Phenotype identification and genome-wide association study of ear-internode vascular bundles in maize (Zea mays).

The vascular bundle in the ear-internode of maize is a key conduit for transporting photosynthetic materials between "source" and "sink", making it critically important to examine its micro-phenotypes and genetic architecture to identify advantageous characteristics and cultivate high-yielding and high-quality varieties. Unfortunately, the limited observation methods and scope of study precludes any comprehensive and systematic investigations into the microscopic phenotypes and genetic mechanisms of vascular bundle in maize ear-internode. In this study, 47 phenotypic traits were extracted in 495 maize inbred lines using micro computed tomography (Micro-CT) scanning technology and a deep learning-based phenotype acquisition method for stem vascular bundle, which included stem slice-related, epidermis zone-related, periphery zone-related, inner zone-related and vascular bundles-related traits.

Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.

It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research.

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.

The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence.

Plant microphenotype: from innovative imaging to computational analysis.

The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management.

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery.

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models.

Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology.

Cotton ( L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods.

Using High-Throughput Phenotyping Analysis to Decipher the Phenotypic Components and Genetic Architecture of Maize Seedling Salt Tolerance.

Soil salinization is a worldwide problem that limits agricultural production. It is important to understand the salt stress tolerance ability of maize seedlings and explore the underlying related genetic resources. In this study, we used a high-throughput phenotyping platform with a 3D laser sensor (Planteye F500) to identify the digital biomass, plant height and normalized vegetation index under normal and saline conditions at multiple time points.

Investigating the genetic basis of maize ear characteristics: a comprehensive genome-wide study utilizing high-throughput phenotypic measurement method and system.

The morphology of maize ears plays a critical role in the breeding of new varieties and increasing yield. However, the study of traditional ear-related traits alone can no longer meet the requirements of breeding. In this study, 20 ear-related traits, including size, shape, number, and color, were obtained in 407 maize inbred lines at two sites using a high-throughput phenotypic measurement method and system.

Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning.

Background: The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. It is an important step in the distinctness, uniformity, and stability (DUS) testing to obtain maize tassel phenotype traits. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype traits because of the advances in the point cloud deep learning method.

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.

The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera.

Disentangling the Heterosis in Biomass Production and Radiation Use Efficiency in Maize: A Phytomer-Based 3D Modelling Approach.

Maize ( L.) benefits from heterosis in-yield formation and photosynthetic efficiency through optimizing canopy structure and improving leaf photosynthesis. However, the role of canopy structure and photosynthetic capacity in determining heterosis in biomass production and radiation use efficiency has not been separately clarified.

Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail.

Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building.

Geometric Wheat Modeling and Quantitative Plant Architecture Analysis Using Three-Dimensional Phytomers.

The characterization, analysis, and evaluation of morphology and structure are crucial in wheat research. Quantitative and fine characterization of wheat morphology and structure from a three-dimensional (3D) perspective has great theoretical significance and application value in plant architecture identification, high light efficiency breeding, and cultivation. This study proposes a geometric modeling method of wheat plants based on the 3D phytomer concept.

Characterization and genetic dissection of maize ear leaf midrib acquired by 3D digital technology.

The spatial morphological structure of plant leaves is an important index to evaluate crop ideotype. In this study, we characterized the three-dimensional (3D) data of the ear leaf midrib of maize at the grain-filling stage using the 3D digitization technology and obtained the phenotypic values of 15 traits covering four different dimensions of the ear leaf midrib, of which 13 phenotypic traits were firstly proposed for featuring plant leaf spatial structure. Cluster analysis results showed that the 13 traits could be divided into four groups, Group I, -II, -III and -IV.

Use of 3D modeling to refine predictions of canopy light utilization: A comparative study on canopy photosynthesis models with different dimensions.

Canopy photosynthesis integrates leaf functional and structural traits in space and time and correlates positively with yield formation. Many models with different levels of architectural details ranging from zero-dimensional (0D) to three-dimensional (3D) have been developed to simulate canopy light interception and photosynthesis. Based on these models, a crop growth model can be used to assess crop yield in response to genetic improvement, optimized practices, and environmental change.

A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction.

Plant phenotyping is essential in plant breeding and management. High-throughput data acquisition and automatic phenotypes extraction are common concerns in plant phenotyping. Despite the development of phenotyping platforms and the realization of high-throughput three-dimensional (3D) data acquisition in tall plants, such as maize, handling small-size plants with complex structural features remains a challenge.

LettuceGDB: The community database for lettuce genetics and omics.

As a globally popular leafy vegetable and a representative plant of the Asteraceae family, lettuce has great economic and academic significance. In the last decade, high-throughput sequencing, phenotyping, and other multi-omics data in lettuce have accumulated on a large scale, thus increasing the demand for an integrative lettuce database. Here, we report the establishment of a comprehensive lettuce database, LettuceGDB (https://www.

Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce.

The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits.

Dissecting the Genetic Structure of Maize Leaf Sheaths at Seedling Stage by Image-Based High-Throughput Phenotypic Acquisition and Characterization.

The rapid development of high-throughput phenotypic detection techniques makes it possible to obtain a large number of crop phenotypic information quickly, efficiently, and accurately. Among them, image-based phenotypic acquisition method has been widely used in crop phenotypic identification and characteristic research due to its characteristics of automation, non-invasive, non-destructive and high throughput. In this study, we proposed a method to define and analyze the traits related to leaf sheaths including morphology-related, color-related and biomass-related traits at V6 stage.

High-Throughput Phenotyping Accelerates the Dissection of the Phenotypic Variation and Genetic Architecture of Shank Vascular Bundles in Maize ( L.).

The vascular bundle of the shank is an important 'flow' organ for transforming maize biological yield to grain yield, and its microscopic phenotypic characteristics and genetic analysis are of great significance for promoting the breeding of new varieties with high yield and good quality. In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm.

Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components.

Background: Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis.

Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network.

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels.

Genome-Wide Association Study of Maize Aboveground Dry Matter Accumulation at Seedling Stage.

Dry matter accumulation and partitioning during the early phases of development could significantly affect crop growth and productivity. In this study, the aboveground dry matter (DM), the DM of different organs, and partition coefficients of a maize association mapping panel of 412 inbred lines were evaluated at the third and sixth leaf stages (V3 and V6). Further, the properties of these phenotypic traits were analyzed.

Image-Based High-Throughput Detection and Phenotype Evaluation Method for Multiple Lettuce Varieties.

The yield and quality of fresh lettuce can be determined from the growth rate and color of individual plants. Manual assessment and phenotyping for hundreds of varieties of lettuce is very time consuming and labor intensive. In this study, we utilized a "Sensor-to-Plant" greenhouse phenotyping platform to periodically capture top-view images of lettuce, and datasets of over 2000 plants from 500 lettuce varieties were thus captured at eight time points during vegetative growth.

Dissecting the phenotypic components and genetic architecture of maize stem vascular bundles using high-throughput phenotypic analysis.

High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high-throughput are urgently needed for large crop populations.