Inferior spikelet filling is affected by T6P/SnRK1-mediated NSC remobilization in large-panicle rice (Oryza sativa L.).

Poor grain filling in inferior spikelets (IS), which is influenced by the remobilization of nonstructural carbohydrates (NSC) stored in the sheath and internode of rice plants, limits the expected high yield of large-panicle rice. NSC remobilization from the sheath to the panicle is regulated by the T6P/SnRK1 pathway. However, in large-panicle rice, it is unclear whether IS grain filling is related to the NSC remobilization mediated by T6P/SnRK1 signaling. In this study, two large-panicle cultivars-W1844 and CJ03-with distinct differences in IS grain filling were used to explore the physiological mechanism mediating IS development. Compared to W1844, CJ03 IS showed lower expression of the genes related to sucrose uploading, later sucrose peaking, and delayed starch accumulation. In the CJ03, low OsSUTs expression and NSC output, transport rate, and contribution rate were detected in the sheaths and internodes. These results suggest that poor NSC remobilization results in insufficient assimilate supply for the IS, and consequently, poor IS grain filling. Furthermore, poor NSC remobilization coincided with the increased T6P content and decreased SnRK1 activity during grain filling in CJ03 IS. The expression levels of genes related to T6P metabolism and those encoding the catalytic subunit of SnRK1 were consistent with the observed T6P content and SnRK1 activity in the sheaths and internodes. Therefore, IS grain filling is potentially affected by T6P/SnRK1 signaling-mediated NSC remobilization in large-panicle rice.

SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.

Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In addition, extracting biologically relevant angle traits is difficult. In this study, we developed a stratified, clustered, and growing-based algorithm (SCAG) for soybean branch detection and branch angle calculation from LiDAR data, which is heuristic, open-source, and expandable. SCAG achieved high branch detection accuracy (F-score = 0.77) and branch angle calculation accuracy (r = 0.84) when evaluated on 152 diverse soybean varieties. Meanwhile, the SCAG outperformed 2 other classic algorithms, the support vector machine (F-score = 0.53) and density-based methods (F-score = 0.55). Moreover, after applying the SCAG to 405 soybean varieties over 2 consecutive years, we quantified various 3D traits, including canopy width, height, stem length, and average angle. After data filtering, we identified novel heritable and repeatable traits for evaluating soybean density tolerance potential, such as the ratio of average angle to height and the ratio of average angle to stem length, which showed greater potential than the well-known ratio of canopy width to height trait. Our work demonstrates remarkable advances in 3D phenotyping and plant architecture screening. The algorithm can be applied to other crops, such as maize and tomato. Our dataset, scripts, and software are public, which can further benefit the plant science community by enhancing plant architecture characterization and ideal variety selection.

Application of slow-controlled release fertilizer coordinates the carbon flow in carbon-nitrogen metabolism to effect rice quality.

Slow-controlled release fertilizers are experiencing a popularity in rice cultivation due to their effectiveness in yield and quality with low environmental costs. However, the underlying mechanism by which these fertilizers regulate grain quality remains inadequately understood. This study investigated the effects of five fertilizer management practices on rice yield and quality in a two-year field experiment: CK, conventional fertilization, and four applications of slow-controlled release fertilizer (UF, urea formaldehyde; SCU, sulfur-coated urea; PCU, polymer-coated urea; BBF, controlled-release bulk blending fertilizer). In 2020 and 2021, the yields of UF and SCU groups showed significant decreases when compared to conventional fertilization, accompanied by a decline in nutritional quality. Additionally, PCU group exhibited poorer cooking and eating qualities. However, BBF group achieved increases in both yield (10.8 t hm-2 and 11.0 t hm-2) and grain quality reaching the level of CK group. The adequate nitrogen supply in PCU group during the grain-filling stage led to a greater capacity for the accumulation of proteins and amino acids in the PCU group compared to starch accumulation. Intriguingly, BBF group showed better carbon-nitrogen metabolism than that of PCU group. The optimal nitrogen supply present in BBF group suitable boosted the synthesis of amino acids involved in the glycolysis/ tricarboxylic acid cycle, thereby effectively coordinating carbon-nitrogen metabolism. The application of the new slow-controlled release fertilizer, BBF, is advantageous in regulating the carbon flow in the carbon-nitrogen metabolism to enhance rice quality.

G × E × M analysis to define allometric rules between leaves and stems in wheat.

Allometric rules provide insights into the structure-function relationships across species and scales and are commonly used in ecology. The fields of agronomy, plant phenotyping and modeling also need simplifications such as allometric rules to reconcile data at different temporal and spatial levels (organs/canopy). This paper explores the variations in relationships for wheat regarding (i) the distribution of crop green area between leaves and stems, and (ii) the allocation of above-ground biomass between leaves and stems during the vegetative period, using a large dataset covering different years, countries, genotypes and management practices. Our results show that the relationship between leaf and stem area was linear, genotype-specific, and sensitive to radiation. The relationship between leaf and stem biomass depended on genotype and nitrogen fertilization. The mass per area, associating area and biomass for both leaf and stem, varied strongly by developmental stage and was significantly affected by environment and genotype. These allometric rules were evaluated with satisfactory performance, and their potential use is discussed with regard to current phenotyping techniques and plant/crop models. Our results enable the definition of models and minimum datasets required for characterizing diversity panels and making predictions in various G × E × M contexts.

Purine permease (PUP) family gene PUP11 positively regulates the rice seed setting rate by influencing seed development.

KEY MESSAGE: Purine permease PUP11 is essential for rice seed development, regulates the seed setting rate, and influences the cytokinin content, sugar transport, and starch biosynthesis during grain development. The distribution of cytokinins in plant tissues determines plant growth and development and is regulated by several cytokinin transporters, including purine permease (PUP). Thirteen PUP genes have been identified within the rice genome; however, the functions of most of these genes remain poorly understood. We found that pup11 mutants showed extremely low seed setting rates and a unique filled seed distribution. Moreover, seed formation arrest in these mutants was associated with the disappearance of accumulated starch 10 days after flowering. PUP11 has two major transcripts with different expression patterns and subcellular locations, and further studies revealed that they have redundant positive roles in regulating the seed setting rate. We also found that type-A Response Regulator (RR) genes were upregulated in the developing grains of the pup11 mutant compared with those in the wild type. The results also showed that PUP11 altered the expression of several sucrose transporters and significantly upregulated certain starch biosynthesis genes. In summary, our results indicate that PUP11 influences the rice seed setting rate by regulating sucrose transport and starch accumulation during grain filling. This research provides new insights into the relationship between cytokinins and seed development, which may help improve cereal yield.

Effects of warming on rice production and metabolism process associated with greenhouse gas emissions.

Evaluating the impact of global warming on rice production and greenhouse gas (GHG) emissions is critical for ensuring food security and mitigating the consequences of climate change. Nonetheless, the impacts of warming on crop production, GHG emissions, and microbial mechanisms in the single-cropping rice systems remain unclear. Here, a two-year field experiment was conducted to explore the effects of warming (increased by 2.7-3.0 °C on average) in the rice growing season on crop production and functional microorganisms associated with GHG emissions. Results showed that warming resulted in significant reduction (p < 0.01) in the aboveground biomass and grain yield as well as in grain weight, the number of spikelets per panicle, and the seed-setting rate. However, it caused a significant increase (p < 0.01) in the number of panicles by 15.6 % and 34.9 %, respectively. Furthermore, warming significantly increased (p < 0.01) seasonal methane (CH4) emissions but reduced nitrous oxide (N2O) emissions, particularly in 2022.The relative abundance of genes associated with CH4 metabolism and nitrogen metabolism was increased by 40.7 % and 32.7 %, respectively, in response to warming. Moreover, warming had a positive impact on the abundance of genes related to CH4 production and oxidation processes but did not affect the denitrification processes associated with N2O production. These results showed that warming decreased rice yield and biomass in the single cropping rice system but increased CH4 emissions and global warming potential. Taken together, to address the increasing food demand of a growing population and mitigate the impacts of global warming, it is imperative to duce GHG emissions and enhance crop yields.

AMPK activator 991 specifically activates SnRK1 and thereby affects seed germination in rice.

Sucrose non-fermenting-1-related protein kinase 1 (SnRK1) and AMP-activated protein kinase (AMPK) are highly conserved. Compound 991 is an AMPK activator in mammals. However, whether 991 also activates SnRK1 remains unknown. The addition of 991 significantly increased SnRK1 activity in desalted extracts from germinating rice seeds in vitro. To determine whether 991 has biological activity, rice seeds were treated with different concentrations of 991. Germination was promoted at low concentrations but inhibited at high concentrations. The effects of 991 on germination were similar to those of OsSnRK1a overexpression. To explore whether 991 affects germination by specifically affecting SnRK1, germination of an snrk1a mutant and the wild type under 1 µM 991 treatment was compared. The snrk1a mutant was insensitive to 991. Phosphoproteomic analysis showed that the differential phosphopeptides induced by 991 and OsSnRK1a overexpression largely overlapped. Furthermore, SnRK1 might regulate rice germination in a dosage-dependent manner by regulating the phosphorylation of three phosphosites, namely S285-PIP2;4, S1013-SOS1, and S110-ABI5. These results indicate that 991 is a specific SnRK1 activator in rice. The promotion and inhibition of germination by 991 also occurred in wheat seeds. Thus, 991 is useful for exploring SnRK1 function and the chemical regulation of growth and development in crops.

Bio-Master: Design and Validation of a High-Throughput Biochemical Profiling Platform for Crop Canopies.

Accurate assessment of crop biochemical profiles plays a crucial role in diagnosing their physiological status. The conventional destructive methods, although reliable, demand extensive laboratory work for measuring various traits. On the other hand, nondestructive techniques, while efficient and adaptable, often suffer from reduced precision due to the intricate interplay of the field environment and canopy structure. Striking a delicate balance between efficiency and accuracy, we have developed the Bio-Master phenotyping system. This system is capable of simultaneously measuring four vital biochemical components of the canopy profile: dry matter, water, chlorophyll, and nitrogen content. Bio-Master initiates the process by addressing structural influences, through segmenting the fresh plant and then further chopping the segment into uniform small pieces. Subsequently, the system quantifies hyperspectral reflectance and fresh weight over the sample within a controlled dark chamber, utilizing an independent light source. The final step involves employing an embedded estimation model to provide synchronous estimates for the four biochemical components of the measured sample. In this study, we established a comprehensive training dataset encompassing a wide range of rice varieties, nitrogen levels, and growth stages. Gaussian process regression model was used to estimate biochemical contents utilizing reflectance data obtained by Bio-Master. Leave-one-out validation revealed the model's capacity to accurately estimate these contents at both leaf and plant scales. With Bio-Master, measuring a single rice plant takes approximately only 5 min, yielding around 10 values for each of the four biochemical components across the vertical profile. Furthermore, the Bio-Master system allows for immediate measurements near the field, mitigating potential alterations in plant status during transportation and processing. As a result, our measurements are more likely to faithfully represent in situ values. To summarize, the Bio-Master phenotyping system offers an efficient tool for comprehensive crop biochemical profiling. It harnesses the benefits of remote sensing techniques, providing significantly greater efficiency than conventional destructive methods while maintaining superior accuracy when compared to nondestructive approaches.

Stimulus-responsive proteins involved in multi-process regulation of storage substance accumulation during rice grain filling under elevated temperature.

BACKGROUND: The intensified global warming during grain filling deteriorated rice quality, in particular increasing the frequency of chalky grains which markedly impact market value. The formation of rice quality is a complex process influenced by multiple genes, proteins and physiological metabolic processes. Proteins responsive to stimulus can adjust the ability of plants to respond to unfavorable environments, which may be an important protein involved in the regulation of quality formation under elevated temperature. However, relatively few studies have hindered our further understanding of rice quality formation under elevated temperature. RESULTS: We conducted the actual field elevated temperature experiment and performed proteomic analysis of rice grains at the early stage of grain filling. Starting with the response to stimulus in GO annotation, 22 key proteins responsive to stimulus were identified in the regulation of grain filling and response to elevated temperature. Among the proteins responsive to stimulus, during grain filling, an increased abundance of signal transduction and other stress response proteins, a decreased abundance of reactive oxygen species-related proteins, and an increased accumulation of storage substance metabolism proteins consistently contributed to grain filling. However, the abundance of probable indole-3-acetic acid-amido synthetase GH3.4, probable indole-3-acetic acid-amido synthetase GH3.8 and CBL-interacting protein kinase 9 belonged to signal transduction were inhibited under elevated temperature. In the reactive oxygen species-related protein, elevated temperature increased the accumulation of cationic peroxidase SPC4 and persulfide dioxygenase ETHE1 homolog to maintain normal physiological homeostasis. The increased abundance of alpha-amylase isozyme 3E and seed allergy protein RA5 was related to the storage substance metabolism, which regulated starch and protein accumulation under elevated temperature. CONCLUSION: Auxin synthesis and calcium signal associated with signal transduction, other stress responses, protein transport and modification, and reactive oxygen species-related proteins may be key proteins responsive to stimulus in response to elevated temperature. Alpha-amylase isozyme 3E and seed allergy protein RA5 may be the key proteins to regulate grain storage substance accumulation and further influence quality under elevated temperature. This study enriched the regulatory factors involved in the response to elevated temperature and provided a new idea for a better understanding of grain response to temperature.

Static magnetic field improvement of the quality of rice dumpling subjected to freeze-thaw cycles: Roles of phase transition of water and changes in structural and physicochemical properties of glutinous rice flour.

This study aimed to investigate the effect of static magnetic field (SMF, 0-10 mT) on the quality of rice dumplings subjected to 7, 14, 21, and 28 freeze-thaw cycles. The underlying mechanism was explored by monitoring changes in water phase transition, water distribution, and structural and physicochemical properties of rice flour. Results suggested that SMF enables the formation of small ice crystals by accelerating freezing rate, shortening phase transition time, and increasing bound water content, which attributes to reducing the mechanical damage on starch granules and thus improves the quality of frozen rice dumpling. After 7-28 freeze-thaw cycles, SMF treatment increased the whiteness by 0.08-1.58, reduced the cracking ratio by 1.67 %-8.34 %, decreased the water loss ratio by 0-0.75 %, and significantly improved the texture of cooked rice dumplings. This study confirmed the feasibility of SMF in improving the quality of rice dumpling, which contributes to expanding the applications of magnetic freezer in the preservation of starch-based foods.

Overexpression of OsSnRK1a through a green tissue-specific promoter improves rice yield by accelerating sheath-to-panicle transport of nonstructural carbohydrates and increasing leaf photosynthesis.

The redistribution of nonstructural carbohydrates (NSCs) in rice (Oryza sativa) sheaths contributes greatly to grain filling. Sucrose nonfermenting-1-related protein kinase 1 (SnRK1) regulates sheath-to-panicle transport of NSCs during rice grain filling; however, it is unknown whether elevated activity of SnRK1 in sheaths improves NSC transport and grain filling. Expression of OsSnRK1a is mainly responsible for regulating SnRK1 activity in rice sheaths. Analysis of transgenic rice plants containing the OsSnRK1a promoter::GUS construct indicated that OsSnRK1a is widely expressed in rice. Notably, OsSnRK1a is highly expressed in mesophyll cells of sheaths. Therefore, a green tissue promoter specifically expressed in sheaths and leaf parenchyma cells and phloem tissue was used to over-express OsSnRK1a in japonica rice. The transgenic lines exhibited increased SnRK1a expression and SnRK1 activity in sheaths. The NSC and starch in the transgenic lines and WT all showed accumulation before heading and during the early-filling stage, and declining at the peak filling stage. But the starch and NSC content in transgenic lines was lower than that of WT. Moreover, the transgenic lines showed lower sucrose contents and higher sucrose efflux rates. The accelerated sheath NSC transport improved grain filling, and stimulated panicle development in transgenic lines. SnRK1a expression and SnRK1 activity were also increased in the leaves of transgenic lines, which improved leaf photosynthetic activity and contributed to optimal grain filling and panicle development. These results verify the promotion of high SnRK1 activity in sheath NSC transport, and also provide a new approach to improving sheath NSC transport and rice yield.

The Inferior Grain Filling Initiation Promotes the Source Strength of Rice Leaves.

Poor grain-filling initiation in inferior spikelets severely impedes rice yield improvement, while photo-assimilates from source leaves can greatly stimulate the initiation of inferior grain-filling (sink). To investigate the underlying mechanism of source-sink interaction, a two-year field experiment was conducted in 2019 and 2020 using two large-panicle rice cultivars (CJ03 and W1844). The treatments included intact panicles and partial spikelet removal. These two cultivars showed no significant difference in the number of spikelets per panicle. However, after removing spikelet, W1844 showed higher promotion on 1000-grain weight and seed-setting rate than CJ03, particularly for inferior spikelets. The reason was that the better sink activity of W1844 led to a more effective initiation of inferior grain-filling compared to CJ03. The inferior grain weight of CJ03 and W1844 did not show a significant increase until 8 days poster anthesis (DPA), which follows a similar pattern to the accumulation of photo-assimilates in leaves. After removing spikelets, the source leaves of W1844 exhibited lower photosynthetic inhibition compared to CJ03, as well as stronger metabolism and transport of photo-assimilates. Although T6P levels remained constant in both cultivars under same conditions, the source leaves of W1844 showed notable downregulation of SnRK1 activity and upregulation of phytohormones (such as abscisic acid, cytokinins, and auxin) after removing spikelets. Hence, the high sink strength of inferior spikelets plays a role in triggering the enhancement of source strength in rice leaves, thereby fulfilling grain-filling initiation demands.

Mitigating the adverse effect of warming on rice canopy and rhizosphere microbial community by nitrogen application: An approach to counteract future climate change for rice.

The adverse impact of climate change on crop production continues to increase, necessitating the development of suitable strategies to mitigate these effects and improve food security. Several studies have revealed how global warming negatively impacts the grain-filling stage of rice and that this effect could be mitigated by nitrogen; however, the impact of nitrogen application on rice canopy and rhizosphere microbial communities remains unclear. We conducted a study using an open-field warming system. Results showed that warming influenced rice canopy by decreasing aboveground biomass and harvest index, whereas nitrogen application had positive effect on rice production under warming conditions by increasing the plant nitrogen content, biomass, harvest index and soil fertilities. Moreover, soil ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) contents were significantly decreased under warming, which were higher after nitrogen application. Notably, warming and nitrogen fertilizer caused 19 % (P < 0.01) and 7 % (P < 0.05) variations, respectively, in the ß diversity of the microbial community, respectively. The impact of warming was significant on NH4+-N-related microorganisms; however, this impact was weakened by nitrogen application for microbes in the rhizosphere. This study demonstrated that enhanced nitrogen fertilizer can alleviate the adverse impact of warming by weakening its effects on rhizosphere microbes, improving soil fertility, promoting rice nitrogen uptake, and increasing the aboveground biomass and harvest index. These findings provide an important theoretical basis for developing practical, responsive cultivation strategies.

Multiple regulators were involved in glutelin synthesis and subunit accumulation in response to temperature and nitrogen during rice grain-filling stage.

Rice glutelin is sensitive to temperature and nitrogen, however, the regulatory mechanism of glutelin response to temperature and nitrogen is unclear. In this study, we conducted the open field warming experiment by the Free-air temperature enhancement facility and application of nitrogen during grain filling. In three-year field warming experiments, glutelin relative content was significantly increased under elevated temperature and application of nitrogen. Temperature and nitrogen and their interaction increased the glutelin accumulation rate in the early and middle grain filling stages (10-25d after flowering), but decreased the glutelin accumulation rate in the middle and late grain filling stages (25-45d after flowering). Elevated temperature promoted pro-glutelin levels whereas application of nitrogen under warming increased the amount of α-glutelin. At the transcriptional level, the expression levels of the glutelin-encoding genes and protein disulphide isomerase-like enzyme (PDIL1-1), glutelin precursor accumulation 4 (GPA4), glutelin precursor mutant 6 (GPA2), glutelin precursor accumulation 3 (GPA3) and vacuolar processing enzyme (OsVPE1) of glutelin folding, transport and accumulation-related genes were up-regulated by nitrogen under natural temperature as early as 5d after flowering. However, elevated temperature up-regulated glutelin-encoding genes before 20d after flowering, and the expression of endoplasmic reticulum chaperone (OsBip1), OsPDIL1-1, small GTPase gene (GPA1), GPA2-GPA4 and OsVPE1 were significantly increased post 20d after flowering under warming. In addition, the increase in glutelin content worsened grain quality, particularly chalkiness and eating quality. Overall, the results were helpful to understand glutelin accumulation and provide a theoretical basis for further study the relationship between rice quality and glutelin under global warming.

High drug loading hydrophobic cross-linked dextran microspheres as novel drug delivery systems for the treatment of osteoarthritis.

Drug delivery via intra-articular (IA) injection has proved to be effective in osteoarthritis (OA) therapy, limited by the drug efficiency and short retention time of the drug delivery systems (DDSs). Herein, a series of modified cross-linked dextran (Sephadex, S0) was fabricated by respectively grafting with linear alkyl chains, branched alkyl chains or aromatic chain, and acted as DDSs after ibuprofen (Ibu) loading for OA therapy. This DDSs expressed sustained drug release, excellent anti-inflammatory and chondroprotective effects both in IL-1ß induced chondrocytes and OA joints. Specifically, the introduction of a longer hydrophobic chain, particularly an aromatic chain, distinctly improved the hydrophobicity of S0, increased Ibu loading efficiency, and further led to significantly improving OA therapeutic effects. Therefore, hydrophobic microspheres with greatly improved drug loading ratio and prolonged degradation rates show great potential to act as DDSs for OA therapy.

PINOID and PIN-FORMED Paralogous Genes Are Required for Leaf Morphogenesis in Rice.

Auxin plays an essential role in modulating leaf development. However, its role in leaf development in rice (Oryza sativa L.) remains largely unknown. In this study, we found that PINOID (OsPID) and two Sister-of-PIN1s, termed PIN-FORMED1c (OsPIN1c) and OsPIN1d, are necessary for rice leaf development. The ospin1c ospin1d null mutant lines presented severe defects in leaf morphogenesis, including drooping and semi-drooping blades, an abnormally thickened sheath and lamina joint, and fused leaves with absent ligules and auricles. Loss-of-function ospid mutants displayed generally similar leaf morphology but lacked leaf fusion. Interestingly, misshaped leaf genesis displayed a preference for being ipsilateral. In addition, OsPIN1c and OsPID were commonly localized in the initiating leaf primordia. Furthermore, accompanied by the more severe organ morphogenesis in the ospin1c ospin1d ospid triple mutant, RNA sequencing analysis revealed that many genes essential for leaf development have an altered expression level. Together, this study furthers our understanding of the role auxin transport plays during leaf development in monocot rice.

Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach.

The green fraction (GF), which is the fraction of green vegetation in a given viewing direction, is closely related to the light interception ability of the crop canopy. Monitoring the dynamics of GF is therefore of great interest for breeders to identify genotypes with high radiation use efficiency. The accuracy of GF estimation depends heavily on the quality of the segmentation dataset and the accuracy of the image segmentation method. To enhance segmentation accuracy while reducing annotation costs, we developed a self-supervised strategy for deep learning semantic segmentation of rice and wheat field images with very contrasting field backgrounds. First, the Digital Plant Phenotyping Platform was used to generate large, perfectly labeled simulated field images for wheat and rice crops, considering diverse canopy structures and a wide range of environmental conditions (sim dataset). We then used the domain adaptation model cycle-consistent generative adversarial network (CycleGAN) to bridge the reality gap between the simulated and real images (real dataset), producing simulation-to-reality images (sim2real dataset). Finally, 3 different semantic segmentation models (U-Net, DeepLabV3+, and SegFormer) were trained using 3 datasets (real, sim, and sim2real datasets). The performance of the 9 training strategies was assessed using real images captured from various sites. The results showed that SegFormer trained using the sim2real dataset achieved the best segmentation performance for both rice and wheat crops (rice: Accuracy = 0.940, F1-score = 0.937; wheat: Accuracy = 0.952, F1-score = 0.935). Likewise, favorable GF estimation results were obtained using the above strategy (rice: R2 = 0.967, RMSE = 0.048; wheat: R2 = 0.984, RMSE = 0.028). Compared with SegFormer trained using a real dataset, the optimal strategy demonstrated greater superiority for wheat images than for rice images. This discrepancy can be partially attributed to the differences in the backgrounds of the rice and wheat fields. The uncertainty analysis indicated that our strategy could be disrupted by the inhomogeneity of pixel brightness and the presence of senescent elements in the images. In summary, our self-supervised strategy addresses the issues of high cost and uncertain annotation accuracy during dataset creation, ultimately enhancing GF estimation accuracy for rice and wheat field images. The best weights we trained in wheat and rice are available: https://github.com/PheniX-Lab/sim2real-seg.

Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking.

Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation. In recent studies, phenotyping of rice panicles during the heading-flowering stage still lacks comprehensive analysis, especially of panicle development under different nitrogen treatments. In this work, we proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5, ResNet50, and DeepSORT models. Combined with field observation data, the proposed method was used to test whether it has an ability to identify subtle differences in panicle developments under different nitrogen treatments. The result shows that panicle counting throughout the heading-flowering stage achieved high accuracy (R2 = 0.96 and RMSE = 1.73), and heading date was estimated with an absolute error of 0.25 days. In addition, by identical panicle tracking based on the time-series images, we analyzed detailed flowering phenotypic changes of a single panicle, such as flowering duration and individual panicle flowering time. For rice population, with an increase in the nitrogen application: panicle number increased, heading date changed little, but the duration was slightly extended; cumulative flowering panicle number increased, rice flowering initiation date arrived earlier while the ending date was later; thus, the flowering duration became longer. For a single panicle, identical panicle tracking revealed that higher nitrogen application led to earlier flowering initiation date, significantly longer flowering days, and significantly longer total duration from vigorous flowering beginning to the end (total DBE). However, the vigorous flowering beginning time showed no significant differences and there was a slight decrease in daily DBE.

The SMC5/6 complex subunit MMS21 regulates stem cell proliferation in rice.

KEY MESSAGE: SMC5/6 complex subunit OsMMS21 is involved in cell cycle and hormone signaling and required for stem cell proliferation during shoot and root development in rice. The structural maintenance of chromosome (SMC)5/6 complex is required for nucleolar integrity and DNA metabolism. Moreover, METHYL METHANESULFONATE SENSITIVITY GENE 21 (MMS21), a SUMO E3 ligase that is part of the SMC5/6 complex, is essential for the root stem cell niche and cell cycle transition in Arabidopsis. However, its specific role in rice remains unclear. Here, OsSMC5 and OsSMC6 single heterozygous mutants were generated using CRISPR/Cas9 technology to elucidate the function of SMC5/6 subunits, including OsSMC5, OsSMC6, and OsMMS21, in cell proliferation in rice. ossmc5/ + and ossmc6/ + heterozygous single mutants did not yield homozygous mutants in their progeny, indicating that OsSMC5 and OsSMC6 both play necessary roles during embryo formation. Loss of OsMMS21 caused severe defects in both the shoot and roots in rice. Transcriptome analysis showed a significant decrease in the expression of genes involved in auxin signaling in the roots of osmms21 mutants. Moreover, the expression levels of the cycB2-1 and MCM genes, which are involved the cell cycle, were significantly lower in the shoots of the mutants, indicating that OsMMS21 was involved in both hormone signaling pathways and the cell cycle. Overall, these findings indicate that the SUMO E3 ligase OsMMS21 is required for both shoot and root stem cell niches, improving the understanding of the function of the SMC5/6 complex in rice.

Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage.

The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages (150,000 images with over 2 million labels). The realism of the images was then improved using domain adaptation methods before training deep learning models. The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset, collecting measurements from 5 countries obtained under different environments, growth stages, and lighting conditions with different cameras (450 images with over 2,162 labels). Among the 6 combinations of deep learning models and domain adaptation techniques, the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance (R2 = 0.94, root mean square error = 8.7). Complementary studies show that it is essential to simulate images with sufficient realism (background, leaf texture, and lighting conditions) before applying domain adaptation techniques. Furthermore, the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips. The method is claimed to be self-supervised since no manual labeling is required for model training. The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems. The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.