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Remote and Proximal Sensing for Plant Research

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A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 8941

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Dr. Oksana Sherstneva

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Guest Editor

Department of Biophysics, N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
Interests: phenotyping; remote sensing; hyperspectral imaging; fluorescence imaging; photosynthesis; photosynthetic regulation; biotic stress; abiotic stress; stress tolerance

Dr. Vladimir Sukhov

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Guest Editor

Department of Biophysics, N.I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
Interests: remote sensing; multispectral imaging; hyperspectral imaging; fluorescence imaging; photosynthesis; simulation; plant adaptation; fluctuations
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Special Issue Information

Dear Colleagues,

Modern plant cultivation requires the early revelation of stress changes, which is the basis of effective protection of plants and food security; i.e., cultivation requires the development of methods of remote and proximal sensing of plants.

Highly informative optical methods, including hyper- and multispectral, fluorescent, thermal, and RGB imaging, allow obtaining information about the parameters of the plant non-invasively, including in the laboratory, greenhouse, and fields. In the fields, these systems can be based on mobile platforms (wheeled platforms, tractors, UAVs, planes, satellites); handheld equipment can be also used. In greenhouses and laboratories, stationary high-throughput phenotyping systems, combining several types of sensors and providing a study of the large number of parameters of many plants at high speed, can be additionally used for estimation of plant morphological parameters, physiological processes, and biochemical composition. Finally, it should be noted that there are alternative methods of fast estimation of plant characteristics, e.g., based on measurements and analysis of plant electrical activity.

This Special Issue of Plants will highlight studies on using spectral, fluorescent, and other methods in remote and proximal sensing of physiological, biochemical, and morphological characteristics of plants. The most recent advances in the development of remote and proximal sensing techniques, including new technical solutions for this sensing, will be discussed.

The main topics of the Special Issue include:

Development of multispectral and hyperspectral plant imaging, including complex analysis of spectra and reflectance indices;
Development of passive (sunlight-induced fluorescence) and active (PAM method, JIP test) fluorescent imaging;
Development of RGB imaging;
Development of high-throughput phenotyping systems;
Development of alternative tools of plant remote and proximal sensing.
Other ways of plant remote and proximal sensing as well as plant phenotyping will also be considered in the Special Issue.

Dr. Oksana Sherstneva
Dr. Vladimir Sukhov
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. Plants 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

remote sensing
proximal sensing
multispectral imaging
hyperspectral imaging
reflectance indices
fluorescent imaging
RGB imaging
high-throughput phenotyping

Published Papers (6 papers)

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Research

16 pages, 3861 KiB

Open AccessArticle

Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants

by Alyona Grishina, Oksana Sherstneva, Anna Zhavoronkova, Maria Ageyeva, Tatiana Zdobnova, Maxim Lysov, Anna Brilkina and Vladimir Vodeneev

Plants 2023, 12(22), 3831; https://0-doi-org.brum.beds.ac.uk/10.3390/plants12223831 - 12 Nov 2023

Viewed by 832

Abstract

Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI_700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (F_v/F_m) and effective quantum yields of photosystem II (Φ_PSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. Φ_PSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing for Plant Research)

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16 pages, 2247 KiB

Open AccessArticle

Detecting the Minimum Limit on Wheat Stripe Rust in the Latent Period Using Proximal Remote Sensing Coupled with Duplex Real-Time PCR and Machine Learning

by Qi Liu, Tingting Sun, Xiaojie Wen, Minghao Zeng and Jing Chen

Plants 2023, 12(15), 2814; https://0-doi-org.brum.beds.ac.uk/10.3390/plants12152814 - 29 Jul 2023

Cited by 1 | Viewed by 1000

Abstract

Wheat stripe rust (WSR) is an airborne disease that causes severe damage to wheat. The rapid and early detection of WSR is essential for the prevention and control of this disease. The minimum detection limit (MDL) is one of the most important characteristics of quantitative methods that can be used to determine the scope and applicability of a measurement technique. Three wheat cultivars were inoculated with Puccinia striiformis f.sp. tritici (Pst), and a spectrometer was used to collect the canopy hyperspectral data, and the Pst content was obtained via a duplex real-time polymerase chain reaction (PCR) during the latent period, respectively. The disease index (DI) and molecular disease index (MDI) were calculated. The regression tree algorithm was used to determine the MDL of the Pst based on hyperspectral feature parameters. The logistic, IBK, and random committee algorithms were used to construct the classification model based on the MDL. The results showed that when the MDL was 0.7, IBK had the best recognition accuracy. The optimal model, which used the spectral feature R_2nd.dv ((the second derivative of the original hyperspectral value)) and the modeling ratio 2:1, had an accuracy of 91.67% on the testing set and 90.67% on the 10-fold cross-validation. Thus, during the latent period, the MDL of Pst was determined using hyperspectral technology as 0.7. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing for Plant Research)

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19 pages, 3816 KiB

Open AccessArticle

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

by Renan Falcioni, Werner Camargos Antunes, José Alexandre M. Demattê and Marcos Rafael Nanni

Plants 2023, 12(12), 2347; https://0-doi-org.brum.beds.ac.uk/10.3390/plants12122347 - 16 Jun 2023

Cited by 4 | Viewed by 1381

Abstract

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet–visible (UV–VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R² values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C₃ and C₄ plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing for Plant Research)

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17 pages, 3629 KiB

Open AccessArticle

Trunk Water Potential Measured with Microtensiometers for Managing Water Stress in “Gala” Apple Trees

by Luis Gonzalez Nieto, Annika Huber, Rui Gao, Erica Casagrande Biasuz, Lailiang Cheng, Abraham D. Stroock, Alan N. Lakso and Terence L. Robinson

Plants 2023, 12(9), 1912; https://0-doi-org.brum.beds.ac.uk/10.3390/plants12091912 - 08 May 2023

Cited by 4 | Viewed by 1731

Abstract

The weather variations around the world are already having a profound impact on agricultural production. This impacts apple production and the quality of the product. Through agricultural precision, growers attempt to optimize both yield and fruit size and quality. Two experiments were conducted using field-grown “Gala” apple trees in Geneva, NY, USA, in 2021 and 2022. Mature apple trees (Malus × domestica Borkh. cv. Ultima “Gala”) grafted onto G.11 rootstock planted in 2015 were used for the experiment. Our goal was to establish a relationship between stem water potential (Ψ_trunk), which was continuously measured using microtensiometers, and the growth rate of apple fruits, measured continuously using dendrometers throughout the growing season. The second objective was to develop thresholds for Ψ_trunk to determine when to irrigate apple trees. The economic impacts of different irrigation regimes were evaluated. Three different water regimes were compared (full irrigation, rainfed and rain exclusion to induce water stress). Trees subjected the rain-exclusion treatment were not irrigated during the whole season, except in the spring (April and May; 126 mm in 2021 and 100 mm in 2022); that is, these trees did not receive water during June, July, August and half of September. Trees subjected to the rainfed treatment received only rainwater (515 mm in 2021 and 382 mm in 2022). The fully irrigated trees received rain but were also irrigated by drip irrigation (515 mm in 2021 and 565 mm in 2022). Moreover, all trees received the same amount of water out of season in autumn and winter (245 mm in 2021 and 283 mm in 2022). The microtensiometer sensors detected differences in Ψ_trunk among our treatments over the entire growing season. In both years, experimental trees with the same trunk cross-section area (TCSA) were selected (23–25 cm⁻² TCSA), and crop load was adjusted to 7 fruits·cm⁻² TCSA in 2021 and 8.5 fruits·cm⁻² TCSA in 2022. However, the irrigated trees showed the highest fruit growth rates and final fruit weight (157 g and 70 mm), followed by the rainfed only treatment (132 g and 66 mm), while the rain-exclusion treatment had the lowest fruit growth rate and final fruit size (107 g and 61 mm). The hourly fruit shrinking and swelling rate (mm·h⁻¹) measured with dendrometers and the hourly Ψ_trunk (bar) measured with microtensiometers were correlated. We developed a logistic model to correlate Ψ_trunk and fruit growth rate (g·h⁻¹), which suggested a critical value of −9.7 bars for Ψ_trunk, above which there were no negative effects on fruit growth rate due to water stress in the relatively humid conditions of New York State. A support vector machine model and a multiple regression model were developed to predict daytime hourly Ψ_trunk with radiation and VPD as input variables. Yield and fruit size were converted to crop value, which showed that managing water stress with irrigation during dry periods improved crop value in the humid climate of New York State. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing for Plant Research)

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15 pages, 20990 KiB

Open AccessArticle

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

by Renan Falcioni, João Vitor Ferreira Gonçalves, Karym Mayara de Oliveira, Caio Almeida de Oliveira, José A. M. Demattê, Werner Camargos Antunes and Marcos Rafael Nanni

Plants 2023, 12(6), 1333; https://0-doi-org.brum.beds.ac.uk/10.3390/plants12061333 - 16 Mar 2023

Cited by 6 | Viewed by 1773

Abstract

In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400–700 nm, 700–1300 nm, and 1300–2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R² and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing for Plant Research)

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18 pages, 7762 KiB

Open AccessArticle

A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones

by Renata Andrade, Sérgio Henrique Godinho Silva, Lucas Benedet, Elias Frank de Araújo, Marco Aurélio Carbone Carneiro and Nilton Curi

Plants 2023, 12(3), 561; https://0-doi-org.brum.beds.ac.uk/10.3390/plants12030561 - 26 Jan 2023

Cited by 3 | Viewed by 1477

Abstract

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R²). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops. Full article

(This article belongs to the Special Issue Remote and Proximal Sensing for Plant Research)

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