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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 (20 March 2023) | Viewed by 20109

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Special Issue Editors

Prof. Dr. José Ramón Rodríguez-Pérez

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Guest Editor

University of León, Avenida de Astorga, sn, 24401 Ponferrada (León), Spain
Interests: remote sensing; field spectroscopy; precision agriculture

Prof. Dr. Shawn C. Kefauver

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Guest Editor

Integrative Crop Ecophysiology Group, Department B.E.E.C.A. Plant Physiology Section, Faculty of Biology, University of Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
Interests: remote sensing; plant ecophysiology; agriculture, forestry; plant phenotyping; spectroscopy and imaging spectroscopy; UAVs; machine learning; data fusion; data processing
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Special Issue Information

Dear Colleagues,

Currently, agriculture requires crops to be analyzed and monitored in the field rather than in the laboratory. Field spectroscopy is one of the most suitable technologies for assessing plants and soils using a non-destructive approach. It can be defined as the measurement of the spectral properties over a continuous region of the electromagnetic spectrum. This may include visible light and near-infrared sensors, which measure the region from 400 to 1000 nanometers (nm), short-wave infrared sensors, which cover the region from 1000 to 2500 nm, full-range (400–2500 nm) sensors, or other more novel spectral sensors that focus specifically on the ultraviolet, chlorophyll fluorescence, or long-wave infrared spectral regions.

Field spectroscopy is safe, rapid, cost-effective, easy-to-use, and sensitive, and allows us to monitor changes in the characteristics of crops throughout the growth season until harvest. It has also proven to be useful to other agronomic applications of interest, including soil surface monitoring and assessment, fruit yield and quality parameter assessment, and other more specific and value-added agricultural assessments.

This Special Issue aims to present a collection of original research articles and reviews related to recent advances in field spectroscopy in agriculture. Potential topics include, but are not limited to:

assessment of crop quality and yield;
classification of crops and soils;
early detection of crop diseases;
physical and chemical characteristics of crops and soils;
water monitoring in crops and soils;
estimation of plant photosynthesis and respiration parameters using empirical models;
testing and development of advanced radiative transfer models;
fruit yield and quality assessments; and
fruit ripeness and marketability assessments.

Prof. Dr. José Ramón Rodríguez-Pérez
Prof. Dr. Shawn C. Kefauver
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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

agricultural practices
crop stress
field spectroscopy for soils and crops in agriculture
optical reflectance
remote sensing
soil and crop modeling
spectroscopic technologies (new rapid spectroscopy sensors)
sustainable agriculture
radiative transfer models
machine learning/deep learning

Published Papers (9 papers)

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Research

13 pages, 2608 KiB

Open AccessArticle

Total Soluble Solids in Grape Must Estimation Using VIS-NIR-SWIR Reflectance Measured in Fresh Berries

by Karen Brigitte Mejía-Correal, Víctor Marcelo, Enoc Sanz-Ablanedo and José Ramón Rodríguez-Pérez

Agronomy 2023, 13(9), 2275; https://doi.org/10.3390/agronomy13092275 - 29 Aug 2023

Cited by 1 | Viewed by 1424

Abstract

Total soluble solids (TSS) is a key variable taken into account in determining optimal grape maturity for harvest. In this work, partial least square (PLS) regression models were developed to estimate TSS content for Godello, Verdejo (white), Mencía, and Tempranillo (red) grape varieties based on diffuse spectroscopy measurements. To identify the most suitable spectral range for TSS prediction, the regression models were calibrated for four datasets that included the following spectral ranges: 400–700 nm (visible), 701–1000 nm (near infrared), 1001–2500 nm (short wave infrared) and 400–2500 nm (the entire spectral range). We also tested the standard normal variate transformation technique. Leave-one-out cross-validation was implemented to evaluate the regression models, using the root mean square error (RMSE), coefficient of determination (R²), ratio of performance to deviation (RPD), and the number of factors (F) as evaluation metrics. The regression models for the red varieties were generally more accurate than the models of those for the white varieties. The best regression model was obtained for Mencía (red): R² = 0.72, RMSE = 0.55 °Brix, RPD = 1.87, and factors n = 7. For white grapes, the best result was achieved for Godello: R² = 0.75, RMSE = 0.98 °Brix, RPD = 1.97, and factors n = 7. The methodology used and the results obtained show that it is possible to estimate TSS content in grapes using diffuse spectroscopy and regression models that use reflectance values as predictor variables. Spectroscopy is a non-invasive and efficient technique for determining optimal grape maturity for harvest. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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13 pages, 2188 KiB

Open AccessArticle

Leaf Trait Hyperspectral Characterization of Castanea sativa Miller Affected by Dryocosmus kuriphilus Yasumatsu

by Dimas Pereira-Obaya, Fernando Castedo-Dorado, Enoc Sanz-Ablanedo, Karen Brigitte Mejía-Correal and José Ramón Rodríguez-Pérez

Agronomy 2023, 13(3), 923; https://doi.org/10.3390/agronomy13030923 - 20 Mar 2023

Viewed by 1194

Abstract

While populations of the Asian chestnut gall wasp (Dryocosmus kuriphilus Yasumatsu), an invasive pest affecting the European chestnut (Castanea sativa Miller), have started to be controlled biologically, this pest still conditions chestnut tree development. With the aim of assessing plant health status as a means of monitoring gall wasp infestation, we used a field spectroradiometer to collect data from leaves taken from 83 trees in two chestnut orchards. We calculated characteristic spectral signatures for pest infestation, and after training and validation, developed classifiers to distinguish between different infestation levels. Several partial least square discriminant analysis (PLS-DA) and random forest (RF) models were fitted with reflectance and transformed values to obtain characteristic curves reflecting infestation. Four wavelengths (560 nm, 680 nm, 1400 nm, and 1935 nm) were identified as showing the greatest differences between curves. The best overall accuracy (69.23%) was achieved by an RF model fitted with reflectance transformed values. Lower overall accuracy (26.92%) was achieved in distinguishing between infestation levels. In conclusion, while more specific differences in infestation levels were not detectable, our method successfully discriminated between gall absence and presence. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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19 pages, 2916 KiB

Open AccessEditor’s ChoiceArticle

Early-Season Mapping of Johnsongrass (Sorghum halepense), Common Cocklebur (Xanthium strumarium) and Velvetleaf (Abutilon theophrasti) in Corn Fields Using Airborne Hyperspectral Imagery

by María Pilar Martín, Bernarda Ponce, Pilar Echavarría, José Dorado and Cesar Fernández-Quintanilla

Agronomy 2023, 13(2), 528; https://doi.org/10.3390/agronomy13020528 - 11 Feb 2023

Cited by 6 | Viewed by 1591

Abstract

Accurate information on the spatial distribution of weeds is the key to effective site-specific weed management and the efficient and sustainable use of weed control measures. This work focuses on the early detection of johnsongrass, common cocklebur and velvetleaf present in a corn field using high resolution airborne hyperspectral imagery acquired when corn plants were in a four to six leaf growth stage. Following the appropriate radiometric and geometric corrections, two supervised classification techniques, such as spectral angle mapper (SAM) and spectral mixture analysis (SMA) were applied. Two different procedures were compared for endmember selections: field spectral measurements and automatic methods to identify pure pixels in the image. Maps for both, overall weeds and for each of the three weed species, were obtained with the different classification methods and endmember sources. The best results were achieved by defining the endmembers through spectral information collected with a field spectroradiometer. Overall accuracies ranged between 60% and 80% using SAM for maps that do not differentiate the weed species while it decreased to 52% when the three weed species were individually classified. In this case, the SMA classification technique clearly improved the SAM results. The proposed methodology shows it to be a promising prospect to be applicable to low cost images acquired by the new generation of hyperspectral sensors onboard unmanned aerial vehicles (UAVs). Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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14 pages, 2562 KiB

Open AccessArticle

Nitrate Content Assessment in Spinach: Exploring the Potential of Spectral Reflectance in Open Field Experiments

by Fabio Stagnari, Walter Polilli, Gabriele Campanelli, Cristiano Platani, Flaviano Trasmundi, Gianpiero Scortichini and Angelica Galieni

Agronomy 2023, 13(1), 193; https://doi.org/10.3390/agronomy13010193 - 07 Jan 2023

Cited by 1 | Viewed by 1969

Abstract

A rapid, non-destructive method for nitrate content assessment is essential for a rational wide-scale application of nitrogen in sustainable growing spinach. The method should be effective in facing environmental, genotype, and management variability. The results from three field experiments carried out in Teramo (Italy), during the 2021 and 2022 growing seasons, and by combining nitrogen supply with spinach genotypes, are presented. The spectral canopy reflectance was collected to find out the spectral band relationship with nitrate concentration. Preliminary PCA and mixed linear model analysis showed that nitrate content is among the less detectable features. Unexpected chlorosis onset in one experiment added more variability; nevertheless, spectral regions of blue-cyan and early NIR when combined into Vegetation Indexes were able to correlate to nitrate content with R² up to 0.65 in all experiments. This study demonstrates that focusing on just a few spectral regions facilitates the acquisition of suitable and robust information on nitrate content in spinach. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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17 pages, 4126 KiB

Open AccessArticle

Mapping Soil Organic Matter Content Based on Feature Band Selection with ZY1-02D Hyperspectral Satellite Data in the Agricultural Region

by Hengliang Guo, Rongrong Zhang, Wenhao Dai, Xiaowen Zhou, Dujuan Zhang, Yaohuan Yang and Jian Cui

Agronomy 2022, 12(9), 2111; https://doi.org/10.3390/agronomy12092111 - 05 Sep 2022

Cited by 5 | Viewed by 1769

Abstract

Soil organic matter (SOM) is an essential nutrient for crop growth and development. Hyperspectral satellite images with comprehensive spectral band coverage and high spectral resolution can be used to estimate and draw a spatial distribution map of SOM content in the region, which can provide a scientific management basis for precision agriculture. This study takes Xinzheng City, Henan Province’s agricultural area, as the research object. Based on ZY1-02D hyperspectral satellite image data, the first derivative of reflectance (FDR) was processed on the original reflectance (OR). The SOM characteristic spectral bands were extracted using the correlation coefficient (CC) and least absolute shrinkage and selection operator (Lasso) methods. The prediction model of SOM content was established by multiple linear regression (MLR), partial least squares regression (PLSR), and random forest (RF) algorithms. The results showed that: (1) FDR processing can enhance SOM spectral features and reduce noise; (2) the Lasso feature band extraction method can reduce the model’s input variables and raise the estimation precision; (3) the SOM content prediction ability of the RF model was significantly better than that of the MLR and PLSR models. The FDR-Lasso-RF model was the best SOM content prediction model, and the validation set R² = 0.921, MAE_V = 0.512 g/kg, RMSE_V = 0.645 g/kg; (4) compared with laboratory hyperspectral data-SOM prediction methods, hyperspectral satellite data can achieve accurate, rapid, and large-scale SOM content prediction and mapping. This study provides an efficient, accurate, and feasible method for predicting and mapping SOM content in an agricultural region. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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16 pages, 428 KiB

Open AccessArticle

Water Availability Affects the Capability of Reflectance Indices to Estimate Berry Yield and Quality Attributes in Rain-Fed Vineyards

by Lydia Serrano and Gil Gorchs

Agronomy 2022, 12(9), 2091; https://doi.org/10.3390/agronomy12092091 - 01 Sep 2022

Cited by 2 | Viewed by 1286

Abstract

Remote sensing methods are known to provide estimates of berry quality. However, previous studies have shown that the Normalized Difference Vegetation Index (NDVI) failed to predict berry quality attributes in rain-fed vineyards. This study explores the association of several reflectance indices with vine biophysical characteristics and berry yield and quality attributes and their temporal stability. The study was conducted in rain-fed Chardonnay vineyards located around Masquefa (Penedès region, Catalonia, Spain) over four years. Canopy reflectance, fractional Intercepted Photosynthetic Active Radiation, predawn water potential and canopy temperature at midday were measured at veraison whereas berry yield and quality attributes were determined at harvest. Water availability and vine biophysical attributes showed large temporal stability whereas berry quality attributes were not temporally stable. The capability of reflectance indices to estimate berry quality attributes was subject to the timing and extent of water deficits. The Photochemical Reflectance Index (PRI), the NDVI and the Water Index (WI) provided estimates of berry quality attributes under mild, moderate and severe water deficits, respectively. These results might have potential applications in precision viticulture activities such as selective harvesting according to grape quality attributes and the assessment of ripening. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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20 pages, 23328 KiB

Open AccessArticle

Remote Sensing-Based Evaluation of Heat Stress Damage on Paddy Rice Using NDVI and PRI Measured at Leaf and Canopy Scales

by Jae-Hyun Ryu, Dohyeok Oh, Jonghan Ko, Han-Yong Kim, Jong-Min Yeom and Jaeil Cho

Agronomy 2022, 12(8), 1972; https://doi.org/10.3390/agronomy12081972 - 20 Aug 2022

Cited by 4 | Viewed by 2135

Abstract

Extremely high air temperature at the heading stage of paddy rice causes a yield reduction due to the increasing spikelet sterility. Quantifying the damage to crops caused by high temperatures can lead to more accurate estimates of crop yields. The remote sensing technique evaluates crop conditions indirectly but provides information related to crop physiology, growth, and yield. In this study, we aim to assess the crop damage caused by heat stress in paddy rice examined under elevated air temperatures in a temperature gradient field chamber from 2016 to 2019, using remote-sensed vegetation indices. A leaf-spectrometer, field-spectrometers, and a multi-spectral camera were used to monitor the conditions of paddy rice. Although, in the leaf- and canopy-scales, the values of normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) decreased after the heading of rice under normal conditions, the decreasing sensitivity of NDVI and PRI was different depending on the degree of physiological heat stress by high temperature conditions. The NDVI after the heading under extremely high air temperature was not dropped and remained the value before heading. The PRI decreased at all air temperature conditions after the heading; the PRI of the plot exposed to the elevated air temperature was higher than that under ambient air temperature. Further, the relative change in NDVI and PRI after the heading exhibited a strong relationship with the ripening ratio of paddy rice, which is the variable related to crop yield. These remote-sensing results aid in evaluating the crop damage caused by heat stress using vegetation indices. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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12 pages, 2008 KiB

Open AccessArticle

Estimating Leaf Water Content through Low-Cost LiDAR

by Akira Hama, Yutaro Matsumoto and Nobuhiro Matsuoka

Agronomy 2022, 12(5), 1183; https://doi.org/10.3390/agronomy12051183 - 14 May 2022

Cited by 3 | Viewed by 2154

Abstract

In recent years, rapid development has been achieved in technologies and sensors related to autonomous driving and assistive technologies. In this study, low-cost light detection and ranging (LiDAR) was used to estimate leaf water content (LWC) by measuring LiDAR reflectance instead of morphological measurement (e.g., plant size), which is the conventional method. Experimental results suggest that reflection intensity can be corrected using the body temperature of LiDAR, when using reflection intensity observed by LiDAR. Comparisons of corrected LiDAR observation data and changes in reflectance attributed to leaf drying suggest that the reflectance increases with leaf drying in the 905 nm band observed with a hyperspectral camera. The LWC is estimated with an R² of 0.950, RMSE of 6.78%, and MAPE of 18.6% using LiDAR reflectance. Although the 905 nm wavelength used by LiDAR is not the main water absorption band, the reflectance is closely related to the leaf structure; therefore, it is believed that the reflectance changes with structural changes accompanying drying, which allows for the indirect estimation of LWC. This can help utilize the reflectance of the 905 nm single-wavelength LiDAR, which, to the best of our knowledge has not been used in plant observations for estimating LWC. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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17 pages, 1327 KiB

Open AccessArticle

Estimating Soil Properties and Nutrients by Visible and Infrared Diffuse Reflectance Spectroscopy to Characterize Vineyards

by José Ramón Rodríguez-Pérez, Víctor Marcelo, Dimas Pereira-Obaya, Marta García-Fernández and Enoc Sanz-Ablanedo

Agronomy 2021, 11(10), 1895; https://doi.org/10.3390/agronomy11101895 - 22 Sep 2021

Cited by 16 | Viewed by 4235

Abstract

Visible, near, and shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy, a cost-effective and rapid means of characterizing soils, was used to predict soil sample properties for four vineyards (central and north-western Spain). Sieved and air-dried samples were measured using a portable spectroradiometer (350–2500 nm) and compared for pistol grip (PG) versus contact probe (CP) setups. Raw data processed using standard normal variate (SVN) and detrending transformation (DT) were grouped into four subsets (VIS: 350–700 nm; NIR: 701–1000 nm; SWIR: 1001–2500 nm; and full range: 350–2500 nm) in order to identify the most suitable range for determining soil characteristics. The performance of partial least squares regression (PLSR) models in predicting soil properties from reflectance spectra was evaluated by cross-validation. The four spectral subsets and transformed reflectances for each setup were used as PLSR predictor variables. The best performing PLSR models were obtained for pH, electrical conductivity, and phosphorous (R² values above 0.92), while models for sand, nitrogen, and potassium showed moderately good performances (R² values between 0.69 and 0.77). The SWIR subset and SVN + DT processing yielded the best PLSR models for both the PG and CP setups. VIS-NIR-SWIR reflectance spectroscopy shows promise as a technique for characterizing vineyard soils for precision viticulture purposes. Further studies will be carried out to corroborate our findings. Full article

(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

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