scholarly journals Application of Phenotyping Methods in Detection of Drought and Salinity Stress in Basil (Ocimum basilicum L.)

2021 ◽  
Vol 12 ◽  
Author(s):  
Boris Lazarević ◽  
Zlatko Šatović ◽  
Ana Nimac ◽  
Monika Vidak ◽  
Jerko Gunjača ◽  
...  
Keyword(s):  
Drought Stress ◽  
Salinity Stress ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Ocimum Basilicum ◽  
Phenotypic Traits ◽  
Severe Drought ◽  
Moderate Salinity ◽  
Automated Phenotyping

Basil is one of the most widespread aromatic and medicinal plants, which is often grown in drought- and salinity-prone regions. Often co-occurrence of drought and salinity stresses in agroecosystems and similarities of symptoms which they cause on plants complicates the differentiation among them. Development of automated phenotyping techniques with integrative and simultaneous quantification of multiple morphological and physiological traits enables early detection and quantification of different stresses on a whole plant basis. In this study, we have used different phenotyping techniques including chlorophyll fluorescence imaging, multispectral imaging, and 3D multispectral scanning, aiming to quantify changes in basil phenotypic traits under early and prolonged drought and salinity stress and to determine traits which could differentiate among drought and salinity stressed basil plants. Ocimum basilicum “Genovese” was grown in a growth chamber under well-watered control [45–50% volumetric water content (VWC)], moderate salinity stress (100 mM NaCl), severe salinity stress (200 mM NaCl), moderate drought stress (25–30% VWC), and severe drought stress (15–20% VWC). Phenotypic traits were measured for 3 weeks in 7-day intervals. Automated phenotyping techniques were able to detect basil responses to early and prolonged salinity and drought stress. In addition, several phenotypic traits were able to differentiate among salinity and drought. At early stages, low anthocyanin index (ARI), chlorophyll index (CHI), and hue (HUE2D), and higher reflectance in red (RRed), reflectance in green (RGreen), and leaf inclination (LINC) indicated drought stress. At later stress stages, maximum fluorescence (Fm), HUE2D, normalized difference vegetation index (NDVI), and LINC contribute the most to the differentiation among drought and non-stressed as well as among drought and salinity stressed plants. ARI and electron transport rate (ETR) were best for differentiation of salinity stressed plants from non-stressed plants both at early and prolonged stress.

scholarly journals Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

2021 ◽  
Vol 13 (4) ◽  
pp. 598
Author(s):  
Daniel O. Wasonga ◽  
Afrane Yaw ◽  
Jouko Kleemola ◽  
Laura Alakukku ◽  
Pirjo S.A. Mäkelä
Keyword(s):  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
High Energy ◽  
Plant Performance ◽  
Estimation Accuracy ◽  
Management Decisions ◽  
Lower Estimation ◽  
Low K

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.

scholarly journals ОЦЕНКА УРОВНЯ ОЗЕЛЕНЕНИЯ ГОРОДА БИРОБИДЖАНА С ПРИМЕНЕНИЕМ МУЛЬТИСПЕКТРАЛЬНЫХ ДАННЫХ

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Д.М. Фетисов ◽  
Д.В. Жучков ◽  
М.В. Горюхин
Keyword(s):  
Green Infrastructure ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Natural Vegetation ◽  
Medium Size ◽  
Functional Zones ◽  
Functional Areas ◽  
The City ◽  
And Storage

The urban greenness distribution between functional areas of a medium-size city Birobidzhan was assessed. To this end, normalized difference vegetation index (NDVI) values were calculated based on Sentinel 2 multispectral imaging. Birobidzhan is characterized by a large scatter of NDVI values (from –0.5 to +1). Areas with high levels of greenery are prevalent. They are found in different types of functional zones, but are specific mainly to natural recreational, agricultural, and individual build-up zones as well as to special areas. The spatial distribution of green infrastructure is highly contrast. The downtown part as well as the industrial and storage zones feature a combination of built-up areas with dense woody vegetation, which is often represented by fragments of preserved natural vegetation. In addition, a feature of the contrast is that low level of tree greenness is characteristic for the built-up districts of the city. Thus, in the city of Birobidzhan, ecological functions are largely performed by the natural vegetation present in the natural recreational zones on 70% of the city's area.

Generalization of Vegetation Indices for Monitoring the Terrestrial Biosphere

2021 ◽  
Author(s):  
Gustau Camps-Valls ◽  
Manuel Campos-Taberner ◽  
Alvaro Moreno-Martinez ◽  
Sophia Walther ◽  
Grégory Duveiller ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Observation Data ◽  
Terrestrial Biosphere ◽  
Time Resolved ◽  
Area Index ◽  
Nonlinear Generalization

<p>Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data.</p><p><span> Reference: <strong>"<span>A Unified Vegetation Index for Quantifying the Terrestrial Biosphere</span>"</strong>, </span><span>Gustau Camps-Valls, Manuel Campos-Taberner, Álvaro Moreno-Martı́nez, Sophia Walther, Grégory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Muñoz-Marı́, Francisco Javier Garcı́a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. </span><em><span><span>Science Advances, in press</span></span><span>, </span> <span>2021</span> </em></p>

scholarly journals Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018

2021 ◽  
Vol 13 (13) ◽  
pp. 2522
Author(s):  
Lkhagvadorj Nanzad ◽  
Jiahua Zhang ◽  
Battsetseg Tuvdendorj ◽  
Shanshan Yang ◽  
Sonam Rinzin ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystems ◽  
Severe Drought ◽  
Sparse Vegetation ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Boreal Ecosystem ◽  
The Impact

Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.

scholarly journals QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

2020 ◽  
Author(s):  
Rasyidah Mohamad Razar ◽  
Ali Missaoui
Keyword(s):  
Qtl Mapping ◽  
Panicum Virgatum ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Yield ◽  
Pearson Correlation ◽  
Biomass Accumulation ◽  
Phenotypic Traits ◽  
Winter Dormancy ◽  
Phenotypic Data

Abstract Background Switchgrass (Panicum virgatum) undergoes seasonal growth changes based on the perception of photoperiod and temperature. It transitions to winter dormancy in early fall, breaks dormancy in the spring, and resume the cycle starting from flowering which is the cue for senescence. The length of growing season can impact the biomass accumulation and yield. In this study, we conducted QTL mapping of winter dormancy measured by fall regrowth height (FRH) and normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two bi-parental pseudo-F1 populations derived from crosses between the lowland AP13 with the lowland B6 (AB) with 285 progenies, and the lowland B6 with the upland VS16 (BV) with 227 progenies. Methods Pearson correlation coefficient between phenotypic traits was calculated to determine if some traits can be as surrogates for other traits. Broad-sense heritabilities were calculated to determine the inheritance, and QTL mapping was conducted for two-years phenotypic data to identify QTLs associated with the trais. Results We identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. The ranges of percent variance explained by these QTLs varied between 4.21–23.27% for FRH, 4.47–24.06% for NDVI, 4.35–32.77% for SE, and 4.61–29.74% for FD. A higher number of QTL was discovered in the BV population, suggesting more variants in the lowland x upland population contributing to the expression of seasonal dormancy underlying traits. We identified 9 regions of colocalized QTL with possible pleiotropic gene action. The positive correlation between FRH or NDVI with dry biomass weight suggests that winter dormancy level could affect switchgrass biomass yield. The medium to high heritability levels of FRH (0.55–0.64 H2) and NDVI (0.30–0.61 H2) support the possibility of using the traits for indirect selection for biomass yield. Conclusion Markers found within the significant QTL interval can serve as genomic resource for breeding non-dormant and semi-dormant switchgrass cultivars for the southern regions, where growers can benefit from the longer production season.

scholarly journals Identificación de patrones relevantes a la sequía agrícola a partir del análisis espacial y temporal del Índice de Condición de la Vegetación – Caso estudio: Áreas agrícolas de la región Piura, Perú (2000 - 2017)

2018 ◽  
pp. 81-89
Keyword(s):  
Spatial Patterns ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Condition Index ◽  
Agricultural Drought ◽  
Sensor Data ◽  
Severe Drought ◽  
Dry Land ◽  
Growth State ◽  
Agricultural Areas

Identificación de patrones relevantes a la sequía agrícola a partir del análisis espacial y temporal del Índice de Condición de la Vegetación – Caso estudio: Áreas agrícolas de la región Piura, Perú (2000 - 2017) Gisell Carbajal1, Bram Willems1,2 y Waldo Lavado3 1 Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Ap. Postal 14-0149, Lima, Perú 2 Centro de Competencias del Agua, Jr. Bolognesi 150 A, 303, San Miguel, Lima, Perú 3 Servicio Nacional de Meteorología e Hidrología del Perú, Jr. Cahuide 785 Jesús María, Lima 11 – Perú Recibido el 19 de noviembre del 2018. Revisado el 9 de diciembre del 2018. Aceptado el 10 de diciembre del 2018 DOI: https://doi.org/10.33017/RevECIPeru2018.0013/ Resumen En el presente trabajo se analiza la evolución espacial y temporal del Índice de Condición de la Vegetación (ICV), con el propósito de identificar patrones relevantes a la ocurrencia de eventos de sequía agrícola en Piura. El ICV provee información acerca del estado de crecimiento de la vegetación durante situaciones extremas, y se deriva del producto: valores del Índice de Vegetación de Diferencia Normalizada (NDVI) - datos del sensor MODIS (Espectrorradiómetro de Imagen de Resolución Moderada) a una resolución espacial de 1 km en el periodo 2000-2017 a bordo del satélite Terra (MOD13A3, versión 6) obtenida en su paso diurno entre las 10:30 horas y las 12:00 horas (hora local). Los patrones espaciales del ICV revelan que, para el caso de las áreas agrícolas de secano, en el 2004, el 21 % presentaron condiciones de sequía extrema y severa, mientras que en el 2007 fue el 19,5 %, el 2011 el 15,5 % y el 2014 llegó al 21 %. Por otro lado, para el caso de las áreas agrícolas por regadío, en el 2004 se vieron afectadas el 44,2 %, el 2005 fue el 55,4 %, el 2007 fue el 38,8 %, el 2011 fue el 17,1 % y el 2014 fue el 37,1 %. Descriptores: Sequía, patrones espaciales, áreas agrícolas, secano, regadío, ICV Abstract The present work, the spatial and temporal evolution of the Vegetation Condition Index (VCI) is analyzed, with the purpose of identifying patterns relevant to the occurrence of agricultural drought events in Piura. The VCI provides information about the growth state of the vegetation during extreme situations, and it is derived from the product: Normalized Difference Vegetation Index (NDVI) values - MODIS (Moderate-Resolution Imaging Spectroradiometer) sensor data at a spatial resolution of 1 km in the period 2000-2017 on board the Terra satellite (MOD13A3, version 6) Obtained in its passage between 10:30 am and 12:00 pm (local time). The spatial patterns of the VCI reveal that, in the case of rainfed agricultural areas, in 2004, 21 % presented extreme and severe drought conditions, while in 2007 it was 19.5 %, in 2011 the 15.5 % and 2014 reached 21 %. On the other hand, in the case of irrigated agricultural areas, 44.2 % were affected in 2004, 55.4 % in 2005, 38.8 % in 2007, 17.1 % in 2011 and 37.1 % in 2014. Keywords: Drought, spatial patterns, agricultural areas, dry land, irrigated land, ICV

scholarly journals Salinity Tolerance and Recovery Attributes of Warm-season Turfgrass Cultivars

HortScience ◽  
2019 ◽  
Vol 54 (9) ◽  
pp. 1625-1631 ◽  
Author(s):  
Manuel Chavarria ◽  
Benjamin Wherley ◽  
James Thomas ◽  
Ambika Chandra ◽  
Paul Raymer
Keyword(s):  
Salinity Stress ◽  
Salinity Tolerance ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Recovery Period ◽  
Warm Season ◽  
Shoot Biomass ◽  
Seashore Paspalum ◽  
Turf Quality ◽  
Elevated Salinity

As population growth places greater pressures on potable water supplies, nonpotable recycled irrigation water is becoming widely used on turfgrass areas including golf courses, sports fields, parks, and lawns. Nonpotable recycled waters often have elevated salinity levels, and therefore turfgrasses must, increasingly, have good salinity tolerance to persist in these environments. This greenhouse study evaluated 10 commonly used cultivars representing warm-season turfgrass species of bermudagrass (Cynodon spp.), zoysiagrass (Zoysia spp.), st. augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze], and seashore paspalum (Paspalum vaginatum Swartz) for their comparative salinity tolerance at electrical conductivity (EC) levels of 2.5 (control), 15, 30, and 45 dS·m–1. Salinity treatments were imposed on the grasses for 10 weeks via subirrigation, followed by a 4-week freshwater recovery period. Attributes, including turf quality, the normalized difference vegetation index (NDVI), canopy firing, and shoot biomass reductions were evaluated before and after salinity stress, as well as after the 4-week freshwater recovery period. Results showed considerable differences in salinity tolerance among the cultivars and species used, with the greatest tolerance to elevated salinity noted within seashore paspalum cultivars and Celebration® bermudagrass. In comparison with growth in 2.5-dS·m–1 control conditions, increased shoot growth and turf quality were noted for many bermudagrass and seashore paspalum cultivars at 15 dS·m–1. However, st. augustinegrass and some zoysiagrass cultivars responded to elevated salinity with decreased growth and turf quality. No cultivars that had been exposed to 30- or 45-dS·m–1 salinity recovered to acceptable levels, although bermudagrass and seashore paspalum recovered to acceptable levels after exposure to 15-dS·m–1 salinity. More severe salinity stress was noted during year 2, which coincided with greater greenhouse temperatures relative to year 1.

scholarly journals Multispectral Imaging with Raspberry Pi for Assessment of Plant Health Status

2019 ◽  
Vol 1 (2) ◽  
pp. 30
Author(s):  
Jan Lukas Bosse ◽  
Marcelinus A. S. Adhiwibawa ◽  
Tatas H.P. Brotosudarmo
Keyword(s):  
Health Status ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Chlorophyll Concentration ◽  
Plant Health ◽  
Magnesium Concentration ◽  
Raspberry Pi ◽  
Major Shortcoming ◽  
Highly Correlated

Non-destructive measurement of plant chlorophyll concentration using the normalized difference vegetation index (NDVI) has long been standard practice to determine the plant health status. This is, because the NDVI value is correlated with the chlorophyll concentration which in turn is highly correlated with other vital plant parameters such as nitrogen and magnesium concentration. Initially the NDVI values were obtained from satellite imagery and thus could only be used to assess the health status of bigger ecosystems like forests and crop fields. With the introduction of handheld chlorophyll meters like the Chlorophyll Meter SPAD-502 Plus made by Konica Minolta, the same principle could be used to determine the chlorophyll concentration of single leaves. However, these devices still have one major shortcoming: They can only measure the chlorophyll concentration on one single spot on the leaf at a time. But depending on the species the chlorophyll concentration tends to vary significantly over the leaf. To overcome this shortcoming, we developed our PlantAnalyzer which offers better spatial resolution of the NDVI values and hence the chlorophyll concentration. Its technical realization and precision shall be elaborated in the following article.

scholarly journals Carbon Isotope Composition and the NDVI as Phenotyping Approaches for Drought Adaptation in Durum Wheat: Beyond Trait Selection

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1679
Author(s):  
Rut Sanchez-Bragado ◽  
Maria Newcomb ◽  
Fadia Chairi ◽  
Giuseppe Emanuele Condorelli ◽  
Richard W. Ward ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Carbon Isotope ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Isotope Composition ◽  
Water Status ◽  
Carbon Isotope Composition ◽  
Phenotypic Traits ◽  
Drought Adaptation ◽  
Leaf Level

High-throughput phenotyping platforms provide valuable opportunities to investigate biomass and drought-adaptive traits. We explored the capacity of traits associated with drought adaptation such as aerial measurements of the Normalized Difference Vegetation Index (NDVI) and carbon isotope composition (δ13C) determined at the leaf level to predict genetic variation in biomass. A panel of 248 elite durum wheat accessions was grown at the Maricopa Phenotyping platform (US) under well-watered conditions until anthesis, and then irrigation was stopped and plot biomass was harvested about three weeks later. Globally, the δ13C values increased from the first to the second sampling date, in keeping with the imposition of progressive water stress. Additionally, δ13C was negatively correlated with final biomass, and the correlation increased at the second sampling, suggesting that accessions with lower water-use efficiency maintained better water status and, thus, performed better. Flowering time affected NDVI predictions of biomass, revealing the importance of developmental stage when measuring the NDVI and the effect that phenology has on its accuracy when monitoring genotypic adaptation to specific environments. The results indicate that in addition to choosing the optimal phenotypic traits, the time at which they are assessed, and avoiding a wide genotypic range in phenology is crucial.

scholarly journals The Combined Effect of Drought Stress and Nitrogen Fertilization on Soybean

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 384
Author(s):  
Oqba Basal ◽  
András Szabó
Keyword(s):  
Drought Stress ◽  
Plant Height ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Seed Quality ◽  
Yield Component ◽  
Stress Conditions ◽  
Irrigation Regimes ◽  
Pod Number ◽  
Plant Plant

Soybean is one of the most important crops worldwide; however, its production and produced seed quality are challenged by the increasingly reported drought waves because of its relative susceptibility to drought stress conditions. Nitrogen (N) is a major macronutrient that has distinctive influence on soybean, especially if applied in correct rates. Moreover, N has an additive importance under drought stress conditions. An experiment was carried out in Debrecen, Hungary in 2017, 2018, and 2019 to investigate the sole and the combined effects of N application under different irrigation regimes on soybean physiology, yield, and its components in addition to the quality of the produced yield. Results showed that the morpho-physiological traits, in addition to the yield component traits were influenced by both fertilization rates and irrigation regimes. Most importantly, high N rate is not recommended with the absence of drought conditions as, compared to low rate, it decreased flower and pod number per plant, plant height, and seed yield. On the other hand, high N rate enhanced most traits under drought stress conditions. 100-seed weight had the highest correlation with yield, followed by flower and pod number per plant, plant height, and Normalized Difference Vegetation Index (NDVI).

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