iPOTs: Internet of Things-based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress

Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice

BACKGROUND AND AIMS

Root system architecture (RSA) plays a key role in plant adaptation to drought because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, as early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional (3D) image analysis.

METHODS

We used 109 F12 recombinant inbred lines (RILs) obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25 cm height) by stopping irrigation 14 days after sowing (DAS). Time-series RSA at 14, 21, and 28 DAS was visualized by X-ray computed tomography, and subsequently compared between drought and well-watered conditions. Following this analysis, we further investigated drought-avoidant RSA by testing 20 randomly selected RILs under drought conditions.

KEY RESULTS

We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth, which positively impacted shoot growth, ranged between 1.7-3.4 cm under drought conditions and between 0.0-1.7 cm under well-watered conditions. Drought-avoidant RILs had a higher root density in the lower layers of the topsoil compared to the others.

CONCLUSIONS

Fine classification of soil layers using 3D image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice.

Life-Cycle Multiomics of Rice Shoots Reveals Growth Stage-Specific Effects of Drought Stress and Time-Lag Drought Responses

Field-grown rice plants are exposed to various stresses at different stages of their life cycle, but little is known about the effects of stage-specific stresses on phenomes and transcriptomes. In this study, we performed integrated time-course multiomics on rice at 3-d intervals from seedling to heading stage under six drought conditions in a well-controlled growth chamber. Drought stress at seedling and reproductive stages reduced yield performance by reducing seed number and setting rate, respectively. High temporal resolution analysis revealed that drought response occurred in two steps: a rapid response via the abscisic acid (ABA) signaling pathway and a slightly delayed DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN (DREB) pathway, allowing plants to respond flexibly to deteriorating soil water conditions. Our long-term time-course multiomics showed that temporary drought stress delayed flowering due to prolonged expression of the flowering repressor gene GRAIN NUMBER, PLANT HEIGHT AND HEADING DATE 7 (Ghd7) and delayed expression of the florigen genes HEADING DATE 3a (Hd3a) and RICE FLOWERING LOCUS T 1 (RFT1). Our life-cycle multiomics dataset on rice shoots under drought conditions provides a valuable resource for further functional genomic studies to improve crop resilience to drought stress.

Rice immediately adapts the dynamics of photosynthates translocation to roots in response to changes in soil water environment

Rice is susceptible to abiotic stresses such as drought stress. To enhance drought resistance, elucidating the mechanisms by which rice plants adapt to intermittent drought stress that may occur in the field is an important requirement. Roots are directly exposed to changes in the soil water condition, and their responses to these environmental changes are driven by photosynthates. To visualize the distribution of photosynthates in the root system of rice plants under drought stress and recovery from drought stress, we combined X-ray computed tomography (CT) with open type positron emission tomography (OpenPET) and positron-emitting tracer imaging system (PETIS) with ¹¹C tracer. The short half-life of ¹¹C (20.39 min) allowed us to perform multiple experiments using the same plant, and thus photosynthate translocation was visualized as the same plant was subjected to drought stress and then re-irrigation for recovery. The results revealed that when soil is drier, ¹¹C-photosynthates mainly translocated to the seminal roots, likely to promote elongation of the root with the aim of accessing water stored in the lower soil layers. The photosynthates translocation to seminal roots immediately stopped after rewatering then increased significantly in crown roots. We suggest that when rice plant experiencing drought is re-irrigated from the bottom of pot, the destination of ¹¹C-photosynthates translocation immediately switches from seminal root to crown roots. We reveal that rice roots are responsive to changes in soil water conditions and that rice plants differentially adapts the dynamics of photosynthates translocation to crown roots and seminal roots depending on soil conditions.

Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis by backward prediction

BACKGROUND

Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images.

RESULTS

We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%.

CONCLUSIONS

We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.

Contribution of Internet of things in water supply chain management: A bibliometric and content analysis

Purpose

This paper aims to identify the role of internet of things (IoT) in water supply chain management and helps to understand its future path from the junction of computer science and resource management.

Design/methodology/approach

The current research was studied through bibliometric review and content analysis, and various contributors and linkages were found. Also, the possible directions and implications of the field were analyzed.

Findings

The paper’s key findings include the role of modern computer science in water resource management through sensor technology, big data analytics, IoT, machine learning and cloud computing. This, in turn, helps in understanding future implications of IoT resource management.

Research limitations/implications

A more extensive database can add up to more combinations of linkages and ideas about the future direction. The implications and understanding gained by the research can be used by governments and firms dealing with water management of smart cities. It can also help find ways for optimizing water resources using IoT and modern-day computer science.

Originality/value

This study is one of the very few investigations that highlighted IoT’s role in water supply management. Thus, this study helps to assess the scope and the trend of the case area.

Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance

Plant response to drought stress includes systems for intracellular regulation of gene expression and signaling, as well as inter-tissue and inter-organ signaling, which helps entire plants acquire stress resistance. Plants sense water-deficit conditions both via the stomata of leaves and roots, and transfer water-deficit signals from roots to shoots via inter-organ signaling. Abscisic acid is an important phytohormone involved in the drought stress response and adaptation, and is synthesized mainly in vascular tissues and guard cells of leaves. In leaves, stress-induced abscisic acid is distributed to various tissues by transporters, which activates stomatal closure and expression of stress-related genes to acquire drought stress resistance. Moreover, the stepwise stress response at the whole-plant level is important for proper understanding of the physiological response to drought conditions. Drought stress is sensed by multiple types of sensors as molecular patterns of abiotic stress signals, which are transmitted via separate parallel signaling networks to induce downstream responses, including stomatal closure and synthesis of stress-related proteins and metabolites. Peptide molecules play important roles in the inter-organ signaling of dehydration from roots to shoots, as well as signaling of osmotic changes and reactive oxygen species/Ca²⁺ . In this review, we have summarized recent advances in research on complex plant drought stress responses, focusing on inter-tissue signaling in leaves and inter-organ signaling from roots to shoots. We have discussed the mechanisms via which drought stress adaptations and resistance are acquired at the whole-plant level, and have proposed the importance of quantitative phenotyping for measuring plant growth under drought conditions.