PI-Plat: a high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Development of Single-Segment Substitution Lines and Fine-Mapping of qSPP4 for Spikelets Per Panicle and qGW9 for Grain Width Based on Rice Dual-Segment Substitution Line Z783

Single segment substitution line (SSSL) libraries are an ideal platform for breeding by design. To develop SSSLs-Xihui18 covering the whole genome, a novel rice chromosome segment substitution line (CSSL), Z783, carrying two substitution segments (average length of 6.55 Mb) on Chr.4 and Chr.9 was identified, which was a gap in the library previously. Z783 was developed from the progeny of recipient "Xihui18" (an indica restorer line) and donor "Huhan3" (a japonica cultivar) by advanced backcross combined molecular marker-assisted selection (MAS). It displayed multiple panicles and less spikelets and wide grains. Then, a F2 population derived from Xihui18/Z783 was used to map quantitative trait loci (QTLs) for yield-related traits by the mixed linear model method. Nine QTLs were detected (p < 0.05). Furthermore, three SSSLs were constructed by MAS, and all 9 QTLs could be validated, and 15 novel QTLs could be detected by these SSSLs by a one-way ANOVA analysis. The genetic analysis showed that qSSP4 for less spikelets and qGW9 for wide grain all displayed dominant gene action in their SSSLs. Finally, qSSP4 and qGW9 were fine-mapped to intervals of 2.75 Mb and 1.84 Mb, on Chromosomes 4 and 9, respectively. The results lay a solid foundation for their map cloning and molecular breeding by design.

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. However, this method requires a large number of data sets and is not robust to automatic segmentation of highly adherent organ components; thus, it should be combined with point cloud processing technology.

RESULTS

An innovative method of incomplete annotation of point cloud data was proposed for easy development of the dataset of maize tassels,and an automatic maize tassel phenotype analysis system: MaizeTasselSeg was developed. The tip feature of point cloud is trained and learned based on PointNet +  + network, and the tip point cloud of tassel branch was automatically segmented. Complete branch segmentation was realized based on the shortest path algorithm. The Intersection over Union (IoU), precision, and recall of the segmentation results were 96.29, 96.36, and 93.01, respectively. Six phenotypic traits related to morphological structure (branch count, branch length, branch angle, branch curvature, tassel volume, and dispersion) were automatically extracted from the segmentation point cloud. The squared correlation coefficients (R²) for branch length, branch angle, and branch count were 0.9897, 0.9317, and 0.9587, respectively. The root mean squared error (RMSE) for branch length, branch angle, and branch count were 0.529 cm, 4.516, and 0.875, respectively.

CONCLUSION

The proposed method provides an efficient scheme for high-throughput organ segmentation of maize tassels and can be used for the automatic extraction of phenotypic traits of maize tassel. In addition, the incomplete annotation approach provides a new idea for morphology-based plant segmentation.

A non-destructive approach for measuring rice panicle-level photosynthetic responses using 3D-image reconstruction

BACKGROUND

Our understanding of the physiological responses of rice inflorescence (panicle) to environmental stresses is limited by the challenge of accurately determining panicle photosynthetic parameters and their impact on grain yield. This is primarily due to the lack of a suitable gas exchange methodology for panicles and non-destructive methods to accurately determine panicle surface area.

RESULTS

To address these challenges, we have developed a custom panicle gas exchange cylinder compatible with the LiCor 6800 Infra-red Gas Analyzer. Accurate surface area measurements were determined using 3D panicle imaging to normalize the panicle-level photosynthetic measurements. We observed differential responses in both panicle and flag leaf for two temperate Japonica rice genotypes (accessions TEJ-1 and TEJ-2) exposed to heat stress during early grain filling. There was a notable divergence in the relative photosynthetic contribution of flag leaf and panicles for the heat-tolerant genotype (TEJ-2) compared to the sensitive genotype (TEJ-1).

CONCLUSION

The novelty of this method is the non-destructive and accurate determination of panicle area and photosynthetic parameters, enabling researchers to monitor temporal changes in panicle physiology during the reproductive development. The method is useful for panicle-level measurements under diverse environmental stresses and is sensitive enough to evaluate genotypic variation for panicle physiology and architecture in cereals with compact inflorescences.

An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation

High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed 'TPanicle-RCNN,' and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.

Endoplasmic reticulum stress pathway mediates the early heat stress response of developing rice seeds

A transient heat stress occurring during early seed development in rice (Oryza sativa) reduces seed size by altering endosperm development. However, the relationship between the timing of the stress and specific developmental stage on heat sensitivity is not well-understood. To address this, we imposed a series of non-overlapping heat stress treatments and found that young seeds are most sensitive during the first two days after flowering. Temporal transcriptome analysis of developing, heat stressed (35°C) seeds during this window shows that Inositol-requiring enzyme 1 (IRE1)-mediated endoplasmic reticulum (ER) stress response and jasmonic acid (JA) pathways are the early (1-3 h) drivers of heat stress response. We propose that increased JA levels under heat stress may precede ER stress response as JA application promotes the spliced form of OsbZIP50, an ER response marker gene linked to IRE1-specific pathway. This study presents temporal and mechanistic insights into the role of JA and ER stress signalling during early heat stress response of rice seeds that impact both grain size and quality. Modulating the heat sensitivity of the early sensing pathways and downstream endosperm development genes can enhance rice resilience to transient heat stress events.

Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress

Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits and processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: (i) time of day of flowering, to escape these stresses during flowering; (ii) optimizing photosynthetic efficiency; (iii) storage and translocation of water-soluble carbohydrates; and (iv) yield and yield components to provide in-season yield estimates. Moreover, we provide an overview of current advances in remote sensing in capturing these traits, and discuss the limitations with existing technology as well as future direction of research to develop high-throughput phenotyping approaches. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.

High night temperature effects on wheat and rice: Current status and way forward

Rapid increases in minimum night temperature than in maximum day temperature is predicted to continue, posing significant challenges to crop productivity. Rice and wheat are two major staples that are sensitive to high night-temperature (HNT) stress. This review aims to (i) systematically compare the grain yield responses of rice and wheat exposed to HNT stress across scales, and (ii) understand the physiological and biochemical responses that affect grain yield and quality. To achieve this, we combined a synthesis of current literature on HNT effects on rice and wheat with information from a series of independent experiments we conducted across scales, using a common set of genetic materials to avoid confounding our findings with differences in genetic background. In addition, we explored HNT-induced alterations in physiological mechanisms including carbon balance, source-sink metabolite changes and reactive oxygen species. Impacts of HNT on grain developmental dynamics focused on grain-filling duration, post-flowering senescence, changes in grain starch and protein composition, starch metabolism enzymes and chalk formation in rice grains are summarized. Finally, we highlight the need for high-throughput field-based phenotyping facilities for improved assessment of large-diversity panels and mapping populations to aid breeding for increased resilience to HNT in crops.

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era.

Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants

The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.

Allelic variation in rice Fertilization Independent Endosperm 1 contributes to grain width under high night temperature stress

A higher minimum (night-time) temperature is considered a greater limiting factor for reduced rice yield than a similar increase in maximum (daytime) temperature. While the physiological impact of high night temperature (HNT) has been studied, the genetic and molecular basis of HNT stress response remains unexplored. We examined the phenotypic variation for mature grain size (length and width) in a diverse set of rice accessions under HNT stress. Genome-wide association analysis identified several HNT-specific loci regulating grain size as well as loci that are common for optimal and HNT stress conditions. A novel locus contributing to grain width under HNT conditions colocalized with Fie1, a component of the FIS-PRC2 complex. Our results suggest that the allelic difference controlling grain width under HNT is a result of differential transcript-level response of Fie1 in grains developing under HNT stress. We present evidence to support the role of Fie1 in grain size regulation by testing overexpression (OE) and knockout mutants under heat stress. The OE mutants were either unaltered or had a positive impact on mature grain size under HNT, while the knockouts exhibited significant grain size reduction under these conditions.

MADS78 and MADS79 Are Essential Regulators of Early Seed Development in Rice

MADS box transcription factors (TFs) are subdivided into type I and II based on phylogenetic analysis. The type II TFs regulate floral organ identity and flowering time, but type I TFs are relatively less characterized. Here, we report the functional characterization of two type I MADS box TFs in rice (Oryza sativa), MADS78 and MADS79 Transcript abundance of both these genes in developing seed peaked at 48 h after fertilization and was suppressed by 96 h after fertilization, corresponding to syncytial and cellularized stages of endosperm development, respectively. Seeds overexpressing MADS78 and MADS 79 exhibited delayed endosperm cellularization, while CRISPR-Cas9-mediated single knockout mutants showed precocious endosperm cellularization. MADS78 and MADS 79 were indispensable for seed development, as a double knockout mutant failed to make viable seeds. Both MADS78 and 79 interacted with MADS89, another type I MADS box, which enhances nuclear localization. The expression analysis of Fie1, a rice FERTILIZATION-INDEPENDENT SEED-POLYCOMB REPRESSOR COMPLEX2 component, in MADS78 and 79 mutants and vice versa established an antithetical relation, suggesting that Fie1 could be involved in negative regulation of MADS78 and MADS 79 Misregulation of MADS78 and MADS 79 perturbed auxin homeostasis and carbon metabolism, as evident by misregulation of genes involved in auxin transport and signaling as well as starch biosynthesis genes causing structural abnormalities in starch granules at maturity. Collectively, we show that MADS78 and MADS 79 are essential regulators of early seed developmental transition and impact both seed size and quality in rice.

Divergent phenotypic response of rice accessions to transient heat stress during early seed development

Increasing global surface temperatures is posing a major food security challenge. Part of the solution to address this problem is to improve crop heat resilience, especially during grain development, along with agronomic decisions such as shift in planting time and increasing crop diversification. Rice is a major food crop consumed by more than 3 billion people. For rice, thermal sensitivity of reproductive development and grain filling is well-documented, while knowledge concerning the impact of heat stress (HS) on early seed development is limited. Here, we aim to study the phenotypic variation in a set of diverse rice accessions for elucidating the HS response during early seed development. To explore the variation in HS sensitivity, we investigated aus (1), indica (2), temperate japonica (2), and tropical japonica (4) accessions for their HS (39/35°C) response during early seed development that accounts for transition of endosperm from syncytial to cellularization, which broadly corresponds to 24 and 96 hr after fertilization (HAF), respectively, in rice. The two indica and one of the tropical japonica accessions exhibited severe heat sensitivity with increased seed abortion; three tropical japonicas and an aus accession showed moderate heat tolerance, while temperate japonicas exhibited strong heat tolerance. The accessions exhibiting extreme heat sensitivity maintain seed size at the expense of number of fully developed mature seeds, while the accessions showing relative resilience to the transient HS maintained number of fully developed seeds but compromised on seed size, especially seed length. Further, histochemical analysis revealed that all the tested accessions have delayed endosperm cellularization upon exposure to the transient HS by 96 HAF; however, the rate of cellularization was different among the accessions. These findings were further corroborated by upregulation of cellularization-associated marker genes in the developing seeds from the heat-stressed samples.