Estimating Leaf Nitrogen Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in Wheat.

The accuracy of leaf nitrogen accumulation (LNA) estimation is often compromised by the vertical heterogeneity of crop nitrogen. In this study, an estimation model of LNA considering vertical heterogeneity of wheat was developed based on unmanned aerial vehicle (UAV) multispectral data and near-ground hyperspectral data, both collected at different view zenith angles (e.g.

The 3D dynamic visualization simulation of rice plant based on morphological structure model and the application in phenotypic calculation.

The virtual crop stands as a vital content in crop model research field, and has become an indispensable tool for exploring crop phenotypes. The focal objective of this undertaking is to realize three-dimensional (3D) dynamic visualization simulations of rice individual and rice populations, as well as to predict rice phenotype using virtual rice. Leveraging our laboratory's existing research findings, we have realized 3D dynamic visualizations of rice individual and populations across various growth degree days (GDD) by integrating the synchronization relationship between the above-ground parts and the root system in rice plant.

Improving Process-Based Modelling to Simulate the Effects of Low-Temperature Stress During Pre-Anthesis on the Quality Characteristics of Wheat Grains.

Low temperatures in late spring pose a potential threat to the maintenance of grain yield and quality. Despite the importance of protein and starch in wheat quality, they are often overlooked in models addressing climate change effects. In this study, we conducted multiyear environment-controlled phytotron experiments and observed adverse effects resulting from low-temperature stress (LTS) on plant carbon and nitrogen dynamics, grain protein and starch formation, and sink capacity.

A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion.

Accurate monitoring of wheat phenological stages is essential for effective crop management and informed agricultural decision-making. Traditional methods often rely on labour-intensive field surveys, which are prone to subjective bias and limited temporal resolution. To address these challenges, this study explores the potential of near-surface cameras combined with an advanced deep-learning approach to derive wheat phenological stages from high-quality, real-time RGB image series.

Individual and combined effects of high-temperature stress at booting and flowering stages on rice grain quality.

Background: High temperature stress (HTS) has become a serious threat to rice grain quality and few studies have examined the effects of HTS across multiple stages on rice grain quality. In the present study, we conducted 2 years of HTS treatments under three temperature regimes (32/22 °C, 40/30 °C and 44/34 °C) and HTS durations of 2 days and 4 days at three critical stages: booting, flowering, and a combination of booting and flowering. We employed the heat degree days (HDD) metric, which accounts for both the level and duration of HTS, to quantify the relationships between grain quality traits and HTS.

Design and implementation of a portable snapshot multispectral imaging crop-growth sensor.

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration.

Predicting rice phenology across China by integrating crop phenology model and machine learning.

This study explores the integration of crop phenology models and machine learning approaches for predicting rice phenology across China, to gain a deeper understanding of rice phenology prediction. Multiple approaches were used to predict heading and maturity dates at 337 locations across the main rice growing regions of China from 1981 to 2020, including crop phenology model, machine learning and hybrid model that integrate both approaches. Furthermore, an interpretable machine learning (IML) using SHapley Additive exPlanation (SHAP) was employed to elucidate influence of climatic and varietal factors on uncertainty in crop phenology model predictions.

Transport and spatio-temporal conversion of sugar facilitate the formation of spatial gradients of starch in wheat caryopses.

Wheat grain starch content displays large variations within different pearling fractions, which affecting the processing quality of corresponding flour, while the underlying mechanism on starch gradient formation is unclear. Here, we show that wheat caryopses acquire sugar through the transfer of cells (TCs), inner endosperm (IE), outer endosperm (OE), and finally aleurone (AL) via micro positron emission tomography-computed tomography (PET-CT). To obtain integrated information on spatial transcript distributions, developing caryopses are laser microdissected into AL, OE, IE, and TC.

Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study.

Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics.

An intelligent spraying system for weeds in wheat fields based on a dynamic model of droplets impacting wheat leaves.

Introduction: Targeted herbicide application refers to precise application of herbicides in weed-infested areas according to the location and density of farmland weeds. At present, targeted herbicide application in wheat fields generally faces problems including the low herbicide adhesion rate, leading to omission and excessive loss of herbicides.

Methods: To solve these problems, changes in the impact force of herbicide and the weed leaves in the operation process of a spraying system were studied from the interaction between weeds and herbicides applied.

Effects of different observed datasets on the calibration of crop model parameters with GLUE: A case study using the CROPGRO-Soybean phenological model.

Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method.

Spatio-temporal change of winter wheat yield and its quantitative responses to compound frost-dry events - An example of the Huang-Huai-Hai Plain of China from 2001 to 2020.

Extreme climate events such as frost and drought have great influence on wheat growth and yield. Understanding the effects of frost, drought and compound frost-dry events on wheat growth and yield is of great significance for ensuring national food security. In this study, wheat yield prediction model (SCYMvp) was developed by combining crop growth model (CGM), satellite images and meteorological variables.

Rumen fluid pretreatment promotes anaerobic methane production: revealing microbial dynamics driving increased acid yield from different concentrations of corn straw.

In this work, the corn straw (CS) with concentrations of 3%, 6%, and 9% (w/v) were pretreated by rumen fluid (RF) and then used for batched mesophilic biogas production. The results showed that after a 6-day pretreatment, volatile fatty acid (VFAs) production of 3.78, 8.

A method for small-sized wheat seedlings detection: from annotation mode to model construction.

The number of seedlings is an important indicator that reflects the size of the wheat population during the seedling stage. Researchers increasingly use deep learning to detect and count wheat seedlings from unmanned aerial vehicle (UAV) images. However, due to the small size and diverse postures of wheat seedlings, it can be challenging to estimate their numbers accurately during the seedling stage.

A low red/far-red ratio restricts nitrogen assimilation by inhibiting nitrate reductase associated with downregulated TaNR1.2 and upregulated TaPIL5 in wheat (Triticum aestivum L.).

Understanding the physiological mechanism underlying nitrogen levels response to a low red/far-red ratio (R/FR) can provide new insights for optimizing wheat yield potential but has been not well documented. This study focused on the changes in nitrogen levels, nitrogen assimilation and nitrate uptake in wheat plants grown with and without additional far-red light. A low R/FR reduced wheat nitrogen accumulation and grain yield compared with the control.

Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR.

Background: The metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity.

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.

Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background.

SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery.

Rapid and accurate estimation of panicle number per unit ground area (PNPA) in winter wheat before heading is crucial to evaluate yield potential and regulate crop growth for increasing the final yield. The accuracies of existing methods were low for estimating PNPA with remotely sensed data acquired before heading since the spectral saturation and background effects were ignored. This study proposed a spectral-textural PNPA sensitive index (SPSI) from unmanned aerial vehicle (UAV) multispectral imagery for reducing the spectral saturation and improving PNPA estimation in winter wheat before heading.

Effects of pre-anthesis low-temperature stress on the mineral components in wheat grains.

Introduction: The nutritional value of wheat is important to human health. Despite minerals being essential nutrients for the human body, they are often neglected in consideration of the nutritional quality of cereal grains. Extreme low-temperature events have become more frequent due to the current environmental unpredictability, and it is yet unknown how the mineral components in grains are affected by low temperature.

Improving the predictions of leaf photosynthesis during and after short-term heat stress with current rice models.

In response to increasing global warming, extreme heat stress significantly alters photosynthetic production. While numerous studies have investigated the temperature effects on photosynthesis, factors like vapour pressure deficit (VPD), leaf nitrogen, and feedback of sink limitation during and after extreme heat stress remain underexplored. This study assessed photosynthesis calculations in seven rice growth models using observed maximum photosynthetic rate (P ) during and after short-term extreme heat stress in multi-year environment-controlled experiments.

Interaction of Genotype, Environment, and Management on Organ-Specific Critical Nitrogen Dilution Curve in Wheat.

The organ-specific critical nitrogen (N) dilution curves are widely thought to represent a new approach for crop nitrogen (N) nutrition diagnosis, N management, and crop modeling. The N dilution curve can be described by a power function (N = A·W), while parameters A and A control the starting point and slope. This study aimed to investigate the uncertainty and drivers of organ-specific curves under different conditions.

Extreme low-temperature events can alleviate micronutrient deficiencies while increasing potential health risks from heavy metals in rice.

Despite global warming, extreme low-temperature stress (LTS) events pose a significant threat to rice production (especially in East Asia) that can significantly impact micronutrient and heavy metal elements in rice. With two billion people worldwide facing micronutrient deficiencies (MNDs) and widespread heavy metal pollution in rice, understanding these impacts is crucial. We conducted detailed extreme LTS experiments with two rice (Oryza sativa L.

Effect of High-Molecular Weight Glutenin Subunits (HMW-GSs) on Gluten Polymerization during Biscuit Making: Insights from Experimental and Molecular Dynamics Simulation Study.

The effect of high-molecular weight glutenin subunits (HMW-GSs) on gluten polymerization during biscuit making was investigated using a set of HMW-GS deletion lines. Results showed that the deletion of HMW-GSs improved the biscuit quality compared with the wild type (WT), especially in x-type HMW-GS deletion lines. Slight gluten depolymerization was observed during dough mixing, while progressive gluten polymerization occurred during biscuit baking.