Special Issue "Applications of Optical Spectroscopy in Plant Sciences"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Ana M. Cavaco
E-Mail Website1 Website2
Guest Editor
Centre of Electronics, Optoelectronics and Telecommunications (CEOT), University of Algarve, Faro, Portugal
Interests: optical-sensing; non-invasive determination of fruit internal quality parameters and fruit disorders; orchard management; precision agriculture; plant and fruit physiology and biochemistry; photosynthesis.
Dr. Andrei B. Utkin
E-Mail Website
Guest Editor
Center of Physics and Engineering of Advanced Materials (CeFEMA), University of Lisbon, 1649-004 Lisbon, Portugal
Interests: electrodynamics; wave optics; spectroscopy and photonics; remote optical sensing; lidar; optoelectronics; photophysiology of plants
Dr. Jorge Marques da Silva
E-Mail Website1 Website2
Guest Editor
BioISI – Biosystems & Integrative Sciences Institute and Department of Plant Biology, Faculty of Sciences, Universidade de Lisboa, Lisboa, Portugal
Interests: plant stress detection and management; plant-water relations; drought resistance; photosynthesis; chlorophyll fluorescence; optical diagnostics; high-throughput plant phenotyping; characterization; valuation of agricultural landraces
Dr. Rui Guerra
E-Mail Website1 Website2
Guest Editor
Centre of Electronics, Optoelectronics and Telecommunications (CEOT), University of Algarve, Faro, Portugal
Interests: optical diagnostics; light scattering and biological applications; non-invasive determination of fruit internal quality parameters; industry applications; pathology symptoms in plant leaves through visible/near infrared spectroscopy; diffuse reflectance spectroscopy; chemometrics; models for light propagation and instrumentation; diode laser spectroscopy

Special Issue Information

Dear Colleagues,

Currently, there are many non-invasive technologies applied to monitoring plants under various conditions, stemming from the development of optical spectroscopy techniques. These are implemented in devices with different functionalities and hardware, assisted by the increasingly accessible computing power and machine learning techniques. Optical spectroscopy has been used to evaluate crop quality and yield and plant responses to biotic and abiotic stress, screening various pathologies, and assessing the contamination in agricultural soils and the impact of plant species in ecosystem biodiversity and sustainability.

In this Special Issue, we invite submissions exploring the development and applications of different forms of optical spectroscopy in plants, namely, visible/near infrared (spectral, multispectral, and hyperspectral), fluorescence (e.g., laser-induced fluorescence) and Raman. Contributions can focus on the development of different spectroscopic systems, applications performed with commercial devices for the monitoring of plants’ responses under specific conditions, or integrated monitoring systems. Survey papers and reviews are also welcomed.

Dr. Ana M. Cavaco
Dr. Andrei B. Utkin
Dr. Jorge Marques da Silva
Dr. Rui Guerra
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. Applied Sciences 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 2000 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

  • Spectroscopy
  • Monitoring
  • Plants
  • Metabolism
  • Non-invasive
  • Stress
  • Quality
  • Sustainability
  • Plant Phenotyping

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data
Appl. Sci. 2021, 11(19), 9230; https://doi.org/10.3390/app11199230 - 04 Oct 2021
Viewed by 336
Abstract
Wheat take-all, caused by two variants of the fungus Gaeumannomyces gramnis (Sacc.) Arx & D. Olivier, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas [...] Read more.
Wheat take-all, caused by two variants of the fungus Gaeumannomyces gramnis (Sacc.) Arx & D. Olivier, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas was reduced by more than 20% and the yield of severe cases was reduced by more than 50%. Large-scale rapid and accurate estimation of the incidence of wheat take-all plays an important role in guiding field control and agricultural yield estimation. In this study, a portable ground spectrometer was used to collect the spectral reflectance in the 350–1050 nm band range of wheat canopy after take-all infection in the wheat grain filling stage and combined with the ground disease survey data.Then a winter wheat take-all disease index estimation model was proposed based on the spectral band division interval and selected band combination. According to the normalized difference spectral index (NDSI) and the determinative coefficient of the disease index formed by any two band combinations, the spectral index band combinations corresponding to the spectral index with high correlation in each region were screened by dividing spectral intervals. Partial least-squares regression was used to establish a binary and ternary disease index calibration model. The results showed that the model based on spectral indices of ternary variables had the highest coefficient of determination. Finally, the optimal regression model of wheat take-all disease condition index composed of NDSI(R590,R598), NDSI(R534,R742) and NDSI(R810,R834) was established: Y = 134.577 − 70.301 NDSI(R590,R598) − 223.533 NDSI(R534,R742) + 51.584 NDSI(R810,R834) (R2 = 0.743, RMSEP = 0.094, df = 15), which was the most suitable model for winter wheat take-all estimation. The construction of this model can provide new method and technical support for future evaluation and monitoring of wheat take-all disease on the field. Full article
(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)
Show Figures

Figure 1

Article
Comparative FT-IR Prospecting for Cellulose in Stems of Some Fiber Plants: Flax, Velvet Leaf, Hemp and Jute
Appl. Sci. 2021, 11(18), 8570; https://doi.org/10.3390/app11188570 - 15 Sep 2021
Viewed by 392
Abstract
Plant fibers are sustainable sources of materials for many industries, and can be obtained from a variety of plants. Cellulose is the main constituent of plant-based fibers, and its properties give the characteristics of the fibers obtained. Detailed characterization of cellulosic fibers is [...] Read more.
Plant fibers are sustainable sources of materials for many industries, and can be obtained from a variety of plants. Cellulose is the main constituent of plant-based fibers, and its properties give the characteristics of the fibers obtained. Detailed characterization of cellulosic fibers is often performed after lengthy extraction procedures, while fast screening might bring the benefit of quick qualitative assessment of unprocessed stems. The aim of this research was to define some marker spectral regions that could serve for fast, preliminary qualitative characterization of unprocessed stems from some textile plants through a practical and minimally invasive method without lengthy extraction procedures. This could serve as a screening method for sorting raw materials by providing an accurate overall fingerprint of chemical composition. For this purpose, we conducted comparative Fourier Transform Infrared Spectroscopy (FT-IR) prospecting for quality markers in stems of flax (Linum usitatissimum L.), velvet leaf (Abutilon theophrasti Medik.), hemp (Cannabis sativa L.) and jute (Corchorus olitorius L.). Analysis confirmed the presence of major components in the stems of the studied plants. Fingerprint regions for cellulose signals were attributed to bands at 1420–1428 cm−1 assigned to the crystalline region and 896–898 cm−1 assigned to the amorphous region of cellulose. The optimization of characterization methods for raw materials is important and can find immediate practical applications. Full article
(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)
Show Figures

Figure 1

Article
Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana
Appl. Sci. 2021, 11(14), 6392; https://doi.org/10.3390/app11146392 - 11 Jul 2021
Viewed by 650
Abstract
Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) [...] Read more.
Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines. Full article
(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)
Show Figures

Figure 1

Article
NIRS Estimation of Drought Stress on Chemical Quality Constituents of Taro (Colocasia esculenta L.) and Sweet Potato (Ipomoea batatas L.) Flours
Appl. Sci. 2020, 10(23), 8724; https://doi.org/10.3390/app10238724 - 05 Dec 2020
Viewed by 714
Abstract
Taro (Colocasia esculenta (L.) Schott) and sweet potato (Ipomoea batatas (L.) Lam.) are important food crops worldwide, whose productivity is threatened by climatic constraints, namely drought. Data calibration, validation, and model development of high-precision near-infrared spectroscopy (NIRS) involving multivariate analyses are [...] Read more.
Taro (Colocasia esculenta (L.) Schott) and sweet potato (Ipomoea batatas (L.) Lam.) are important food crops worldwide, whose productivity is threatened by climatic constraints, namely drought. Data calibration, validation, and model development of high-precision near-infrared spectroscopy (NIRS) involving multivariate analyses are needed for the fast prediction of the quality of tubers and shoots impacted by drought stress. The main objective of this study was to generate accurate NIRS models for quality assessment of taro and sweet potato accessions (acc.) subjected to water scarcity conditions. Seven taro and eight sweet potato acc. from diverse geographical origins were evaluated for nitrogen (N), protein (Pt), starch (St), total mineral (M), calcium oxalate (CaOx), carbon isotope discrimination (Δ13C), and nitrogen isotopic composition (δ15N). Models were developed separately for both crops underground and aboveground organs. N, Pt, St, and M models could be used as quality control constituents, with a determination coefficient of prediction (r2pred) between 0.856 and 0.995. δ13C, δ15N, and CaOx, with r2pred between 0.178 and 0.788, could be used as an informative germplasm screening tool. The approach used in the present study demonstrates NIRS’s potential for further research on crop quality under drought. Full article
(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)
Show Figures

Figure 1

Article
Determination of the Most Effective Wavelengths for Prediction of Fuji Apple Starch and Total Soluble Solids Properties
Appl. Sci. 2020, 10(22), 8145; https://doi.org/10.3390/app10228145 - 17 Nov 2020
Viewed by 659
Abstract
Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three [...] Read more.
Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three different growth stages, and then starch and soluble solids were extracted. Spectral data in the range of 800 to 900 nm were used to predict the amount of starch content and 920 to 980 nm to estimate total soluble solids (TSS). Reflectance spectra were pre-processed and the most effective wavelengths of each property were selected using hybrid artificial neural network-simulated annealing (ANN-SA). Non-destructive estimation of physicochemical properties was conducted using spectral data of the most effective wavelengths using a hybrid artificial neural network-biogeography-based optimization algorithm (ANN-BBO). The results indicated that the regression coefficient of the best state of training for predicting starch was 0.97 and of TSS was 0.96, while R2 was 0.92 for both. The most effective wavelengths were 852.58, 855.54, 849.03, 855.83, 853.47, 844.90 nm for starch and 967.86, 966.67, 964.90, 958.40, 957.22, 963.97 nm for TSS. Full article
(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)
Show Figures

Figure 1

Article
Glyphosate-Based Herbicide Toxicophenomics in Marine Diatoms: Impacts on Primary Production and Physiological Fitness
Appl. Sci. 2020, 10(21), 7391; https://doi.org/10.3390/app10217391 - 22 Oct 2020
Cited by 7 | Viewed by 836
Abstract
Glyphosate is the main active component of the commercial formulation Roundup®, the most widely used chemical herbicide worldwide. However, its potential high toxicity to the environment and throughout trophic webs has come under increasing scrutiny. The present study aims to investigate [...] Read more.
Glyphosate is the main active component of the commercial formulation Roundup®, the most widely used chemical herbicide worldwide. However, its potential high toxicity to the environment and throughout trophic webs has come under increasing scrutiny. The present study aims to investigate the application of bio-optical techniques and their correlation to physiological and biochemical processes, including primary productivity, oxidative stress, energy balance, and alterations in pigment and lipid composition in Phaeodactylum tricornutum, a representative species of marine diatoms, using the case study of its response to the herbicide glyphosate-based Roundup® formulation, at environmentally relevant concentrations. Cultures were exposed to the herbicide formulation representing effective glyphosate concentrations of 0, 10, 50, 100, 250, and 500 μg L−1. Results showed that high concentrations decreased cell density; furthermore, the inhibition of photosynthetic activity was not only caused by the impairment of electron transport in the thylakoids, but also by a decrease of antioxidant capacity and increased lipid peroxidation. Nevertheless, concentrations of one of the plastidial marker fatty acids had a positive correlation with the highest concentration as well as an increase in total protein. Cell energy allocation also increased with concentration, relative to control and the lowest concentration, although culture growth was inhibited. Pigment composition and fatty acid profiles proved to be efficient biomarkers for the highest glyphosate-based herbicide concentrations, while bio-optical data separated controls from intermediate concentrations and high concentrations. Full article
(This article belongs to the Special Issue Applications of Optical Spectroscopy in Plant Sciences)
Show Figures

Figure 1

Back to TopTop