type plants exposed to Cd2+, and the translocation of Cd2+ was inhibited under Ca2+ and Zn2+. Overall, the present study provides a novel genetic resource for improving the uptake of microelements and reducing accumulation of toxic heavy metals in wheat.

Stability of T-DNA Integration in Phalaenopsis “Sogo Vivien” Transgenic Orchid Carrying 35S::Gal4::AtRKD4::GR

Orchid is an elegant ornamental plant and favoured by the society. Phalaenopsis "Sogo vivien" is a mini-sized orchid with an interesting white-striped purple petals. This study was aimed to analyze the stability of the integration of embryonic gene carrier T-DNA from Arabidobsis AtRKD4 into the P. "Sogo vivien" genome produced in 2016. The study was conducted in 3 stages: 1) Transgenic plant phenotype analysis (1 year old); 2) Examination of T-DNA integration in orchid genotypes using PCR. 3) Analysis of transgenic plant leaf explants’ ability to produce somatic embryo in vitro. In vitro cultures were performed on the base medium of New Phalaenopsis (NP), plus various concentrations of TDZ (0, 1, 2 mg.L-1) and IBA (0, 1, 2 mg.L-1) or without TDZ and IBA as controls. The transgenic Phalaenopsis ‘Sogo vivien’ were transferred to pot mediums via ex vitro with two treatments: the first leaves were cut as explants for in vitro culture, and the plants were transferred to the mixture of fern medium with shavings of bark. The integration of T-DNA in the genome was detected by DNA genome amplification from the second leaves using the AtRKD4 gene primers and the POH1 gene. The results showed that the highest number of somatic embryo (SE) propagules or protocorm like bodies (PLBs) amounted to 27 were derived from transgenic plant # 2 cultured on NP + 2 mg.L-1 TDZ +1 mg.L-1 IBA medium. The presence of AtRKD4 transgenes were detected with the amplification of 380 bp of the RKD4 gene from the genome of transgenic plant # 2 by using PCR. There were 2 out of 15 plants that positively carry the AtRKD4 gene and produce SE. Thus, the stability of the AtRKD4 carrier T-DNA integration in the genomes of transgenic plants was 13.3%.

PlantCV v2: Image analysis software for high-throughput plant phenotyping.

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.