Special Issue "Acquire and Perceive: Novel Approaches for Imaging-Based Plant Phenotyping"
Deadline for manuscript submissions: closed (31 March 2021).
Interests: computer vision; machine learning; plant phenotyping; deep learning aspects: explain-ability; efficient inference; modular networks
Interests: computer vision; deep learning; unsupervised domain adaptation; plant image analysis
Special Issues and Collections in MDPI journals
Interests: environmental optical acquisition for agriculture tasks; computational optics; optical design inverse problems; learning
Plants are the fundamental source of food for people, livestock, and all live species on earth. The growth in the human population, with 10 billion people expected by 2050, requires an increase of 50% in agriculture production. Crop optimization is approached by multiple means, including automation of agricultural operations and improved plant breeding process, creating an urgent need for plant trait analysis and phenotyping. However, manual plant analysis is tedious, often destructive for the plant, and non-scalable. With improved sensors and recent advances in machine learning (especially deep learning), imaging-based plant analysis provides a promising alternative, with a growing impact.
This Special Issue invites cutting-edge contributions concerning all aspects of the imaging-based plant analysis challenge. Image acquisition is one topic that is of particular interest. Agriculture monitoring is done in complex and changing illumination conditions, often with modalities beyond RGB, such as depth or hyperspectral data. Papers considering illumination condition, illumination design, and joint illumination, as well as acquisition algorithms, sensor fusion, or image processing design, are encouraged. Another topic of interest is image perception—computer vision and machine learning techniques applied to plant analysis from images. Novel phenotyping tasks, as well as methods for improved accuracy, and/or robustness in existing tasks are welcome. Additional topics of interest include (but are not limited to) fine-grained phenotyping, flexibility and task transfer, and phenotype tracking in a time series. Furthermore, people wishing to discuss a topic of particular interest, to outline the next steps and challenges, are welcome to submit review/survey papers.
Dr. Aharon Bar-Hillel
Dr. Mario Valerio Giuffrida
Dr. Iftach Klapp
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Plant phenotyping and acquisition
- Computer vision
- Machine learning/deep learning
- Precision agriculture
- Multi-modal imaging and sensor fusion
- Joint illumination and image processing design
- Acquisition and phenotype tracking in time series