scholarly journals Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing

2022 ◽  
Vol 12 (1) ◽  
pp. 458
Author(s):  
Julyanne Braga Cruz Amaral ◽  
Fernando Bezerra Lopes ◽  
Ana Caroline Messias de Magalhães ◽  
Sebastian Kujawa ◽  
Carlos Alberto Kenji Taniguchi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Block Design ◽  
Nutrient Content ◽  
Single Band ◽  
Growth Stages ◽  
Least Squares Regression ◽  
Phenological Stages ◽  
Band Ratio ◽  
Remote Sensing Techniques ◽  
Using Data

Although hyperspectral remote sensing techniques have increasingly been used in the nutritional quantification of plants, it is important to understand whether the method shows a satisfactory response during the various phenological stages of the crop. The aim of this study was to quantify the levels of phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) in the leaves of Vigna Unguiculata (L.) Walp using spectral data obtained by a spectroradiometer. A randomised block design was used, with three treatments and twenty-five replications. The crop was evaluated at three growth stages: V4, R6 and R9. Single-band models were fitted using simple correlations. For the band ratio models, the wavelengths were selected by 2D correlation. For the models using partial least squares regression (PLSR), the stepwise method was used. The model showing the best fit was used to estimate the phosphorus content in the single-band (R² = 0.62; RMSE = 0.54 and RPD = 1.61), band ratio (R² = 0.66; RMSE = 0.65 and RPD = 1.52) and PLSR models, using data from each of the phenological stages (R² = 0.80; RMSE = 0.47 and RPD = 1.66). Accuracy in modelling leaf nutrients depends on the phenological stage, as well as the amount of data used, and is more accurate with a larger number of samples.

scholarly journals Effect of Different Levels of Phosphorus, Phosphorus Solubilising Bacteria and Farmyard Manure on Nutrient Content and Uptake by Bt-Cotton

2021 ◽  
pp. 17-27
Author(s):  
K. V. S. L. Raj Rushi ◽  
P. Prasuna Rani ◽  
P. R. K. Prasad ◽  
P. Anil Kumar
Keyword(s):  
Block Design ◽  
Farmyard Manure ◽  
Nutrient Content ◽  
Inorganic Phosphorus ◽  
Phosphorus Fertilization ◽  
Growth Stages ◽  
Bt Cotton ◽  
Constant Weight ◽  
Plant Samples ◽  
Randomized Block Design

Aim: To determine the effect of integrated use of inorganic phosphorus along with phosphorus solubilising bacteria and farmyard manure on nutrient Content and uptake in Bt-Cotton. Study Design: The experiment was laid out in randomized block design with three replications. Place of Study: At College Farm, Agricultural College, Bapatla, Guntur district. Methodology: After the preliminary layout, the Tulasi-BG II hybrid of cotton was used as a test crop, with a spacing of 90 cm x 60 cm in the experimental site. Plant samples were collected at 45, 90 DAS, and harvest. Plant samples were shade dried and kept in hot air oven at 75°C until a constant weight was obtained. Samples were powdered and then analysed using standard chemical procedures. Results: The uptake of macronutrients viz., N, P, and K was markedly influenced by the treatments with maximum values recorded by the treatment that received 60 kg P2O5 ha-1 + FYM + PSB. Their uptake significantly differed with increase in dose of phosphorus applied at all growth stages. Combined use of 60 kg P2O5 ha-1 + FYM + PSB resulted in a significant increase in uptake of all micronutrients (Zn, Cu, Mn, and Fe) at all stages except Cu at 90 DAS. Conclusion: Application of phosphorus combined with FYM and PSB not only reduce inorganic phosphorus fertilization but also enhance the uptake of nutrients and plant nutrient content by providing favourable conditions for plant growth. Application of 30 kg P2O5 ha-1+ PSB + FYM was found to be on a par with addition of only 60 kg ha-1P2O5. Hence the P dose can be reduced to half by integrating with PSB and FYM.

scholarly journals Monitoring Beach Topography and Nearshore Bathymetry Using Spaceborne Remote Sensing: A Review

2019 ◽  
Vol 11 (19) ◽  
pp. 2212 ◽  
Author(s):  
Edward Salameh ◽  
Frédéric Frappart ◽  
Rafael Almar ◽  
Paulo Baptista ◽  
Georg Heygster ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Frequency ◽  
Anthropogenic Pressure ◽  
Fundamental Parameters ◽  
Temporal Sampling ◽  
Current State ◽  
Needed Information ◽  
Spatial Coverage ◽  
Remote Sensing Techniques ◽  
Using Data

With high anthropogenic pressure and the effects of climate change (e.g., sea level rise) on coastal regions, there is a greater need for accurate and up-to-date information about the topography of these systems. Reliable topography and bathymetry information are fundamental parameters for modelling the morpho-hydrodynamics of coastal areas, for flood forecasting, and for coastal management. Traditional methods such as ground, ship-borne, and airborne surveys suffer from limited spatial coverage and temporal sampling due to logistical constraints and high costs which limit their ability to provide the needed information. The recent advancements of spaceborne remote sensing techniques, along with their ability to acquire data over large spatial areas and to provide high frequency temporal monitoring, has made them very attractive for topography and bathymetry mapping. In this review, we present an overview of the current state of spaceborne-based remote sensing techniques used to estimate the topography and bathymetry of beaches, intertidal, and nearshore areas. We also provide some insights about the potential of these techniques when using data provided by new and future satellite missions.

scholarly journals Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments

2020 ◽  
Vol 12 (18) ◽  
pp. 2867 ◽  
Author(s):  
K. Colton Flynn ◽  
Amy E. Frazier ◽  
Sintayehu Admas
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Partial Least Squares ◽  
Nutrient Content ◽  
Hyperspectral Data ◽  
Eragrostis Tef ◽  
Least Squares Regression ◽  
Agricultural Field ◽  
Scientific Results ◽  
Study Sites

Achieving reproducibility and replication (R&R) of scientific results is tantamount for science to progress, and it is also necessary for ensuring the self-correcting mechanism of the scientific method. Topics of R&R have sailed to the forefront of research agenda in many fields recently but have received less attention in remote sensing in general and specifically for studies utilizing hyperspectral data. Given the extremely local environments in which many hyperspectral studies are conducted (e.g., agricultural field plots), purposeful attention to the repeatability of findings across study locales can help ensure methods are generalizable. This study undertakes an investigation of the nutrient content of tef (Eragrostis tef), an understudied plant that is growing in importance due to both food and forage benefits, but does so within the context of the replicability of methods and findings across two study sites situated in different international and environmental contexts. The aims are to (1) determine whether calcium, magnesium, and protein of both the plant and grain can be predicted using hyperspectral data with partial least squares (PLS) regression with waveband selection, and (2) compare the replicability of models across differing environments. Results suggest the method can produce high nutrient prediction accuracy for both the plant and grain in individual environments, but selection of wavebands for nutrient prediction was not comparable across study areas. The findings suggest that the method must be calibrated in each location, thereby reducing the potential to extrapolate methods to different areas. Our findings highlight the need for greater attention to methods and results replication in remote sensing, specifically hyperspectral analyses, in order for scientific findings to be repeatable beyond the plot level.

Remote Sensing as an Aid to the Inventory and Management of Natural Resources

1978 ◽  
Vol 32 (2) ◽  
pp. 183-203
Author(s):  
Robert N. Colwell
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Fossil Fuels ◽  
The United States ◽  
Agricultural Crops ◽  
Nonrenewable Resources ◽  
Remotely Sensed Data ◽  
Remote Sensing Techniques ◽  
Using Data ◽  
Work Done

An analysis is given of the extent to which modern remote-sensing techniques might be used to facilitate the inventory and management of such renewable natural resources as timber, forage, and agricultural crops and of such nonrenewable resources as minerals and fossil fuels. The first part of the paper seeks to clarify both the terms and the concepts that are applicable to the fast growing field of remote sensing. This is followed by a discussion of the various basic considerations that enter into the acquisition and analysis of remotely sensed data. There is an analysis of both the feasibility and the desirability of using data acquired by LANDSAT and other remote-sensing vehicles in the making of globally uniform inventories of various kinds of natural resources. There follows a tabulation of recent and representative applications and the citing of various references in which additional examples are fully described and well illustrated with remote-sensing imagery. Although the paper may appear to be justifiably optimistic, it concludes with some words of caution on the difficulties that can arise whenever there is an overstatement of remote-sensing capabilities and an understatement of remote-sensing limitations. The numerous specific examples of LANDSAT applications that are given in this paper pertain primarily to work done in Canada and the United States.

scholarly journals Assessment of Rice Panicle Blast Disease Using Airborne Hyperspectral Imagery

2016 ◽  
Vol 10 (1) ◽  
pp. 28-34 ◽  
Author(s):  
Takashi Kobayashi ◽  
Masashi Sasahara ◽  
Eiji Kanda ◽  
Kiyoshi Ishiguro ◽  
Shu Hase ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imagery ◽  
Disease Incidence ◽  
Blast Disease ◽  
Rice Grain ◽  
Large Area ◽  
Potential Yield ◽  
Band Ratio ◽  
Panicle Blast ◽  
Remote Sensing Techniques

Rice blast disease occurs in rice production areas all over the world and is the most important disease in Japan. Remote sensing techniques may provide a mean for detecting disease intensity for large area without being subjected to raters. This study evaluated the use of airborne hyperspectral imagery to measure the severity of panicle blast in field crops. Hyperspectral remote sensing imagery was acquired at the dough stage of rice grain development in northern Japan. The most consistent relationship, with high R2 and low P, was the simple band ratio R498 to 515/R700 to 717 (i.e., the reflectance at 498 to 515-nm divided by the reflectance at 700- to 717-nm). The band ratio of R498 to 515/R700 to 717 increased significantly (P < 0.001) with increasing visual estimates of disease incidence, defined as the percentage of diseased spikelets (R2 = 0.83). Assessment of disease distribution and severity could provide useful information for making decisions regarding the necessity of fungicide application and estimate potential yield loss due to the disease.

scholarly journals MAPPING GLAUCONITE UNITES WITH USING REMOTE SENSING TECHNIQUES IN NORTH EAST OF IRAN

2014 ◽  
Vol XL-2/W3 ◽  
pp. 7-11 ◽  
Author(s):  
R. Ahmadirouhani ◽  
S. Samiee
Keyword(s):  
Remote Sensing ◽  
Ferric Iron ◽  
Silicate Mineral ◽  
Band Ratio ◽  
Spectral Angle Mapper ◽  
Shallow Marine ◽  
North East ◽  
Composition Methods ◽  
East Of Iran ◽  
Remote Sensing Techniques

Glauconite is a greenish ferric-iron silicate mineral with micaceous structure, characteristically formed in shallow marine environments. Glauconite has been used as a pigmentation agent for oil paint, contaminants remover in environmental studies and a source of potassium in plant fertilizers, and other industries. Koppeh-dagh basin is extended in Iran, Afghanistan and Turkmenistan countries and Glauconite units exist in this basin. In this research for enhancing and mapping glauconitic units in Koppeh-dagh structural zone in north east of Iran, remote sensing techniques such as Spectral Angle Mapper classification (SAM), band ratio and band composition methods on SPOT, ASTER and Landsat data in 3 steps were applied.

scholarly journals Scrutinising the relationship between major physiological and compositional changes during ‘Merrill O’Henry’ peach growth with brown rot susceptibility

2020 ◽  
pp. 108201322095998
Author(s):  
Núria Baró-Montel ◽  
Jordi Giné-Bordonaba ◽  
Rosario Torres ◽  
Núria Vall-llaura ◽  
Neus Teixidó ◽  
...  
Keyword(s):  
Disease Incidence ◽  
Negative Relationship ◽  
Positive Association ◽  
Brown Rot ◽  
Growth Stages ◽  
Least Squares Regression ◽  
Full Bloom ◽  
Phenological Stages ◽  
Compositional Changes ◽  
The Moment

In the present work, the major physiological and compositional changes occurring during ‘Merrill O’Henry’ peach growth and its relationship with susceptibility to three strains of Monilinia spp. at 49, 77, 126 and 160 days after full bloom were explored. Results of disease incidence indicated wide differences among phenological stages, being 49 and 126 days after full bloom the moment when peaches showed significantly lower susceptibility to brown rot (40 and 23% of rotten fruit, respectively, for strain ML8L). Variation in brown rot susceptibility among different growth stages was also strain-dependent. Lower fruit susceptibility to ML8L at 49 and 126 was accompanied by noticeable changes in the fruit ethylene and respiration patterns, and also in sugars and organic acids content. By employing a partial least squares regression model, a strong negative relationship between citric acid, and a positive association of ethylene with peach susceptibility to Monilinia spp. at diverse phenological stages were observed. The results obtained herein highlight that the content of certain compounds such as citrate, malate and sucrose; the respiratory activity and the fruit ethylene production may mediate in a coordinated manner the fruit resistance to Monilinia spp. at different phenological stages of peach fruit.

scholarly journals Remote Sensing Techniques for Classification and Mapping of Sugarcane Growth

2020 ◽  
Vol 10 (4) ◽  
pp. 6041-6046
Author(s):  
M. K. Villareal ◽  
A. F. Tongco
Keyword(s):  
Remote Sensing ◽  
Saccharum Officinarum ◽  
Support Vector ◽  
Growth Stages ◽  
Remote Sensing Classification ◽  
Object Based ◽  
Sugarcane Yield ◽  
Rule Set ◽  
Remote Sensing Techniques ◽  
Yield Formation

This study aimed to apply remote sensing technologies in delineating sugarcane (Saccharum officinarum) plantations and in identifying its growth stages. Considering the growing demand for sugarcane in the local and global markets, the need for a science-based resource inventory emerges. In this sense, remote sensing techniques’ unique ability is vital to monitor crop growth and estimate crop yield. Object-Based Image Analysis (OBIA) concept was employed by utilizing orthophotos and Light Detection And Ranging (LiDAR) datasets. Specifically, the study applied the Support Vector Machine (SVM) algorithm to generate the resource map, validated by a handheld Global Positioning System (GPS). The classification result showed an accuracy of 98.4%, delineating a total of 13.93 hectares of sugarcane plantation in the study area. The height information from LiDAR datasets aided in developing the rule-set that can further classify the sugarcane according to its growth stages. Results showed that the area distribution of sugarcane at establishment, tillering, yield formation, and ripening stage were 6.65%, 11.61%, 13.89%, and 17.90% respectively. GPS validation points of the growth stages verified the accuracy of SVM. The accuracy results for growth stages, i.e. establishment, tillering, yield formation, and ripening are 88%, 94.4%, 96.3%, and 91.7% respectively. The results proved the usefulness of SVM as a remote sensing classification technique which led to an exact mapping of the sugarcane areas as well as the practical use of LiDAR height information in estimating the growth stages of the mapped resource, both of which can provide valuable aid in estimating the potential sugarcane yield in the future.

scholarly journals Parameterization of SSM Model to Analyze Wheat Growth and Yield Potential Under Pakdasht Conditions

2018 ◽  
Vol 11 (4) ◽  
pp. 1913-1926
Author(s):  
Hamed Azad ◽  
Gholam Abbas Akbar ◽  
Gholam Ali Akbari ◽  
Elias Soltani
Keyword(s):  
Block Design ◽  
Simulation Models ◽  
Growth And Yield ◽  
Yield Potential ◽  
Growth Stages ◽  
Wheat Cultivars ◽  
Phenological Stages ◽  
Wheat Growth ◽  
Production And Distribution ◽  
Growth And Aging

Simulation models of crops are used for experimental and complementary research on field projects. These models are also useful for interpreting the results and examining agricultural systems under different environmental and management conditions. The aim of this study was to describe a model for wheat (SSM), guarantee wheat cultivars in a genetic discussion in the Pakdasht environment, and present the results of its evaluation. The model of phenological stages, growth, and aging of leaf area and the production and distribution of dry matter simulates water function and balance. The SSM model simulates the growth stages of the plant in response to environmental factors, heat, and the ability to access solar radiation. In order to evaluate the SSM model, field experiment data of two wheat cultivars—SW and Pishtaz—were used as factorial, based on a randomized complete block design with four replications. Subsequently, the parameters were evaluated, the model was tested in accordance with independent data, and the results indicate its acceptance for the main aspects of crops compared to the observed experiments—for example, for SW, we have 1830 GDD to 2310 GDD from pollination to treatment and extinction factor in Pishtaz is 0.71 and PLAPOW coefficient is 1.6484±.063, which can finally be used to simulate these figures.

scholarly journals Integrated Remote Sensing and GIS for Calculating Shoreline Change in Rokan Estuary

2016 ◽  
Vol 1 ◽  
Author(s):  
Sigit Sutikno
Keyword(s):  
Remote Sensing ◽  
Shoreline Change ◽  
Remote Sensing And Gis ◽  
Ratio Method ◽  
Band Ratio ◽  
Regional Area ◽  
Accretion Rates ◽  
Remote Sensing Techniques ◽  
Analysis System ◽  
Landsat Satellite

This paper presents an application of satellite remote sensing techniques to detect and to<strong><em> </em></strong>analyze the spatial changes as well as quantify the shoreline change in Rokan estuary, Riau Province, Indonesia. Coastal zone of Rokan estuary, a place through which Rokan River flows into Malacca Strait is dynamically changed because of the hydrodynamic nature and high sediment transport in downstream of Rokan River. By integrating modern techniques of remote sensing and GIS (Geographic Information System), the rates of shoreline change would be easily and quickly determined for a regional area. Landsat satellite images were used with a combination of histogram thresholding and band ratio method for shoreline change detection for last 14 years from 2000 to 2014. The shoreline data then were adjusted for serving as an input for GIS tool to estimate the erosion and deposition rates. The statistical method called as LRR (Linear Regression Rate) in DSAS (Digital Shoreline Analysis System) was used in this study. The results of this study present shoreline changes map of Rokan estuary for last 14 years. Quantitatively, the shoreline of Rokan estuary is dynamically changed over a time because accretion rate is very high. The accretion rates in Halang, Barkey, and Serusai Island within 14 years are 67 m/yr, 53 m/yr, and 114 m/yr respectively.This occurs because

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