High-throughput phenotyping of pea physiological traits for drought stress tolerance

info:eu-repo/semantics/publishedVersio

The Phenotyping Dilemma—The Challenges of a Diversified Phenotyping Community

In the past decade, large investments have been made for plant phenotyping in terms of funding, research hours, and high-tech installations in Europe, Australia, North America and Asia. The number of actors in phenotyping has increased rapidly and the focus has gradually shifted from basic to strategic crop research linked to classic agricultural traits. During the recent years, community-wide surveys have pinpointed focus areas, challenges, and bottlenecks in plant phenotyping. Increasing efforts addressing abiotic and biotic stresses associated with the effects of global climate change in mind are developing. Crop wild relatives (CWRs) are important sources for genes for both biotic and abiotic stress tolerance since diversity lost during domestication is vast. Within the last decade, large-scale phenotyping research platforms have been set up and are organized within national phenotyping facilities with a range of high-tech applications in climate rooms, greenhouses and in the field

High-throughput plant phenotyping: a role for metabolomics?

High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype–phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.info:eu-repo/semantics/publishedVersio

Pitfalls and potential of high-throughput plant phenotyping platforms

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved

Field robots for plant phenotyping

The technology developed within the domain of plant phenotyping provides excellent data-driven tools for plant breeders to use in the collection and description of the relationship between genotyping and environmental factors. Field robots are emerging as sensor-mounted phenotyping platforms, which offer the advantages of flexible operation, advanced automation and high duration. They are capable of acquiring phenotypic information in a non-destructive, high-throughput and efficient manner. The acquisition of high-throughput information is particularly challenging with traditional manual methods of measurement. Multiple types of automated operating platforms are being developed within this field, including unmanned aerial vehicles (UAV), field robots, unmanned ground vehicles (UGV), devices linked through the Internet of Things (IoT) and satellites. Various plant-phenotyping platforms are in use across the industry and academia. We provide an overview of current research on different morphologies of plant-phenotyping robots and state-of-art sensors and technologies for automatic plant phenotyping. We summarize the advantages of and problems associated with this research field and present prospects for future development. Platforms are developed in many variations, with some being capable of carrying high loads and allowing simultaneous measurements with multiple sensors. Some robots are characterized by a flexible chassis, and others focus on long operating times. Moreover, the efficiency and accuracy of advanced field robots using artificial intelligence (AI) technologies are continually improving. This will eventually provide a solid foundation for the further development of the domain of plant phenotyping, which is increasingly focussing on open-field applications

Recent developments and potential of robotics in plant eco-phenotyping

Automated acquisition of plant eco-phenotypic information can serve as a decisionmaking basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide steppingstones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture

Virtual reality-based digital twins for greenhouses: A focus on human interaction

The agricultural domain is experiencing an increasing use of digital twin technologies in greenhouse horticulture for the betterment of monitoring production. Research in this domain has started leading towards more advanced ways of visually interacting with these digital twins, and this could be by means of immersive technologies (such as virtual reality) to better allow farmers to feel a sense of presence when exploring these digital copies. Yet there are many remaining challenges for the technology’s integration and more studies are needed into user interaction; specifically regarding simulation sickness to cater for comfort during prolonged and regular use. Thus, this article documents the survey of 30 participants by means of the Simulator Sickness Questionnaire (before and after interaction) when using an immersive digital greenhouse twin environment. Findings indicate that users who experience simulation sickness tend to provide a lower evaluation score of the digital twin and their prior experience of gaming (on varied devices) affects the overall evaluation of the digital environment. Further, the level of playability and realism (referred to as convenience), statistically affects the end users’ level of sickness. For tracking crop growth by means of digital greenhouse twins, where prolonged user interaction may take place in a virtual environment, these are notable considerations for digital twin developers

Early locomotion and the development of spatial language: evidence from young children with motor impairments

International audienc

Cost-effective imaging in plant phenotyping from information-based approaches

Cost-effective imaging in plant phenotyping from information-based approaches&nbsp;</p

Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping

In plant phenotyping, there is a demand for high-throughput, non-destructive systems that can accurately analyse various plant traits by measuring features such as plant volume, leaf area, and stem length. Existing vision-based systems either focus on speed using 2D imaging, which is consequently inaccurate, or on accuracy using time-consuming 3D methods. In this paper, we present a computer-vision system for seedling phenotyping that combines best of both approaches by utilizing a fast three-dimensional (3D) reconstruction method. We developed image processing methods for the identification and segmentation of plant organs (stem and leaf) from the 3D plant model. Various measurements of plant features such as plant volume, leaf area, and stem length are estimated based on these plant segments. We evaluate the accuracy of our system by comparing the measurements of our methods with ground truth measurements obtained destructively by hand. The results indicate that the proposed system is very promising