The majority of organs in plants are not established until after germination, when pluripotent stem cells in the growing apices give rise to daughter cells that proliferate and subsequently differentiate into new tissues and organ primordia. This remarkable capacity is not only restricted to the meristem, since maturing cells in many organs can also rapidly alter their identity depending on the cues they receive. One general feature of plant cell differentiation is a change in cell wall composition at the cell surface. Historically, this has been viewed as a downstream response to primary cues controlling differentiation, but a closer inspection of the wall suggests that it may play a much more active role. Specific polymers within the wall can act as substrates for modifications that impact receptor binding, signal mobility, and cell flexibility. Therefore, far from being a static barrier, the cell wall and its constituent polysaccharides can dictate signal transmission and perception, and directly contribute to a cell’s capacity to differentiate. In this review, we re-visit the role of plant cell wall-related genes and polysaccharides during various stages of development, with a particular focus on how changes in cell wall machinery accompany the exit of cells from the stem cell niche.

SUMMARY: The barleycellulose synthase‐like F(CslF) genes encode putative cell wall polysaccharide synthases. They are related to thecellulose synthase(CesA) genes involved in cellulose biosynthesis, and theCslDgenes that influence root hair development. AlthoughCslDgenes are implicated in callose, mannan and cellulose biosynthesis, and are found in both monocots and eudicots,CslFgenes are specific to the Poaceae. Recently the barleyCslF3(HvCslF3) gene was shown to be involved in the synthesis of a novel (1,4)‐β‐linked glucoxylan, but it remains unclear whether this gene contributes to plant growth and development. Here, expression profiling using qRT‐PCR and mRNAin situhybridization revealed thatHvCslF3accumulates in the root elongation zone. SilencingHvCslF3by RNAi was accompanied by slower root growth, linked with a shorter elongation zone and a significant reduction in root system size. Polymer profiling of theRNAilines revealed a significant reduction in (1,4)‐β‐linked glucoxylan levels. Remarkably, the heterologous expression ofHvCslF3in wild‐type (Col‐0) and root hair‐deficient Arabidopsis mutants (csld3andcsld5) complemented thecsld5mutant phenotype, in addition to altering epidermal cell fate. Our results reveal a key role forHvCslF3during barley root development and suggest that members of theCslDandCslFgene families have similar functions during root growth regulation.

Summary: Barley (Hordeum vulgare) is an important global cereal crop and a model in genetic studies. Despite advances in characterising barley genomic resources, few mutant studies have identified genes controlling root architecture and anatomy, which plays a critical role in capturing soil resources. Our phenotypic screening of a TILLING mutant collection identified line TM5992 exhibiting a short‐root phenotype compared with wild‐type (WT) Morex background. Outcrossing TM5992 with barley variety Proctor and subsequent SNP array‐based bulk segregant analysis, fine mapped the mutation to a cM scale. Exome sequencing pinpointed a mutation in the candidate gene HvPIN1a, further confirming this by analysing independent mutant alleles. Detailed analysis of root growth and anatomy in Hvpin1a mutant alleles exhibited a slower growth rate, shorter apical meristem and striking vascular patterning defects compared to WT. Expression and mutant analyses of PIN1 members in the closely related cereal brachypodium (Brachypodium distachyon) revealed that BdPIN1a and BdPIN1b were redundantly expressed in root vascular tissues but only Bdpin1a mutant allele displayed root vascular defects similar to Hvpin1a. We conclude that barley PIN1 genes have sub‐functionalised in cereals, compared to Arabidopsis (Arabidopsis thaliana), where PIN1a sequences control root vascular patterning.

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex- view perspective to synthesise remote sensing–based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): Plant-pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): The diverse disease detection tasks and their corresponding research objectives. (3) Sensor (S): The sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): The environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot and regional scales. (5) Algorithm (A): The classical and state-of-the-art data analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): The data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production.

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease-monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, the measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): plant–pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): the diverse disease-detection tasks and their corresponding research objectives. (3) Sensor (S): the sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): the environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot, and regional scales. (5) Algorithm (A): the classical and state-of-the-art data-analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): the data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease-detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production.

The architecture and structure of cereal spikes are key indicators of quality and yield and are determined by both genetic and environmental factors. Traditional phenotyping methods for cereal spike trait measurements rely largely on manual work and are often qualitative or destructive, which leads to loss of spatial information. In this study, by using the newly installed X-ray computed tomography (X-ray CT) system at the Plant Accelerator (Australian Plant Phenomics Facility), we developed a high-throughput and non-destructive method to analyse cereal spikes. The fully automated algorithm generates detailed grain traits measurement, including count, weight, size, surface area, sphericity and spatial position. The average scan time required per sample cassette is under 7 minutes for 30 cereal spikes. Various cultivars of wheat, barley, oat and sorghum have been tested, and the results have confirmed the high accuracy and efficiency of using the X-ray CT system in grain studies. To further utilise the obtained 3D information, a pipeline for analyse spike and spikelet morphological traits has been developed for wheat spikes and oat panicles. This method allows to study the spikelet morphological traits by categorising the spikelets base on grain count, volume and weight, which can potentially bring the insights of the relationship between the development of single or multiflorous spikelets and grain quality and yield. The innovative X-ray CT system can efficiently generate grain and spike traits in high-throughput and non-destructive way, which provides a new method to contribute to a better understanding of cereal screening and breeding directions.