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High-Throughput Phenotyping of Crop Traits: Progresses, Opportunities, and Challenges

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 28183

Special Issue Editors

Center for Environment, Energy, and Economy, Harrisburg University, Harrisburg, PA 17101, USA
Interests: remote sensing; plant physiology; urban climate; soil science; machine learning; digital agriculture; ecology
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Guest Editor
Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Interests: photosynthesis; in-field crop phenotyping; food security
School of Biological Sciences, The University of Hong Kong, Room 2N12, Kadoorie Biological Sciences Building, Pokfulam, Hong Kong, China
Interests: remote sensing of vegetation structure, function, and traits; biogeochemical cycling of carbon, water, and energy; physiological ecology; global change and its ecological impacts; model–data fusion

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Guest Editor
1. Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
2. Global Change and Photosynthesis Unit, United States Department of Agriculture-Agricultural Research Service, Urbana, IL 61801, USA
Interests: plant biology; biofuels; photosynthesis; global change; agriculture

Special Issue Information

Dear Colleagues,

Improved crop productivity is of paramount importance to meet increasing agricultural demands associated with exponential population growth. These increasing demands will be challenged further with the pressures from climate change and with the world’s shrinking farmlands. Solutions to overcome this daunting challenge include breeding better crops through both traditional techniques and genetic modifications. Currently, a wealth of genomic information associated with crop productivity is available; yet linking these resources to crop phenotypes under field conditions is severely limiting. This phenotypic bottleneck restricts advances in crop improvement.

To bridge this knowledge gap, advances in high-throughput phenotyping (HTP) are highly sought to estimate crop phenotypic traits in a nondestructive, rapid, and cost-effective way. The rapid advancements in sensor technologies and low-cost sensing platforms are projected to ease the crop phenotyping bottleneck and offer scientists with rich data to help to seek the ways to improve crop productivity.

This Special Issue aims at showcasing the latest developments in HTP platforms (HTPPs), sensing technologies, and methodological advances to measure crop phenotypic traits from a proximal and remote sensing perspective. We also welcome review papers to synthesize the recent progresses of high-throughput phenotyping and to discuss those grand challenges remaining unresolved. In this Special Issue, potential topics include but are not limited to:

  • High-throughput phenotyping platforms (HTPPs), such as unmanned aerial vehicles, robots, and gantries that have an important component in close-range/remote sensing;
  • Innovative use of new sensors to collect phenotypic data (e.g., LiDAR, solar-induced florescence, thermal sensor);
  • State-of-the-art techniques to process phenotypic measurements (e.g., deep learning);
  • Data fusion (e.g., fusion of multisource data, such as structural, optical, physiological, and thermal data) for understanding plant growth;
  • Advances in hyperspectral remote sensing for phenotyping;
  • Phenotyping of plant stress (e.g., disease and drought stress).

Topics should be related to improving agricultural output, which includes all facets of agriculture, such as yield quantity, yield quality, crop health, agroecosystem services, etc.

Dr. Peng Fu
Dr. Katherine Meacham-Hensold
Dr. Jin Wu
Prof.Dr. Carl Bernacchi
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 submissions that pass pre-check are 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 2700 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.

Keywords

  • high-throughput phenotyping
  • close-range/remote sensing
  • crop productivity
  • data fusion
  • plant stress
  • hyperspectral image processing
  • crop traits
  • agroecosystem services
  • plant physiology

Published Papers (5 papers)

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19 pages, 7341 KiB  
Article
High-Throughput Phenotyping of Soybean Maturity Using Time Series UAV Imagery and Convolutional Neural Networks
by Rodrigo Trevisan, Osvaldo Pérez, Nathan Schmitz, Brian Diers and Nicolas Martin
Remote Sens. 2020, 12(21), 3617; https://doi.org/10.3390/rs12213617 - 4 Nov 2020
Cited by 21 | Viewed by 7172
Abstract
Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual [...] Read more.
Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges. Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs. Full article
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22 pages, 4168 KiB  
Article
Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation
by Bikram Pratap Banerjee, German Spangenberg and Surya Kant
Remote Sens. 2020, 12(19), 3164; https://doi.org/10.3390/rs12193164 - 26 Sep 2020
Cited by 32 | Viewed by 4141
Abstract
Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages [...] Read more.
Efficient, precise and timely measurement of plant traits is important in the assessment of a breeding population. Estimating crop biomass in breeding trials using high-throughput technologies is difficult, as reproductive and senescence stages do not relate to reflectance spectra, and multiple growth stages occur concurrently in diverse genotypes. Additionally, vegetation indices (VIs) saturate at high canopy coverage, and vertical growth profiles are difficult to capture using VIs. A novel approach was implemented involving a fusion of complementary spectral and structural information, to calculate intermediate metrics such as crop height model (CHM), crop coverage (CC) and crop volume (CV), which were finally used to calculate dry (DW) and fresh (FW) weight of above-ground biomass in wheat. The intermediate metrics, CHM (R2 = 0.81, SEE = 4.19 cm) and CC (OA = 99.2%, Κ = 0.98) were found to be accurate against equivalent ground truth measurements. The metrics CV and CV×VIs were used to develop an effective and accurate linear regression model relationship with DW (R2 = 0.96 and SEE = 69.2 g/m2) and FW (R2 = 0.89 and SEE = 333.54 g/m2). The implemented approach outperformed commonly used VIs for estimation of biomass at all growth stages in wheat. The achieved results strongly support the applicability of the proposed approach for high-throughput phenotyping of germplasm in wheat and other crop species. Full article
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22 pages, 5419 KiB  
Article
Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
by Yu Jiang, John L. Snider, Changying Li, Glen C. Rains and Andrew H. Paterson
Remote Sens. 2020, 12(2), 315; https://doi.org/10.3390/rs12020315 - 18 Jan 2020
Cited by 8 | Viewed by 4350
Abstract
Improving plant photosynthesis provides the best possibility for increasing crop yield potential, which is considered a crucial effort for global food security. Chlorophyll fluorescence is an important indicator for the study of plant photosynthesis. Previous studies have intensively examined the use of spectrometer, [...] Read more.
Improving plant photosynthesis provides the best possibility for increasing crop yield potential, which is considered a crucial effort for global food security. Chlorophyll fluorescence is an important indicator for the study of plant photosynthesis. Previous studies have intensively examined the use of spectrometer, airborne, and spaceborne spectral data to retrieve solar induced fluorescence (SIF) for estimating gross primary productivity and carbon fixation. None of the methods, however, had a spatial resolution and a scanning throughput suitable for applications at the canopy and sub-canopy levels, thereby limiting photosynthesis analysis for breeding programs and genetics/genomics studies. The goal of this study was to develop a hyperspectral imaging approach to characterize plant photosynthesis at the canopy level. An experimental field was planted with two cotton cultivars that received two different treatments (control and herbicide treated), with each cultivar-treatment combination having eight replicate 10 m plots. A ground mobile sensing system (GPhenoVision) was configured with a hyperspectral module consisting of a spectrometer and a hyperspectral camera that covered the spectral range from 400 to 1000 nm with a spectral sampling resolution of 2 nm. The system acquired downwelling irradiance spectra from the spectrometer and reflected radiance spectral images from the hyperspectral camera. On the day after 24 h of the DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) application, the system was used to conduct six data collection trials in the experiment field from 08:00 to 18:00 with an interval of two hours. A data processing pipeline was developed to measure SIF using the irradiance and radiance spectral data. Diurnal SIF measurements were used to estimate the effective quantum yield and electron transport rate, deriving rapid light curves (RLCs) to characterize photosynthetic efficiency at the group and plot levels. Experimental results showed that the effective quantum yields estimated by the developed method highly correlated with those measured by a pulse amplitude modulation (PAM) fluorometer. In addition, RLC characteristics calculated using the developed method showed similar statistical trends with those derived using the PAM data. Both the RLC and PAM data agreed with destructive growth analyses. This suggests that the developed method can be used as an effective tool for future breeding programs and genetics/genomics studies to characterize plant photosynthesis at the canopy level. Full article
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18 pages, 1057 KiB  
Article
High-Throughput Phenotyping of Indirect Traits for Early-Stage Selection in Sugarcane Breeding
by Sijesh Natarajan, Jayampathi Basnayake, Xianming Wei and Prakash Lakshmanan
Remote Sens. 2019, 11(24), 2952; https://doi.org/10.3390/rs11242952 - 10 Dec 2019
Cited by 36 | Viewed by 4078
Abstract
One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based [...] Read more.
One of the major limitations for sugarcane genetic improvement is the low heritability of yield in the early stages of breeding, mainly due to confounding inter-plot competition effects. In this study, we investigate an indirect selection index (Si), developed based on traits correlated to yield (indirect traits) that were measured using an unmanned aerial vehicle (UAV), to improve clonal assessment in early stages of sugarcane breeding. A single-row early-stage clonal assessment trial, involving 2134 progenies derived from 245 crosses, and a multi-row experiment representative of pure-stand conditions, with an unrelated population of 40 genotypes, were used in this study. Both experiments were screened at several stages using visual, multispectral, and thermal sensors mounted on a UAV for indirect traits, including canopy cover, canopy height, canopy temperature, and normalised difference vegetation index (NDVI). To construct the indirect selection index, phenotypic and genotypic variance-covariances were estimated in the single-row and multi-row experiment, respectively. Clonal selection from the indirect selection index was compared to single-row yield-based selection. Ground observations of stalk number and plant height at six months after planting made from a subset of 75 clones within the single-row experiment were highly correlated to canopy cover (rg = 0.72) and canopy height (rg = 0.69), respectively. The indirect traits had high heritability and strong genetic correlation with cane yield in both the single-row and multi-row experiments. Only 45% of the clones were common between the indirect selection index and single-row yield based selection, and the expected efficiency of correlated response to selection for pure-stand yield based on indirect traits (44%–73%) was higher than that based on single-row yield (45%). These results highlight the potential of high-throughput phenotyping of indirect traits combined in an indirect selection index for improving early-stage clonal selections in sugarcane breeding. Full article
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10 pages, 8926 KiB  
Technical Note
Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning
by Ethan L. Stewart, Tyr Wiesner-Hanks, Nicholas Kaczmar, Chad DeChant, Harvey Wu, Hod Lipson, Rebecca J. Nelson and Michael A. Gore
Remote Sens. 2019, 11(19), 2209; https://doi.org/10.3390/rs11192209 - 21 Sep 2019
Cited by 67 | Viewed by 7278
Abstract
Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight [...] Read more.
Plant disease poses a serious threat to global food security. Accurate, high-throughput methods of quantifying disease are needed by breeders to better develop resistant plant varieties and by researchers to better understand the mechanisms of plant resistance and pathogen virulence. Northern leaf blight (NLB) is a serious disease affecting maize and is responsible for significant yield losses. A Mask R-CNN model was trained to segment NLB disease lesions in unmanned aerial vehicle (UAV) images. The trained model was able to accurately detect and segment individual lesions in a hold-out test set. The mean intersect over union (IOU) between the ground truth and predicted lesions was 0.73, with an average precision of 0.96 at an IOU threshold of 0.50. Over a range of IOU thresholds (0.50 to 0.95), the average precision was 0.61. This work demonstrates the potential for combining UAV technology with a deep learning-based approach for instance segmentation to provide accurate, high-throughput quantitative measures of plant disease. Full article
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