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The Advantages of Using Multi- and Hyperspectral Data in Agriculture, Horticulture and Forestry

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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 13350

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Dr. Lorenzo Cotrozzi

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Plant Pathology Lab, Department of Agriculture, Food and Environment, University of Pisa, 56124 Pisa, Italy
Interests: Plant pathology, biomonitoring, vegetation spectroscopy, hyperspectral remote sensing

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Dear Colleagues,

Advancements in our ability to rapidly detect and monitor plant interactions with the environment are necessary to improve crop, forest, and ecosystem management practices and meet global challenges such as climate change and food security.

The use of multi- and hyperspectral data in plant science research (i.e., vegetation spectroscopy) represents a powerful tool for plant and ecosystem management because it is a non-destructive, rapid, and relatively low-cost approach for rapid in vivo evaluation of vegetation status. In addition, this approach can help to monitor plant function over large geographic regions if scaled to remote collections from air- or space-borne platforms where data are generated in the form of a 3D spatial map of spectral variation related to the plant’s functional dynamics (i.e., imaging spectroscopy). However, questions remain regarding limitations and effective advantages of using the technique.

This Special Issue aims to collect the most recent research on the use of multi- and hyperspectral data (collected at any scale, from leaf to space level) in agriculture, horticulture, and forestry, as well as reviews showing the state of art and directions of this technique. Contributions covering related points such as technical improvement of instrumentation, advancement of chemometric modeling methods, or economic impacts are welcomed.

Outcomes and approaches presented in this Special Issue could have applications in numerous scientific fields such as precision agriculture, robotic monitoring, plant phenotyping, and ecosystem management.

Dr. Lorenzo Cotrozzi
Guest Editor

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Keywords

vegetation spectroscopy
imaging spectroscopy
crop and ecosystem management
plant phenotyping
precision agriculture
reflectance
vegetation spectral indices
chemometric modeling
spectral signatures
spectrometers

Published Papers (4 papers)

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Research

11 pages, 495 KiB

Open AccessArticle

FTIR Screening to Elucidate Compositional Differences in Maize Recombinant Inbred Lines with Contrasting Saccharification Efficiency Yields

by Ana López-Malvar, Rogelio Santiago, Rosa Ana Malvar, Daniel Martín, Inês Pereira dos Santos, Luís A. E. Batista de Carvalho, Laura Faas, Leonardo D. Gómez and Ricardo M. F. da Costa

Agronomy 2021, 11(6), 1130; https://doi.org/10.3390/agronomy11061130 - 02 Jun 2021

Cited by 13 | Viewed by 2509

Abstract

With a high potential to generate biomass, maize stover arises as an outstanding feedstock for biofuel production. Maize stover presents the added advantage of being a multiple exploitation of the crop as a source of food, feed, and energy. In this study, contrasting groups of recombinant inbred lines (RILs) from a maize multiparent advanced generation intercross (MAGIC) population that showed variability for saccharification efficiency were screened by FTIR-ATR spectroscopy to explore compositional differences between high and low saccharification yielders. High and low saccharification efficiency groups differed in cell wall compositional features: high saccharification RILs displayed higher proportions of S subunits, aromatic compounds, and hemicellulose as opposed to low saccharification efficiency groups in which FTIR predicted higher proportions of lignin, more precisely lignin being richer in subunits G, and greater proportions of crystalline cellulose and acetyl methyl esters. The application of FTIR-ATR spectroscopy in this material allowed us to obtain a rapid and broad vision of cell wall compositional features in contrasting groups of saccharification efficiency. These results helped us to deepen our knowledge into the relationship between cell wall composition and biorefining potential; they also allowed us to establish new targets for future research regarding lignocellulosic bioconversion. Full article

(This article belongs to the Special Issue The Advantages of Using Multi- and Hyperspectral Data in Agriculture, Horticulture and Forestry)

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20 pages, 2399 KiB

Open AccessArticle

Hyperspectral Detection and Monitoring of Salt Stress in Pomegranate Cultivars

by Antonella Calzone, Lorenzo Cotrozzi, Giacomo Lorenzini, Cristina Nali and Elisa Pellegrini

Agronomy 2021, 11(6), 1038; https://doi.org/10.3390/agronomy11061038 - 22 May 2021

Cited by 17 | Viewed by 2443

Abstract

Advancements in the ability to detect plant responses to salinity are mandatory to improve crop yield, quality, and management practices. The present study shows the capability of hyperspectral reflectance (400–2400 nm) to rapidly and non-destructively detect and monitor the responses of two pomegranate cultivars (Parfianka, P, and Wonderful, W) under salt treatment (i.e., 200 mL of 100 mM NaCl solution every day) for 35 days. Analyzing spectral signatures from asymptomatic leaves, the two cultivars, as well as salinity conditions were discriminated. Furthermore, using a partial least squares regression approach, we constructed predictive models to concomitantly estimate (goodness-of-fit model, R²: 0.61–0.79; percentage of the root mean square error over the data range, %_RMSE: 9–14) from spectra of various physiological leaf parameters commonly investigated in plant/salinity studies. The analyses of spectral signatures enabled the early detection of salt stress (i.e., from 14 days from the beginning of treatment, FBT), even in the absence of visible symptoms, but they did not allow the identification of the different degrees of salt tolerance between cultivars; this cultivar-specific tolerance to salt was instead reported by analyzing variations of leaf parameters estimated from spectra (W was less tolerant than P), which, in turn, allowed the detection of salt stress only at later times of analysis (i.e., slightly from 21 day FBT and, evidently, at the end of treatment). The proposed approach could be used in precision agriculture, high-throughput plant phenotyping, and smart nursery management to enhance crop quality and yield. Full article

(This article belongs to the Special Issue The Advantages of Using Multi- and Hyperspectral Data in Agriculture, Horticulture and Forestry)

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17 pages, 1723 KiB

Open AccessArticle

Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies

by Tiago Rodrigues Tavares, José Paulo Molin, Lidiane Cristina Nunes, Marcelo Chan Fu Wei, Francisco José Krug, Hudson Wallace Pereira de Carvalho and Abdul Mounem Mouazen

Agronomy 2021, 11(6), 1028; https://doi.org/10.3390/agronomy11061028 - 21 May 2021

Cited by 17 | Viewed by 3648

Abstract

Rapid, cost-effective, and environmentally friendly analysis of key soil fertility attributes requires an ideal combination of sensors. The individual and combined performance of visible and near infrared (VNIR) diffuse reflectance spectroscopy, X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) was assessed for predicting clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients in tropical soils. A set of 102 samples, collected from two agricultural fields, with broad ranges of fertility attributes were selected. Two contrasting data fusion approaches have been applied for modeling: (i) merging spectral data of different sensors followed by partial least squares regression (PLS), known as fusion before prediction; and (ii) applying the Granger and Ramanathan (GR) averaging approach, known as fusion after prediction. Results showed VNIR as individual technique to be the best for the prediction of clay and OM content (2.61 ≤ residual prediction deviation (RPD) ≤ 3.37), while the chemical attributes CEC, V, ex-P, ex-K, ex-Ca, and ex-Mg were better predicted (1.82 ≤ RPD ≤ 4.82) by elemental analysis techniques (i.e., XRF and LIBS). Only pH cannot be predicted regardless the technique. The attributes OM, V, and ex-P were best predicted using single-sensor approaches, while the attributes clay, CEC, pH, ex-K, ex-Ca, and ex-Mg were overall best predicted using multi-sensor approaches. Regarding the performance of the multi-sensor approaches, ex-K, ex-Ca, and ex-Mg, were best predicted (RPD of 4.98, 5.30, and 4.11 for ex-K, ex-Ca and ex-Mg, respectively) using two-sensor fusion approach (VNIR + XRF for ex-K and XRF + LIBS for ex-Ca and ex-Mg), while clay, CEC and pH were best predicted (RPD of 4.02, 2.63, and 1.32 for clay, CEC, and pH, respectively) with the three-sensor fusion approach (VNIR + XRF + LIBS). Therefore, the best combination of sensors for predicting key fertility attributes proved to be attribute-specific, which is a drawback of the data fusion approach. The present work is pioneering in highlighting benefits and limitations of the in tandem application of VNIR, XRF, and LIBS spectroscopies for fertility analysis in tropical soils. Full article

(This article belongs to the Special Issue The Advantages of Using Multi- and Hyperspectral Data in Agriculture, Horticulture and Forestry)

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16 pages, 3643 KiB

Open AccessArticle

Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data

by Thomas Astor, Supriya Dayananda, Sunil Nautiyal and Michael Wachendorf

Agronomy 2020, 10(10), 1600; https://doi.org/10.3390/agronomy10101600 - 19 Oct 2020

Cited by 16 | Viewed by 3925

Abstract

Remote sensing (RS) has been an effective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error < 10% was achieved. The growth stage of the crops had no significant effect on the prediction accuracy, although increasing trends of an underestimation of FMY with later sampling dates for eggplant and tomato were found. The study proves that an estimation of vegetable FMY using RS data is successful throughout the growing season. Different RS datasets were best for biomass prediction of the three vegetables, indicating that multi-sensory data collection should be preferred to single sensor use, as no one sensor system is superior. Full article

(This article belongs to the Special Issue The Advantages of Using Multi- and Hyperspectral Data in Agriculture, Horticulture and Forestry)

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