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Chlorophyll Fluorescence Sensing in Plant Phenotyping

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 25207

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Dr. Marek Zivcak

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Department of Plant Physiology, Slovak University of Agriculture, 94976 Nitra, Slovakia
Interests: plant stress physiology; abiotic stress; photosynthesis; crop physiology; chlorophyll fluorescence; non-invasive methods; plant phenotyping
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Prof. Dr. Marian Brestic

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Department of Plant Physiology, Slovak University of Agriculture, A. Hlinku 2, 94976 Nitra, Slovakia
Interests: plant breeding & genetics; biochemistry; cell signaling; metabolomics; transcriptomics
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Prof. Dr. Hazem M. Kalaji

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Department of Plant Physiology, Faculty of Agriculture and Biology, Warsaw University of Life Sciences SGGW, Warsaw, Poland
Interests: fluorescence sensors; chlorophyll fluorescence analysis; photochemistry of photosynthesis; plant stress; physiology of plants and algae; plant talk and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, many approaches are being discussed and incorporated into the constantly improving new process of fast, non-destructive phenotyping of plants. Plant phenotyping is the extensive evaluation of multiplex plant features, including photosynthetic performance and responses to various stresses. The developed sensors and systems for phenotyping are important for representing the full set of genetic and environmental factors that play a role in the phenotypic variation of quantitative parameters for cells, tissues, organs or whole plants. Chlorophyll fluorescence sensing represents an important tool enabling the non-invasive analysis of photosynthetic apparatus and the responses of photosynthetic processes to environmental factors or different treatments. Moreover, different techniques and modifications of chlorophyll fluorescence sensors enable us to obtain specific information on plant properties, such as the level of nitrogen nutrition, accumulation of specific compounds (e.g. UV-absorbing compounds, pigments) or structural parameters.

This Special Issue aims to highlight advances in the use of different chlorophyll fluorescence sensors and systems in plants. Topics may include, but are not limited, to the application of the following fluorescence techniques in automated or non-automated plant phenotyping:

Chlorophyll fluorescence imaging
Fast data acquisition chlorophyll fluorescence sensors
Handheld fluorescence sensors
Fluorescence sensors integrated in systems with other types of sensors
Laser-induced fluorescence (LIFT)
Sun-induced fluorescence

Dr. Marek Zivcak
Prof. Dr. Marian Brestic
Prof. Dr. Hazem M. Kalaji
Guest Editors

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Published Papers (5 papers)

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Research

18 pages, 4394 KiB

Open AccessArticle

Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations

by Jidai Chen, Xinjie Liu, Shanshan Du, Yan Ma and Liangyun Liu

Sensors 2020, 20(9), 2493; https://doi.org/10.3390/s20092493 - 28 Apr 2020

Cited by 25 | Viewed by 3527

Abstract

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF₇₆₀) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF₇₆₀ tracked GPP well at both diurnal and seasonal scales, and that SIF₇₆₀ was more linearly correlated to PAR than GPP was. Therefore, the SIF₇₆₀–GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R² value was 0.66 in 2017 and 2018. Based on daily mean observations, the R² value was 0.82 and 0.76 in 2017 and 2018, respectively. Secondly, it was found that the SIF₇₆₀–GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF₇₆₀ decreased noticeably as the CI increased. Finally, the SIF₇₆₀-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF₇₆₀-based GPP estimates based on daily mean observations: the value of R² increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF₇₆₀-based GPP estimation was improved greatly by including the CI, giving a higher R² and a lower RMSE. These values improved from R² = 0.66 and RMSE = 7.02 mw/m²/nm/sr to R² = 0.76 and RMSE = 6.36 mw/m²/nm/sr for the linear model, and from R² = 0.71 and RMSE = 4.76 mw/m²/nm/sr to R² = 0.78 and RMSE = 3.50 mw/m²/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF₇₆₀ is a reliable proxy for GPP and that SIF₇₆₀-based GPP estimation can be greatly improved by integrating the CI with SIF₇₆₀. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable. Full article

(This article belongs to the Special Issue Chlorophyll Fluorescence Sensing in Plant Phenotyping)

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12 pages, 2584 KiB

Open AccessArticle

Salt Priming Protects Photosynthetic Electron Transport against Low-Temperature-Induced Damage in Wheat

by Hui Li, Huawei Li, Yanjie Lv, Yongjun Wang, Zongshuai Wang, Caiyun Xin, Shengqun Liu, Xiancan Zhu, Fengbin Song and Xiangnan Li

Sensors 2020, 20(1), 62; https://doi.org/10.3390/s20010062 - 20 Dec 2019

Cited by 14 | Viewed by 2949

Abstract

Low temperature limits the photochemical efficiency of photosystems in wheat plants. To test the effect of salt priming on the photosynthetic electron transport in wheat under low temperature, the germinating seeds of a winter wheat cv. Jimai44 were primed with varying concentrations of NaCl solutions (0, 10, 30, and 50 mM NaCl, indicated by S0, S10, S30, and S50, respectively) for 6 d, and after 11 d of recovery, the seedlings were subsequently exposed to 24-h low-temperature stress (2 °C). Under low temperature, the S30 plants possessed the highest absorption flux per reaction center and higher density of reaction center per cross-section among the treatments. In addition, S30 plants had higher trapped energy flux for reducing Q_A and fraction of Q_A-reducing reaction centers and non-Q_B reducing center than the non-primed plants under low temperature, indicating that S30 plants could maintain the energy balance of photosystems and a relatively higher maximum quantum efficiency of photosystem II under low temperature. In addition, the low temperature-induced MDA accumulation and cell death were alleviated by salt priming in S30 plants. It was suggested that salt priming with an optimal concentration of NaCl solution (30 mM) during seed germination enhanced the photochemical efficiency of photosystems in wheat seedlings, which could be a potential approach to improve cold tolerance in wheat at an early stage. Full article

(This article belongs to the Special Issue Chlorophyll Fluorescence Sensing in Plant Phenotyping)

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25 pages, 4800 KiB

Open AccessArticle

Phenomic and Physiological Analysis of Salinity Effects on Lettuce

by Neil D. Adhikari, Ivan Simko and Beiquan Mou

Sensors 2019, 19(21), 4814; https://doi.org/10.3390/s19214814 - 5 Nov 2019

Cited by 55 | Viewed by 6071

Abstract

Salinity is a rising concern in many lettuce-growing regions. Lettuce (Lactuca sativa L.) is sensitive to salinity, which reduces plant biomass, and causes leaf burn and early senescence. We sought to identify physiological traits important in salt tolerance that allows lettuce adaptation to high salinity while maintaining its productivity. Based on previous salinity tolerance studies, one sensitive and one tolerant genotype each was selected from crisphead, butterhead, and romaine, as well as leaf types of cultivated lettuce and its wild relative, L. serriola L. Physiological parameters were measured four weeks after transplanting two-day old seedlings into 350 mL volume pots filled with sand, hydrated with Hoagland nutrient solution and grown in a growth chamber. Salinity treatment consisted of gradually increasing concentrations of NaCl and CaCl₂ from 0 mM/0 mM at the time of transplanting, to 30 mM/15 mM at the beginning of week three, and maintaining it until harvest. Across the 10 genotypes, leaf area and fresh weight decreased 0–64% and 16–67%, respectively, under salinity compared to the control. Salinity stress increased the chlorophyll index by 4–26% in the cultivated genotypes, while decreasing it by 5–14% in the two wild accessions. Tolerant lines less affected by elevated salinity were characterized by high values of the chlorophyll fluorescence parameters Fv/Fm and instantaneous photosystem II quantum yield (QY), and lower leaf transpiration. Full article

(This article belongs to the Special Issue Chlorophyll Fluorescence Sensing in Plant Phenotyping)

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24 pages, 5690 KiB

Open AccessArticle

Exploration of Chlorophyll a Fluorescence and Plant Gas Exchange Parameters as Indicators of Drought Tolerance in Perennial Ryegrass

by Piotr Dąbrowski, Aneta H. Baczewska-Dąbrowska, Hazem M. Kalaji, Vasilij Goltsev, Momchil Paunov, Marcin Rapacz, Magdalena Wójcik-Jagła, Bogumiła Pawluśkiewicz, Wojciech Bąba and Marian Brestic

Sensors 2019, 19(12), 2736; https://doi.org/10.3390/s19122736 - 18 Jun 2019

Cited by 96 | Viewed by 6153

Abstract

Perennial ryegrass (Lolium perenne L.) belongs to the common cultivated grass species in Central and Western Europe. Despite being considered to be susceptible to drought, it is frequently used for forming the turf in urban green areas. In such areas, the water deficit in soil is recognized as one of the most important environmental factors, which can limit plant growth. The basic aim of this work was to explore the mechanisms standing behind the changes in the photosynthetic apparatus performance of two perennial ryegrass turf varieties grown under drought stress using comprehensive in vivo chlorophyll fluorescence signal analyses and plant gas exchange measurements. Drought was applied after eight weeks of sowing by controlling the humidity of the roots ground medium at the levels of 30, 50, and 70% of the field water capacity. Measurements were carried out at four times: 0, 120, and 240 h after drought application and after recovery (refilling water to 70%). We found that the difference between the two tested varieties’ response resulted from a particular re-reduction of P700⁺ (reaction certer of PSI) that was caused by slower electron donation from P680. The difference in the rate of electron flow from Photosystem II (PSII) to PSI was also detected. The application of the combined tools (plants’ photosynthetic efficiency analysis and plant gas exchange measurements) allowed exploring and explaining the specific variety response to drought stress. Full article

(This article belongs to the Special Issue Chlorophyll Fluorescence Sensing in Plant Phenotyping)

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17 pages, 3639 KiB

Open AccessArticle

Time-Series Chlorophyll Fluorescence Imaging Reveals Dynamic Photosynthetic Fingerprints of sos Mutants to Drought Stress

by Dawei Sun, Yueming Zhu, Haixia Xu, Yong He and Haiyan Cen

Sensors 2019, 19(12), 2649; https://doi.org/10.3390/s19122649 - 12 Jun 2019

Cited by 27 | Viewed by 4779

Abstract

Resistance to drought stress is one of the most favorable traits in breeding programs yet drought stress is one of the most poorly addressed biological processes for both phenomics and genetics. In this study, we investigated the potential of using a time-series chlorophyll fluorescence (ChlF) analysis to dissect the ChlF fingerprints of salt overly sensitive (SOS) mutants under drought stress. Principle component analysis (PCA) was used to identify a shifting pattern of different genotypes including sos mutants and wild type (WT) Col-0. A time-series deep-learning algorithm, sparse auto encoders (SAEs) neural network, was applied to extract time-series ChlF features which were used in four classification models including linear discriminant analysis (LDA), k-nearest neighbor classifier (KNN), Gaussian naive Bayes (NB) and support vector machine (SVM). The results showed that the discrimination accuracy of sos mutants SOS1-1, SOS2-3, and wild type Col-0 reached 95% with LDA classification model. Sequential forward selection (SFS) algorithm was used to obtain ChlF fingerprints of the shifting pattern, which could address the response of sos mutants and Col-0 to drought stress over time. Parameters including QY, NPQ and Fm, etc. were significantly different between sos mutants and WT. This research proved the potential of ChlF imaging for gene function analysis and the study of drought stress using ChlF in a time-series manner. Full article

(This article belongs to the Special Issue Chlorophyll Fluorescence Sensing in Plant Phenotyping)

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