Special Issue "Imaging for Plant Phenotyping"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 20 December 2021.

Special Issue Editors

Dr. Dilip Kumar Biswas
E-Mail Website
Guest Editor
Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
Interests: abiotic stress plant physiology; crop physiology; plant adaptation to climate change; plant phenotyping
Dr. Sebastian Varela
E-Mail Website
Guest Editor
Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois, Champaign, IL, USA
Interests: multi-scale crop phenotyping; remote sensing of crops; machine learning; spatiotemporal modelling

Special Issue Information

Dear Colleagues,

Climate change is taking its toll on crop production worldwide due to changing agronomic conditions through warming, variability of climate, and abiotic stresses along with resource limitations which represents significant challenges we face in our dependence on crops. In the next three decades, production of food, feed and biofuel crops will have to double to meet the projected demands of the global population. Genetic improvements in crop in the face of climate change remain the key role in improving crop productivity, but the current rate of improvement cannot meet the needs of sustainability and food security. The last 20 years have observed significant progress in the genomics for plant breeding research. Linking these advances to crop phenotypes is critical for successful identification of superior cultivars, but this is still limiting. To overcome this challenge, high-throughput phenotyping has emerged as a multidisciplinary area of research combining non-invasive state-of-the-art sensors, image analysis, and predictive modelling to estimate plant phenotypic traits at scale with reduced manpower effort. The rapid development in sensors and low-cost platforms are expected to ease the current phenotyping bottleneck and offer researchers with novel insights to help guide ways to improve crop productivity and adaptation. This Special Issue "Imaging in plant phenotyping" is focused on the latest innovative research in the integration of sensing technologies and methodological advances to estimate crop phenotypic traits. We welcome papers from the global research community actively involved in novel integrations of remote sensing in plant phenotyping to discuss current advances, challenges, and future directions.

In this Special Issue, potential topics include but are not limited to:

  • Aerial and ground high-throughput phenotyping platforms, such as low orbit satellites, unmanned aerial vehicles, close range moving sensing platforms, and ground fixed-point stations;
  • Innovative approaches of using different imaging sensors (e.g., 3-D photogrammetry, hyperspectral, thermal sensors, LIDAR) to collect novel phenotypic traits;
  • Multi-scale integration of sensors;
  • Imagery algorithms (machine learning, deep learning, spatial and spatiotemporal approaches), novel approaches to estimate crop phenotypic traits to improve throughput in field conditions.

Dr. Dilip Kumar Biswas
Dr. Sebastian Varela
Guest Editors

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.


  • Abiotic stress (drought, heat, waterlogging and salinity) and resource-use efficiency
  • Crop phenotyping
  • Climate change
  • Imaging
  • Machine learning

Published Papers (1 paper)

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Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions
Remote Sens. 2021, 13(7), 1380; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071380 - 03 Apr 2021
Cited by 2 | Viewed by 628
Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to [...] Read more.
Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras’ positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy. Full article
(This article belongs to the Special Issue Imaging for Plant Phenotyping)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Morphological and Physiological Screening To Predict Lettuce Biomass Production in Controlled Environment Agriculture
Authors: Changhyeon Kim; Marc W. van Iersel
Affiliation: Department of Horticulture, University of Georgia, Athens, GA, 30602, USA
Abstract: Fast growth is an important crop trait in controlled environment agriculture (CEA). Due to the high operational cost, rapid crop turnover is desirable. Canopy size is associated with fast growth, because a larger canopy size intercepts more photons, increasing canopy photosynthesis and biomass production. Therefore, cultivar selection for CEA may benefit from a simple, and rapid method to quantify canopy size. An ideal screening approach should detect desirable phenotypes non-invasively and at an early growth stage and detect desirable physiological and/or morpho-logical characteristics. Hence, we established a rapid screening protocol based on chlorophyll fluorescence imaging (CFI) to quantify projected canopy size (PCS) of plants, combined with electron transport rate (ETR) measurements using a chlorophyll fluorometer. Eleven lettuce cultivars (Lactuca sativa), selected based on morphological differences, were grown in a greenhouse and imaged biweekly using CFI. Final biomass of green cultivars was correlated with PCS at 13 days after germination (DAG) (R2 = 0.74) when the first true leaves had just appeared and the PCS was < 8.5 cm2. However, early PCS of high anthocyanin (red) cultivars did not show a correlation with final biomass. Light-saturated ETR and biomass were also correlated in green (R2 = 0.36), but not in red cultivars. Because anthocyanins in red lettuce direct absorbed photons away from photosynthesis, anthocyanins lower light use efficiency (LUE; biomass / total incident light on canopy over the cropping cycle) and ETR may be overestimated in such cultivars. Additionally, total incident light on canopy over cropping cycle explained 90% and 55% of variability in biomass production among green and red cultivars, respectively. In conclusion, early PCS quantification can is a useful tool for selection of fast-growing green lettuce phenotypes. However, this approach may not work in cultivars with high anthocyanin content, because anthocyanins direct excitation energy away from photosynthesis and growth.

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