PanicleNeRF: Low-Cost, High-Precision In-Field Phenotyping of Rice Panicles with Smartphone.

The rice panicle traits substantially influence grain yield, making them a primary target for rice phenotyping studies. However, most existing techniques are limited to controlled indoor environments and have difficulty in capturing the rice panicle traits under natural growth conditions. Here, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field based on the video acquired by the smartphone.

Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.

The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs).

GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data.

Wheat () is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits.

Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge. Acquiring high-quality 3D MS point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure. Here, we present a novel 3D spatial-spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field (NeREF) for radiometric calibration.

Genome-wide association study reveals the genetic basis for petal-size formation in rapeseed (Brassica napus) and CRISPR-Cas9-mediated mutagenesis of BnFHY3 for petal-size reduction.

Petals in rapeseed (Brassica napus) serve multiple functions, including protection of reproductive organs, nutrient acquisition, and attraction of pollinators. However, they also cluster densely at the top, forming a thick layer that absorbs and reflects a considerable amount of photosynthetically active radiation. Breeding genotypes with large, small, or even petal-less varieties, requires knowledge of primary genes for allelic selection and manipulation.

Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images.

Accurate and high-throughput plant phenotyping is important for accelerating crop breeding. Spectral imaging that can acquire both spectral and spatial information of plants related to structural, biochemical, and physiological traits becomes one of the popular phenotyping techniques. However, close-range spectral imaging of plants could be highly affected by the complex plant structure and illumination conditions, which becomes one of the main challenges for close-range plant phenotyping.

Phenotyping of Silique Morphology in Oilseed Rape Using Skeletonization with Hierarchical Segmentation.

Silique morphology is an important trait that determines the yield output of oilseed rape (.). Segmenting siliques and quantifying traits are challenging because of the complicated structure of an oilseed rape plant at the reproductive stage.

Structural variations and environmental specificities of flowering time-related genes in Brassica napus.

We found that the flowering time order of accessions in a genetic population considerably varied across environments, and homolog copies of essential flowering time genes played different roles in different locations. Flowering time plays a critical role in determining the life cycle length, yield, and quality of a crop. However, the allelic polymorphism of flowering time-related genes (FTRGs) in Brassica napus, an important oil crop, remains unclear.

The level and integrity of plasma circulating cell-free DNA in patients with primary multiple myeloma.

Background: To evaluate the clinical research related to the level and integrity of circulating free DNA (cfDNA) in the plasma of patients with multiple myeloma (MM).

Methods: The plasma samples of 56 patients with newly diagnosed MM and 60 healthy volunteers were collected. ALU247 fragment and ALU115 fragment were used as target genes, and quantitative polymerase chain reaction (qPCR) was used to assess the plasma of the patient and healthy control groups.

Advances in optical phenotyping of cereal crops.

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction.

A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles.

Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP).

Spatiotemporal Heterogeneity of Chlorophyll Content and Fluorescence Response Within Rice ( L.) Canopies Under Different Nitrogen Treatments.

Accurate acquisition of plant phenotypic information has raised long-standing concerns in support of crop breeding programs. Different methods have been developed for high throughput plant phenotyping, while they mainly focused on the canopy level without considering the spatiotemporal heterogeneity at different canopy layers and growth stages. This study aims to phenotype spatiotemporal heterogeneity of chlorophyll (Chl) content and fluorescence response within rice leaves and canopies.

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras.

Three-dimensional (3D) structure is an important morphological trait of plants for describing their growth and biotic/abiotic stress responses. Various methods have been developed for obtaining 3D plant data, but the data quality and equipment costs are the main factors limiting their development. Here, we propose a method to improve the quality of 3D plant data using the time-of-flight (TOF) camera Kinect V2.

Optimal temporal-spatial fluorescence techniques for phenotyping nitrogen status in oilseed rape.

Nitrogen (N) fertilizer maximizes the growth of oilseed rape (Brassica napus L.) by improving photosynthetic performance. Elucidating the dynamic relationship between fluorescence and plant N status could provide a non-destructive diagnosis of N status and the breeding of N-efficient cultivars.

Combining near-infrared hyperspectral imaging with elemental and isotopic analysis to discriminate farm-raised pacific white shrimp from high-salinity and low-salinity environments.

White shrimp (Litopenaeus vannamei) raised in low-salinity farm are considered inferior to those in seawater. In order to develop a rapid discrimination method for the food industry, we investigated the potential of using near-infrared hyperspectral imaging to discriminate shrimp muscle samples from freshwater and seawater farms. We constructed 3 different discrimination models with 4 optimal wavelength selection methods and compared the performance of each model.

Time-Series Chlorophyll Fluorescence Imaging Reveals Dynamic Photosynthetic Fingerprints of Mutants to Drought Stress.

Resistance to drought stress is one of the most favorable traits in breeding programs yet drought stress is one of the most poorly addressed biological processes for both phenomics and genetics. In this study, we investigated the potential of using a time-series chlorophyll fluorescence (ChlF) analysis to dissect the ChlF fingerprints of salt overly sensitive (SOS) mutants under drought stress. Principle component analysis (PCA) was used to identify a shifting pattern of different genotypes including mutants and wild type (WT) Col-0.

Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality.

Background: The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS.

Results: The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with R  = 0.

Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras.

Background: Unmanned aerial vehicle (UAV)-based remote sensing provides a flexible, low-cost, and efficient approach to monitor crop growth status at fine spatial and temporal resolutions, and has a high potential to accelerate breeding process and improve precision field management.

Method: In this study, we discussed the use of lightweight UAV with dual image-frame snapshot cameras to estimate aboveground biomass (AGB) and panicle biomass (PB) of rice at different growth stages with different nitrogen (N) treatments. The spatial-temporal variations in the typical vegetation indices (VIs) and AGB were first investigated, and the accuracy of crop surface model (CSM) extracted from the Red Green Blue (RGB) images at two different stages were also evaluated.

Phenotyping of Drought Stress Response Using Kinetic Chlorophyll Fluorescence and Multicolor Fluorescence Imaging.

Plant responses to drought stress are complex due to various mechanisms of drought avoidance and tolerance to maintain growth. Traditional plant phenotyping methods are labor-intensive, time-consuming, and subjective. Plant phenotyping by integrating kinetic chlorophyll fluorescence with multicolor fluorescence imaging can acquire plant morphological, physiological, and pathological traits related to photosynthesis as well as its secondary metabolites, which will provide a new means to promote the progress of breeding for drought tolerant accessions and gain economic benefit for global agriculture production.

Lipopolysaccharides Trigger Two Successive Bursts of Reactive Oxygen Species at Distinct Cellular Locations.

Lipopolysaccharides (LPS) are major components of the outer membrane of gram-negative bacteria and are an important microbe-associated molecular pattern (MAMP) that triggers immune responses in plants and animals. A previous genetic screen in Arabidopsis () identified LIPOOLIGOSACCHARIDE-SPECIFIC REDUCED ELICITATION (LORE), a B-type lectin -domain receptor kinase, as a sensor of LPS. However, the LPS-activated LORE signaling pathway and associated immune responses remain largely unknown.

Chlorophyll Fluorescence Imaging Uncovers Photosynthetic Fingerprint of Citrus Huanglongbing.

Huanglongbing (HLB) is one of the most destructive diseases of citrus, which has posed a serious threat to the global citrus production. This research was aimed to explore the use of chlorophyll fluorescence imaging combined with feature selection to characterize and detect the HLB disease. Chlorophyll fluorescence images of citrus leaf samples were measured by an in-house chlorophyll fluorescence imaging system.