Special Issue Call For Papers: Digital Plant Pathology for Precision Agriculture

Submission deadline: December 1, 2021

Background and scope

Neuer InhaltWe would like to invite you to take a critical look at the past decades of sensor-guided plant pathology in precision agriculture by submitting an original article, review, meta-analysis or perspectives paper to this special issue. We welcome your research, ideas and opinions on how to shape the future of this field. We encourage and invite especially, but not exclusively, early career scientists to submit their research and perspectives.

Over the last 20 years the field of Precision Agriculture has revolved around the goal of integrating sensors, machine learning and new technologies into knowledge based methods for practical plant phenotyping and plant protection. It offers manifold applications in Digital Plant Pathology, even beyond pathogen detection and phytopathometry. However, application of available and swiftly developing technology has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Deciding on a sensor type (e.g. thermography or hyperspectral camera), a deployment platform (e.g. rovers, UAVs, or satellites), and the appropriate spatial and temporal scale adds to the challenge, because all pathosystems are unique and differ in their interactions and symptomology, or lack thereof. Adding biological interactions within pathosystems, their visual and temporal occurrence and their signals to sensor systems further escalade the complexity. Not to forget, the economics of practical implementation must be considered. Eventually, the benefit of Precision Agriculture and Digital Plant Pathology will be through optimal resource utilization yielding significant financial, ecological, and societal returns.

Sophisticated methods to analyze and interpret complex sensor data are necessary in addition to the sensor and platform choice. Modern machine learning - especially deep learning - approaches enable fast and subjective data perusal and identification of probable cohesive parameters during plant-pathogen interactions. However, developed methods often lack trust in performance and reliability in plant protection needed for real field applications. Linking biological cause to machine learning features is a crucial task that will bridge the gap between proof of concept to proof of application in Digital Plant Pathology. In the future, research in the field of interpretable machine learning will hopefully allow operators to interact with the algorithm to gain trust in the system and the technology. New robots for the field, artificial machine intelligence, and natural human intelligence, will define the future of agriculture and plant protection.

For this Special Issue in the Journal of Plant Diseases and Protection we invite you to submit your most recent research on disease identification and quantification from leaf- to field scale, sensing plant-pathogen interactions, sensor based plant protection, and on highly sophisticated sensor data analysis methods. We also welcome data-driven reviews, instructions, and perspectives papers on how we can shape the future of Digital Plant Pathology for Precision Agriculture and ensure to keep its promise.

We explicitly invite and encourage manuscripts in the four following areas of research:

1. Sensor-Plant-Pathogen Interactions
- Disease indicators and detection thresholds for specific sensors in Digital Plant Pathology.
- Sensor and spectral databases for plant disease identification in the field.
- Evaluated systems for Digital Plant Pathology. Validation across and within pathosystems.
2. Machine Learning in Digital Plant Pathology
- New methods for image analysis.
- Interpretable Machine Learning: linking biological processes with algorithmic features.
- In-field data processing workflows for real-time applications and decision making.
3. Robots, UAVs and Satellites for pest and pathogen detection and management
- Sensor integration in multi-sensor approaches and processing workflows.
- Multiscale sensing in plant pathology.
- Good practices and manuals for sensor applications in greenhouses and in the field. Applied precision agriculture.
4. Digital Plant Pathology for Integrated Disease Management
- Adoption rates, economic and environmental evaluation of applied Digital Plant Pathology.
-  Shaping the Digital Plant Pathology infrastructures enable sensor technology.
- Opinion pieces on perspectives and outlooks in Digital Plant Pathology.

All voluntarily submitted or invited manuscripts will be peer-reviewed. Submitted manuscripts should neither have been published nor be under consideration for publication in other venues - with the exception of preprints, which are encouraged. Manuscripts should be formatted according to the journal’s Instructions for Authors and submitted via the online submission system by logging on to the website.

During the submission process, the corresponding author will be asked if the manuscript is part of a special issue. Please respond yes and select the option DigitalPlantPathology21.

We strongly encourage authors to submit data and code/scripts along with the manuscript. Our Publishing Editors will further provide support for preparing data and codes of the accepted articles for appropriate archiving and licensing. Accepted articles will be published online early, and subsequently listed together when the special issue is published.

For more information on the scope of this issue or for questions related to it, please contact the journal.

Guest Editors

Katie Gold
Dr. Kaitlin (Katie) Gold is Assistant Professor of Grape Pathology in the Plant Pathology and Plant-Microbe Biology Section of the School of Integrative Plant Science at Cornell University where she holds primary research and extension responsibilities for grape disease management in New York State. Dr. Gold’s Grape Sensing, Pathology, and Extension Lab at Cornell (GrapeSPEC) studies the fundamental and applied science of plant disease sensing to improve integrated grape disease management.

René Heim
Dr. René HJ Heim is a remote sensing scientist at the UAV Research Center (URC) at Ghent University. He is co-leading the Remote Sensing of Plant Health Lab at the University of Pretoria which is run as a satellite lab between the URC and the Forestry and Agricultural Biotechnology Institute (FABI) in South Africa.

Associated Editors

Ali Kashif Bashir
Dr. Ali Kashif Bashir is Senior Lecturer at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. He is involved in the Institute of Electrical and Electronics Engineers (IEEE) developing standards for the computer and electronics industry.

Anna Brugger
Anna Brugger is a scientist at the Cantonal Advisory Institute Arenenberg, Switzerland, focusing on precision experiments around current issues in arable farming with the help of multispectral cameras and UAVs.

Matheus Kuska
Dr. Matheus Thomas Kuska is a biologist, specialized in plant-microbe interactions and sensor technologies for plant sciences. As the referee for plant protection in arable farming and grassland at the North Rhine-Westphalia Chamber of Agriculture in Germany, he and his team study sustainable agriculture, to secure a sufficient crop production with the lowest environmental stress as possible.

Stefan Paulus
Dr. Stefan Paulus is specialist for 3D & Hyperspectral Imaging, Machine Learning, and Remote Sensing at the Institute of Sugar Beet Research Göttingen, Germany

Anne-Katrin Mahlein
Prof. Dr. Anne-Katrin Mahlein is director of the Institute of Sugar Beet Research in Göttingen, Germany. Her research focuses on digital technologies for plant pathology and crop science. As a professor, she is a member of the Faculty of Agricultural Sciences at the Georg-August University in Göttingen and of the Faculty of Agricultural Sciences at the Rheinische-Friedrich-Wilhelms University in Bonn. She teaches in the field of crop science.