scholarly journals Assessment of RGB Vegetation Indices to Estimate Chlorophyll Content in Sugar Beet Leaves in the Final Cultivation Stage

2020 ◽  
Vol 2 (1) ◽  
pp. 128-149 ◽  
Author(s):  
Luis Fernando Sánchez-Sastre ◽  
Nuno M. S. Alte da Veiga ◽  
Norlan Miguel Ruiz-Potosme ◽  
Paula Carrión-Prieto ◽  
José Luis Marcos-Robles ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Chlorophyll Content ◽  
Vegetation Indices ◽  
Principal Component ◽  
Harvest Time ◽  
Light Conditions ◽  
Nitrogen Status ◽  
Chlorophyll Contents ◽  
Aerial Vehicle ◽  
Good Agreement

Estimation of chlorophyll content with portable meters is an easy way to quantify crop nitrogen status in sugar beet leaves. In this work, an alternative for chlorophyll content estimation using RGB-only vegetation indices has been explored. In a first step, pictures of spring-sown ‘Fernanda KWS’ variety sugar beet leaves taken with a commercial camera were used to calculate 25 RGB indices reported in the literature and to obtain 9 new indices through principal component analysis (PCA) and stepwise linear regression (SLR) techniques. The performance of the 34 indices was examined in order to evaluate their ability to estimate chlorophyll content and chlorophyll degradation in the leaves under different natural light conditions along 4 days of the canopy senescence period. Two of the new proposed RGB indices were found to improve the already good performance of the indices reported in the literature, particularly for leaves featuring low chlorophyll contents. The 4 best indices were finally tested in field conditions, using unmanned aerial vehicle (UAV)-taken photographs of a sugar beet plot, finding a reasonably good agreement with chlorophyll-meter data for all indices, in particular for I2 and (R−B)/(R+G+B). Consequently, the suggested RGB indices may hold promise for inexpensive chlorophyll estimation in sugar beet leaves during the harvest time, although a direct relationship with nitrogen status still needs to be validated.

scholarly journals Principal Component Analysis of Chlorophyll Content in Tobacco, Bean and Petunia Plants Exposed to Different Tropospheric Ozone Concentrations

2014 ◽  
Vol 12 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Klaudia Borowiak ◽  
Janina Zbierska ◽  
Anna Budka ◽  
Dariusz Kayzer
Keyword(s):  
Principal Component Analysis ◽  
Chlorophyll Content ◽  
Plant Species ◽  
Ozone Concentration ◽  
Tropospheric Ozone ◽  
Principal Component ◽  
Ambient Air ◽  
Component Analysis ◽  
Chlorophyll Contents ◽  
Ozone Concentrations

Abstract Three plant species were assessed in this study - ozone-sensitive and -resistant tobacco, ozone-sensitive petunia and bean. Plants were exposed to ambient air conditions for several weeks in two sites differing in tropospheric ozone concentrations in the growing season of 2009. Every week chlorophyll contents were analysed. Cumulative ozone effects on the chlorophyll content in relation to other meteorological parameters were evaluated using principal component analysis, while the relation between certain days of measurements of the plants were analysed using multivariate analysis of variance. Results revealed variability between plant species response. However, some similarities were noted. Positive relations of all chlorophyll forms to cumulative ozone concentration (AOT 40) were found for all the plant species that were examined. The chlorophyll b/a ratio revealed an opposite position to ozone concentration only in the ozone-resistant tobacco cultivar. In all the plant species the highest average chlorophyll content was noted after the 7th day of the experiment. Afterwards, the plants usually revealed various responses. Ozone-sensitive tobacco revealed decrease of chlorophyll content, and after few weeks of decline again an increase was observed. Probably, due to the accommodation for the stress factor. While during first three weeks relatively high levels of chlorophyll contents were noted in ozone-resistant tobacco. Petunia revealed a slow decrease of chlorophyll content and the lowest values at the end of the experiment. A comparison between the plant species revealed the highest level of chlorophyll contents in ozone-resistant tobacco.

scholarly journals Impact of Foliar Application of Various Forms of Silicon on the Chemical Composition of Sugar Beet Plants

Sugar Tech ◽  
2021 ◽  
Author(s):  
Arkadiusz Artyszak ◽  
Małgorzata Kondracka ◽  
Dariusz Gozdowski ◽  
Alicja Siuda ◽  
Magda Litwińczuk-Bis
Keyword(s):  
Chemical Composition ◽  
Sugar Beet ◽  
Foliar Application ◽  
Foliar Spray ◽  
Water Shortage ◽  
Sugar Yield ◽  
Orthosilicic Acid ◽  
Harvest Time ◽  
Polysilicic Acid ◽  
Critical Phase

AbstractThe effect of marine calcite, a mixture of ortho- and polysilicic acid as well as orthosilicic acid applied as a foliar spray on the chemical composition of sugar beet leaves in the critical phase of nutrient supply (beginning of July) but also leaves and roots during harvest time in 2015–2016, was studied. The content of silicon in the leaves ranged from 1.24 to 2.36 g kg−1 d.m. at the beginning of July, 3.85–5.34 g kg−1 d.m. during harvest and 2.91–4.20 g kg−1 d.m. in the roots. The foliar application of silicon caused a significant increase in the content of magnesium and calcium in leaves (in July) as compared to the control. The sugar beet consumes approx. 75 kg Si ha−1, which is almost 3.5 times more than P and 20% more than Mg thus proving its importance for its species. About 70% of the silicon taken up by sugar beet is stored in roots and 30% in leaves. The pure sugar yield is most favorably influenced by two- and threefold foliar application of the product containing silicon in the form of orthosilicic acid stabilized with choline, and a threefold mixture of ortho- and polysilicic acid. The increase in the pure sugar yield is not the result of a change in the chemical composition of sugar beet plants, but their more efficient functioning after foliar application of silicon under stress conditions caused by water shortage.

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

A novel spectral index for estimation of relative chlorophyll content of sugar beet

2021 ◽  
Vol 184 ◽  
pp. 106088
Author(s):  
Jing Zhang ◽  
Haiqing Tian ◽  
Di Wang ◽  
Haijun Li ◽  
Abdul Mounem Mouazen
Keyword(s):  
Sugar Beet ◽  
Chlorophyll Content ◽  
Spectral Index ◽  
Relative Chlorophyll Content

Agreement and reliability of magnetic, angular rate, and gravity (MARG) sensors to assess multiple body segment’s external loads during off-road running

2021 ◽  
pp. 175433712199698
Author(s):  
Daniel Rojas-Valverde ◽  
José Pino-Ortega ◽  
Rafael Timón ◽  
Randall Gutiérrez-Vargas ◽  
Braulio Sánchez-Ureña ◽  
...  
Keyword(s):  
Vastus Lateralis ◽  
Angular Rate ◽  
Wearable Sensors ◽  
Intraclass Correlation ◽  
Principal Component ◽  
Test Reliability ◽  
Data Set ◽  
Body Segments ◽  
External Loads ◽  
Good Agreement

The extensive use of wearable sensors in sport medicine, exercise medicine, and health has increased the interest in their study. That is why it is necessary to test these technologies’ efficiency, effectiveness, agreement, and reliability in different settings. Consequently, the purpose of this article was to analyze the magnetic, angular rate, and gravity (MARG) sensor’s test-retest agreement and reliability when assessing multiple body segments’ external loads during off-road running. A total of 18 off-road runners (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran two laps (1st and 2nd Lap) of a 12 km circuit wearing six MARG sensors. The sensors were attached to six different body segments: left (MPLeft) and right (MPRight) malleolus peroneus, left (VLLeft) and right (VLRight) vastus lateralis, lumbar (L1-L3), and thorax (T2-T4) using a special neoprene suit. After a principal component analysis (PCA) was performed, the total data set variance of all body segments was represented by 44.08%–70.64% for the 1st PCA factor considering two variables, Player LoadRT and Impacts, on L1-L3, respectively. These two variables were chosen among three total accelerometry-based external load indicators (ABELIs) to perform the agreement and reliability tests due to their relevance based on PCAs for each body segment. There were no significant differences between laps in the Player LoadRT or Impacts ( p > 0.05, trivial). The intraclass correlation and lineal correlation showed a substantial to almost perfect over-time test consistency assessed via reliability in both Player LoadRT and Impacts. Bias and t-test assessments showed good agreement between Laps. It can be concluded that MARGs sensors offer significant test re-test reliability and good agreement when assessing off-road kinematics in the six different body segments.

scholarly journals Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (10) ◽  
pp. 1930
Author(s):  
Gabriel Loureiro ◽  
André Dias ◽  
Alfredo Martins ◽  
José Almeida
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Detection Algorithm ◽  
Cloud Data ◽  
Safety Issues ◽  
Spot Detection ◽  
Cloud Clusters ◽  
Aerial Vehicle ◽  
Incoming Light ◽  
Landing Spot

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

Sign in / Sign up

Export Citation Format

Share Document