scholarly journals Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

2019 ◽  
Vol 11 (10) ◽  
pp. 1192 ◽  
Author(s):  
Nianxu Xu ◽  
Jia Tian ◽  
Qingjiu Tian ◽  
Kaijian Xu ◽  
Shaofei Tang
Keyword(s):  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Bare Soil ◽  
Vegetation Canopy ◽  
Vegetation Monitoring ◽  
Red Edge ◽  
Positive Correlations

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

2021 ◽  
Vol 18 (22) ◽  
pp. 6077-6091
Author(s):  
Trina Merrick ◽  
Stephanie Pau ◽  
Matteo Detto ◽  
Eben N. Broadbent ◽  
Stephanie A. Bohlman ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Canopy Structure ◽  
Temporal Heterogeneity ◽  
Spatial And Temporal Heterogeneity ◽  
And Function ◽  
Tropical Forest Canopy

Abstract. Recently, remotely sensed measurements of the near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation have emerged as indicators of vegetation structure and function with potential to enhance or improve upon commonly used indicators, such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI). The applicability of these remotely sensed indices to tropical forests, key ecosystems for global carbon cycling and biodiversity, has been limited. In particular, fine-scale spatial and temporal heterogeneity of structure and physiology may contribute to variation in these indices and the properties that are presumed to be tracked by them, such as gross primary productivity (GPP) and absorbed photosynthetically active radiation (APAR). In this study, fine-scale (approx. 15 cm) tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad and by lidar-derived height is investigated and compared to NIRv and EVI using unoccupied aerial system (UAS)-based hyperspectral and lidar sensors. By exploiting near-infrared signals, NIRv, FCVI, and NIRvrad captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI) and then the normalized difference vegetation index (NDVI). Wavelet analyses showed the dominant spatial scale of variability of all indicators was driven by tree clusters and larger-than-tree-crown size gaps rather than individual tree crowns. NIRv, FCVI, NIRvrad, and EVI captured variability at smaller spatial scales (∼ 50 m) than NDVI (∼ 90 m) and the lidar-based surface model (∼ 70 m). We show that spatial and temporal patterns of NIRv and FCVI were virtually identical for a dense green canopy, confirming predictions in earlier studies. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated more strongly with GPP and PAR than did other indicators. NIRv, FCVI, and NIRvrad, which are related to canopy structure and the radiation regime of vegetation canopies, are promising tools to improve understanding of tropical forest canopy structure and function.

scholarly journals USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS

2016 ◽  
Vol XLI-B8 ◽  
pp. 711-718 ◽  
Author(s):  
R. Minařík ◽  
J. Langhammer
Keyword(s):  
Bark Beetle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Forest Disturbance ◽  
Methodological Approach ◽  
Detection And Tracking ◽  
Red Edge ◽  
Tree Stress ◽  
Simple Ratio

This study presents a new methodological approach for assessment of spatial and qualitative aspects of forest disturbance based on the use of multispectral imaging camera with the UAV photogrammetry. We have used the miniaturized multispectral sensor Tetracam Micro Multiple Camera Array (μ-MCA) Snap 6 with the multirotor imaging platform to get multispectral imagery with high spatial resolution. The study area is located in the Sumava Mountains, Central Europe, heavily affected by windstorms, followed by extensive and repeated bark beetle (Ips typographus [L.]) outbreaks in the past 20 years. After two decades, there is apparent continuous spread of forest disturbance as well as rapid regeneration of forest vegetation, related with changes in species and their diversity. For testing of suggested methodology, we have launched imaging campaign in experimental site under various stages of forest disturbance and regeneration. The imagery of high spatial and spectral resolution enabled to analyse the inner structure and dynamics of the processes. The most informative bands for tree stress detection caused by bark beetle infestation are band 2 (650nm) and band 3 (700nm), followed by band 4 (800 nm) from the, red-edge and NIR part of the spectrum. We have identified only three indices, which seems to be able to correctly detect different forest disturbance categories in the complex conditions of mixture of categories. These are Normalized Difference Vegetation Index (NDVI), Simple 800/650 Ratio Pigment specific simple ratio B1 and Red-edge Index.

scholarly journals Unpiloted Aerial System (UAS)-Supported Biogeomorphic Analysis of Restored Sierra Nevada Montane Meadows

2020 ◽  
Vol 12 (11) ◽  
pp. 1828
Author(s):  
Jerry Davis ◽  
Leonhard Blesius ◽  
Michelle Slocombe ◽  
Suzanne Maher ◽  
Michael Vasey ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Cross Sections ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Survey Methods ◽  
Vegetation Indices ◽  
Field Studies ◽  
Red Edge ◽  
Montane Meadows

The benefits of meadow restoration can be assessed by understanding the connections among geomorphology, hydrology, and vegetation; and multispectral imagery captured from unpiloted aerial systems (UASs) can provide the best method in terms of cost, resolution, and support for vegetation indices. Our field studies were conducted on northern Sierra montane meadows (with ≤70 km2 watershed area). The meadows exist in various stages of ecological restoration. Field survey methods included GPS + laser-leveling channel survey, cross-sections, LiDAR, vegetation sampling, soil measurements, and UAS imaging. A sensor captured calibrated blue (465–485 nm), green (550–570 nm), red (663–673 nm), near infrared (NIR) (820–860 nm), and red-edge (712–722 nm) bands at 5.5 cm resolution (as well as thermal at 81 cm resolution) and provided multispectral images and derivative vegetation indices such as the normalized difference vegetation index (NDVI) and red-edge chlorophyll index (Clre). This fine-scale imagery extended our morphometric assessment of post-restoration channel bedform patterns and sinuosity related to Carex-influenced soil properties and Salix influence, and also documented groundwater-related effects via Carex patterns evident from spring snowmelt images, as well as NDVI and Clre (derived from spring and summer images) in growing to senescent phenological stages. Carex was significantly associated with low bulk density and high soil moisture, NDVI, and Clre in low-lying areas, and channel sinuosity was significantly associated with willow influence. Our methods can be applied by restoration managers to assess where projects are threatened by renewed incision and to document levels of carbon sequestration significant to addressing climate change.

scholarly journals Experimental Evaluation and Consistency Comparison of UAV Multispectral Minisensors

2020 ◽  
Vol 12 (16) ◽  
pp. 2542
Author(s):  
Han Lu ◽  
Tianxing Fan ◽  
Prakash Ghimire ◽  
Lei Deng
Keyword(s):  
Precision Agriculture ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Change Analysis ◽  
Vegetation Monitoring ◽  
Ground Point ◽  
Highly Correlated

In recent years, the use of unmanned aerial vehicles (UAVs) has received increasing attention in remote sensing, vegetation monitoring, vegetation index (VI) mapping, precision agriculture, etc. It has many advantages, such as high spatial resolution, instant information acquisition, convenient operation, high maneuverability, freedom from cloud interference, and low cost. Nowadays, different types of UAV-based multispectral minisensors are used to obtain either surface reflectance or digital number (DN) values. Both the reflectance and DN values can be used to calculate VIs. The consistency and accuracy of spectral data and VIs obtained from these sensors have important application value. In this research, we analyzed the earth observation capabilities of the Parrot Sequoia (Sequoia) and DJI Phantom 4 Multispectral (P4M) sensors using different combinations of correlation coefficients and accuracy assessments. The research method was mainly focused on three aspects: (1) consistency of spectral values, (2) consistency of VI products, and (3) accuracy of normalized difference vegetation index (NDVI). UAV images in different resolutions were collected using these sensors, and ground points with reflectance values were recorded using an Analytical Spectral Devices handheld spectroradiometer (ASD). The average spectral values and VIs of those sensors were compared using different regions of interest (ROIs). Similarly, the NDVI products of those sensors were compared with ground point NDVI (ASD-NDVI). The results show that Sequoia and P4M are highly correlated in the green, red, red edge, and near-infrared bands (correlation coefficient (R2) > 0.90). The results also show that Sequoia and P4M are highly correlated in different VIs; among them, NDVI has the highest correlation (R2 > 0.98). In comparison with ground point NDVI (ASD-NDVI), the NDVI products obtained by both of these sensors have good accuracy (Sequoia: root-mean-square error (RMSE) < 0.07; P4M: RMSE < 0.09). This shows that the performance of different sensors can be evaluated from the consistency of spectral values, consistency of VI products, and accuracy of VIs. It is also shown that different UAV multispectral minisensors can have similar performances even though they have different spectral response functions. The findings of this study could be a good framework for analyzing the interoperability of different sensors for vegetation change analysis.

scholarly journals Spectral Reflectance in Silver Birch Genotypes from Three Provenances in Finland

2020 ◽  
Vol 12 (17) ◽  
pp. 2677
Author(s):  
Maya Deepak ◽  
Sarita Keski-Saari ◽  
Laure Fauch ◽  
Lars Granlund ◽  
Elina Oksanen ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Chlorophyll Content ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Leaf Traits ◽  
Silver Birch ◽  
Red Edge

The goal of this study was to investigate the variation in the leaf spectral reflectance and its association with other leaf traits from 12 genotypes among three provenances of origin (populations) in a common garden for Finnish silver birch trees in 2015 and 2016. The spectral reflectance was measured in the laboratory from the detached leaves in the wavelength range of visible and near-infrared (VNIR, 400–1000 nm) and shortwave infrared (SWIR, 1000–2500 nm). The variation among the provenance was initially visualized with principal component analysis (PCA) and a clear separation among the provenances was detected with the discriminant analysis of principal components (DAPC) and partial least squares discriminant analysis (PLS-DA) depicting a less strong variation among the genotypes within the provenances. Wavelengths contributing to the separation of the genotypes and provenances were identified from the contribution plot of DAPC and the red edge was strongly related to the differences. Chlorophyll content showed clear provenance variation and was associated with the separation among the genotypes and provenances in the DAPC space. The normalized difference vegetation index (NDVI705,750) and chlorophyll reflectance index (CRI) showed clear significance among the provenances, whereas NDVI670,780 showed no variation. The variation in the chlorophyll content and the CRI and red edge-based NDVI indices indicated seasonal variation as the chlorophyll content starts increasing in early June. The correlation of foliar chlorophyll content and the chlorophyll-related spectral indices for the discrimination of provenances and genotypes are reported for the first time in a naturally occurring tree species consecutively for two years.

scholarly journals A Comparison of UAV RGB and Multispectral Imaging in Phenotyping for Stay Green of Wheat Population

2021 ◽  
Vol 13 (24) ◽  
pp. 5173
Author(s):  
Xiaofeng Cao ◽  
Yulin Liu ◽  
Rui Yu ◽  
Dejun Han ◽  
Baofeng Su
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Wheat Yield ◽  
Grain Filling ◽  
High Yield ◽  
Infrared Band ◽  
Stay Green ◽  
Wheat Genotypes ◽  
Red Edge

High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.

scholarly journals Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV

2019 ◽  
Vol 11 (9) ◽  
pp. 993 ◽  
Author(s):  
Fernando Carvajal-Ramírez ◽  
José Rafael Marques da Silva ◽  
Francisco Agüera-Vega ◽  
Patricio Martínez-Carricondo ◽  
João Serrano ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Fire Severity ◽  
Matrix Analysis ◽  
Surface Model ◽  
Chi Square ◽  
Error Matrix ◽  
Red Edge ◽  
The Impact

Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications.

scholarly journals USE OF A MULTISPECTRAL UAV PHOTOGRAMMETRY FOR DETECTION AND TRACKING OF FOREST DISTURBANCE DYNAMICS

2016 ◽  
Vol XLI-B8 ◽  
pp. 711-718 ◽  
Author(s):  
R. Minařík ◽  
J. Langhammer
Keyword(s):  
Bark Beetle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Forest Disturbance ◽  
Methodological Approach ◽  
Detection And Tracking ◽  
Red Edge ◽  
Tree Stress ◽  
Simple Ratio

This study presents a new methodological approach for assessment of spatial and qualitative aspects of forest disturbance based on the use of multispectral imaging camera with the UAV photogrammetry. We have used the miniaturized multispectral sensor Tetracam Micro Multiple Camera Array (μ-MCA) Snap 6 with the multirotor imaging platform to get multispectral imagery with high spatial resolution. The study area is located in the Sumava Mountains, Central Europe, heavily affected by windstorms, followed by extensive and repeated bark beetle (Ips typographus [L.]) outbreaks in the past 20 years. After two decades, there is apparent continuous spread of forest disturbance as well as rapid regeneration of forest vegetation, related with changes in species and their diversity. For testing of suggested methodology, we have launched imaging campaign in experimental site under various stages of forest disturbance and regeneration. The imagery of high spatial and spectral resolution enabled to analyse the inner structure and dynamics of the processes. The most informative bands for tree stress detection caused by bark beetle infestation are band 2 (650nm) and band 3 (700nm), followed by band 4 (800 nm) from the, red-edge and NIR part of the spectrum. We have identified only three indices, which seems to be able to correctly detect different forest disturbance categories in the complex conditions of mixture of categories. These are Normalized Difference Vegetation Index (NDVI), Simple 800/650 Ratio Pigment specific simple ratio B1 and Red-edge Index.

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.

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