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With this Special Issue we invite contribution that focus on the use of phenotyping approaches for the quantification of physiological traits under biotic and abiotic stress of crops under controlled and field conditions with the goal to understand basic plant environment interaction and translate this understanding into application in breeding or agronomy scenarios, as one of the key objectives on the way towards sustainable agriculture.

Submission deadline: 31 July 2023

Guest Editors

Forschungszentrum Jülich GmbH
Institut für Bio- und Geowissenschaften (IBG)/ Pflanzenwissenschaften (IBG-2)

Prof. Dr. Ulrich Schurr
Prof. Dr. Uwe Rascher
Dr. Sven Fahrner
Dr. Roland Pieruschka

Useful Links
Call-for-paper
Submission website
Guide for authors

More information

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To that end, EMPHASIS-GO will (1) develop and deploy services, (2) implement the legal, financial and organisational framework and (3) consolidate the business plan for the Operational Phase.

EMPHASIS-GO brings together 13 leading plant phenotyping institutions from eleven countries. It is a 36 months project funded with ~ 1.3 million euros by the European Union (Grant agreement ID: 101079772)

In PHENET, the European Research Infrastructures (RI) on plant phenotyping (EMPHASIS), ecosystems experimentation (AnaEE), long-term observation (eLTER) and data management and bioinformatics (ELIXIR) will join their forces to co-develop, with a diversity of innovative companies, new tools and methods – meant to contribute to new RI services – for the identification of future-proofed combinations of species, genotypes and management practices in front of the most likely climatic scenarios across Europe.

Ambitioning to go beyond current highly instrumented but often spatially and temporally limited RI installations, PHENET derived services will allow wide access to enlarged sources of in-situ phenotypic and environmental data thanks to (i) new AI-based multi (agroecology-related) traits multi-sensors devices (ii) to unleashed access to high resolution Earth Observation data connected to ground based data, (iii) FAIR data support for connection with (iv) new generation of predictive modelling solutions encompassing AI and digital twins. Developments will be challenged by and implemented in a series of eight Use Cases covering a large range of agroecosystems but also of ecosystems to demonstrate portability of solutions. Several of these Use Cases will mobilize on-farm data.

A large effort will be devoted to training RI staff and beyond through a sustained collection of training material fed by experts. Outreaching activities will aim at enlarging the range of RI users. PHENET will not only strengthen RI but will also have major impact on the development of innovative companies on phenotyping, envirotyping and precision agriculture as well as on the emergence of climate smart crop varieties and innovative practices fitted to climate change and agroecological transition.

This new title also details the use of plant phenotyping to analyse traits such as crop root functionality, yield performance and disease resistance.

Find out more about the new title here.

*Special Offer*

Receive 20% off your order of the book using code EMP20 via the BDS Website. Please note that this discount code expires 31^st July 2022.

Furthermore, even though modern AI-based methods typically generalize well to unseen data, no standard exists to enable sharing and fine-tuning of pre-trained models between different analysis tools. Existing user-facing platforms operate entirely independently from each other, often failing to comply with FAIR data and Open Science standards. The field of AI and ML is developing at a staggering pace, making it impossible for the non-specialist to stay up to date.

To enable the life science communities to benefit from AI/ML-powered image analysis methods, AI4LIFE will build bridges, providing urgently needed services on the common European research infrastructures. The project builds an open, accessible, community-driven repository of FAIR pre-trained AI models and develops services to deliver these models to life scientists, including those without substantial computational expertise. Direct support and ample training activities will prepare life scientists for responsible use of AI methods, while contributor services and open standards will drive community contributions of new models and interoperability between analysis tools. Open calls and public challenges will provide state-of-the-art solutions to yet unsolved image analysis problems in the life sciences.

The consortium brings together AI/ML researchers, developers of popular open source image analysis tools, providers of European-scale storage and compute services and European life sciences Research Infrastructures — all united behind the common goal to enable life scientists to fully benefit from the untapped but potentially tremendous power of AI-based analysis methods.

AgroServ, thanks to a large consortium of recognized European Research Infrastructures, features a vast offer of services at all scales, from the molecule, to the organism, to the ecosystem, and to the society. AgroServ goes beyond the state-of-the-art because it will allow users access, for the first time, to make use of a consistent, integrated, and customized offer of services from several RIs spanning all relevant disciplines: they will bring together several types of expertise, disciplines and technologies, integrating competences from chemists, biologists, agronomists, ecologists, bioengineers, analysts, sociologists, economists.

The transdisciplinary offer of services will have a high impact on the future of the food system, preserving biodiversity and reducing the impact of agriculture on climate. The consortium will work closely with partners from the society, farmers, industry, citizens and policy makers, through living labs and towards the establishment of evidence-based policy, and codeveloped practices in agriculture. By delivering a pan-European and inter/multidisciplinary data ecosystem, and providing state-of-the art services on agroecosystems, the project will society long-term capacity to respond to global challenges in the agriculture sector. It will also provide evidence-based policy making for a resilient and sustainable agricultural system and enable new discoveries and knowledge breakthroughs in the field of agroecology. Through user engagement and Living Lab activities, it will develop the agroecology research community, encourage cross-fertilisation and enable a wider sharing of knowledge.

The project is expected to start by the end of 2022.

Abstract

The realization of the full objectives of international policies targeting global food security and climate change mitigation, including the United Nation’s Sustainable Development Goals, the Paris Climate Agreement COP21 and the European Green Deal, requires that we (i) sustainably increase the yield, nutritional quality and biodiversity of major crop species, (ii) select climate-ready crops that are adapted to future weather dynamic and (iii) increase the resource use efficiency of crops for sustainably preserving natural resources. Ultimately, the grand challenge to be met by agriculture is to sustainably provide access to sufficient, nutritious and diverse food to a worldwide growing population, and to support the circular bio-based economy. Future-proofing our crops is an urgent issue and a challenging goal, involving a diversity of crop species in differing agricultural regimes and under multiple environmental drivers, providing versatile crop-breeding solutions within wider socio-economic-ecological systems. This goal can only be realized by a large-scale, international research cooperation. We call for international action and propose a pan-European research initiative, the CropBooster Program, to mobilize the European plant research community and interconnect it with the interdisciplinary expertise necessary to face the challenge. Link to paper

Harbinson et. al. (2021) Designing the Crops for the Future; The CropBooster Program (PDF)

This challenge is part of the MLCAS2021 workshop. Accurate prediction of crop yield can improve agricultural breeding and provide monitoring across diverse climatic conditions. This can protect crop production from climatic challenges. To predict crop yield, it is important to integrate weather information across the crop growing season for multiple genotypes. Unraveling the importance of different weather parameters for crop yield prediction would be a substantial step forward in understanding the impact of climate change on a variety’s plasticity.

Our dataset assimilates and utilizes complex data for crop yield prediction. This dataset can provide a significant advancement in the domain of yield prediction – previous works have mostly relied on a small subset of factors, therefore failing to capture the complexity of biological interactions and more site-specific weather variable complexities. Some approaches have been based on meteorological data (maximum daily temperature, minimum daily temperature) without field-scale farming data. It is important to develop models which can capture temporal correlations and our dataset can enable that.

Link to website

Submission Deadline: June 30, 2023

Scope

Plant phenotyping is widely known as the basis of plant phenomics, which is the bridge as well as a bottleneck for studying the interactions between genomics and the environment. Recently, time-series phenotyping is gaining more and more attention due to its potential in revealing key trait differences in critical growth periods as well as a way to access more functional traits. Meanwhile, proximal sensing and image analysis have brought a renaissance in plant phenotyping. It is possible to combine multi-source proximal sensing techniques, such as RGB, light detection and ranging (LiDAR), multi/hyperspectral, solar-induced fluorescence (SIF), and thermal sensors, to better describe plant growth and development. Image analysis, especially computer vision and data-driven deep learning methods, can make phenotyping more objective, accurate, and efficient than traditional field measurement. Therefore, combining advanced proximal sensing and image analysis will provide a comprehensive study of time-series plant phenotyping.

Given the above context, this special issue invites submissions broadly contributing to time-series plant phenotyping. Specific topics of interest include:

1. Data fusion of multi-source proximal sensing imagery to provide consistent spatial and temporal support.
2. Methods in time-series phenotyping including object detection, tracking, trait extraction, etc.
3. Application of time-series imagery to plant breeding, cultivation, and management.

Articles must be original research, not published elsewhere. All articles will go through a rigorous peer-review process as per the journal norms. Review articles around the topics are also encouraged. Articles must follow the instructions for authors at https://spj.sciencemag.org/journals/plantphenomics/guidelines/.
Guest Editors

Shichao Jin, Nanjing Agricultural University

Yanjun Su, Institute of Botany, Chinese Academy of Sciences

Changying Li, University of Georgia

Qinghua Guo, Peking University

Submission Deadline

June 30, 2023. All papers will be published online after acceptance.

Submission Instructions

Please submit the full manuscript to Plant Phenomics via our online submission system. When submitting, please indicate in your cover letter that your submission is intended for consideration for the special issue, Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping. For inquiries, please contact Dr. Shichao Jin (jschaon@njau.edu.cn).

Submit to this Special Issue