scholarly journals Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3971
Author(s):  
Gabriel Silva de Oliveira ◽  
José Marcato Junior ◽  
Caio Polidoro ◽  
Lucas Prado Osco ◽  
Henrique Siqueira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Dry Matter ◽  
Pearson Correlation ◽  
Genetic Correlations ◽  
Remote Sensing Data ◽  
Breeding Program ◽  
Yield Traits ◽  
Dry Matter Yield ◽  
Breeding Programs ◽  
Ground Truth Data

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha−1 for LDMY and from 413.07 to 506.56 kg·ha−1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.

scholarly journals Groundwater Potential Zones in Relation to Catchment Condition in Orenburg, Russia

2019 ◽  
pp. 60-65
Author(s):  
K Choudhary ◽  
M S Boori ◽  
A Kupriyanov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Groundwater Potential ◽  
Groundwater Potential Zone ◽  
Potential Zone ◽  
Ground Truth Data ◽  
Groundwater Availability ◽  
Sensing Data

The main objective of this study was to detect groundwater availability for agriculture in the Orenburg, Russia. Remote sensing data (RS) and geographic information system (GIS) were used to locate potential zones for groundwater in Orenburg. Diverse maps such as a base map, geomorphological, geological structural, lithology, drainage, slope, land use/cover and groundwater potential zone were prepared using the satellite remote sensing data, ground truth data, and secondary data. ArcGIS software was utilized to manipulate these data sets. The groundwater availability of the study was classified into different classes such as very high, high, moderate, low and very low based on its hydro-geomorphological conditions. The land use/cover map was prepared using a digital classification technique with the limited ground truth for mapping irrigated areas in the Orenburg, Russia.

Genetic analysis in the pasture grass Setaria sphacelata. I. Dry matter yield and flowering

1981 ◽  
Vol 32 (2) ◽  
pp. 295 ◽  
Author(s):  
JB Hacker ◽  
RA Bray
Keyword(s):  
Genetic Analysis ◽  
Dry Matter ◽  
Genetic Correlations ◽  
Diallel Cross ◽  
Dry Weight ◽  
Flower Head ◽  
High Order ◽  
Dry Matter Yield ◽  
Pasture Grass ◽  
Variance Estimates

The progeny of a 7 x 7 diallel cross between plants randomly selected from a segregating tetraploid setaria population (S. sphacelata var. sevicea and var. splendida) was studied at Lawes, south-east Queensland, over a two year period in two spaced plant replicates. Plants were harvested at 6-week intervals (9-10 week in winter), and dry weight and flower head number were recorded. Two additional replicates were allowed to grow uninterrupted, and date of flowering and flower head number were recorded. Damage caused by the fungus Pyricularia gvisea was scored on all four replicates. Genetic analysis indicated a high order of general combining ability variance for yield in the population and strong positive genetic correlation between seasons for yield (rg = 0.72-1.26). Variance estimates derived from analysis of variance and parent offspring regression were comparable. Genetic variance was strongly and consistently additive for days to flower and flower head number. Positive genetic correlations between seasons and years for days to flower (rg = 0.31-1.09) indicated that genetic differences in flowering were not strongly confounded by environmental effects. Dry matter yield was genetically correlated with flower head number and hence earliness to flower (rg = 0.79-1.16). Days to flower was genetically correlated with flower head length (rg = 0.71-0.91). Resistance to the pathogen Pyricularia grisea was shown to be under genetic control. The data suggested that resistance may be controlled by a gene exhibiting tetrasomic inheritance with two alleles necessary for expression of a high order of resistance.

scholarly journals MONITORING AND CALCULATING THE CARBON DIOXIDE EMISSIONS IN BAGHDAD AND ITS EFFECT ON INCREASING TEMPERATURES FROM 2003-2018 USING REMOTE SENSING DATA

2020 ◽  
Vol 17 (36) ◽  
pp. 357-371
Author(s):  
Mazin Shakir JASIM ◽  
Fouad Kadhum MASHEE
Keyword(s):  
Remote Sensing ◽  
Carbon Dioxide ◽  
Correlation Coefficient ◽  
Carbon Dioxide Emissions ◽  
Pearson Correlation ◽  
Remote Sensing Data ◽  
Hydrocarbon Gases ◽  
Entire Study ◽  
Sensing Data ◽  
The City

The city of Baghdad has witnessed an urban and industrial expansion with an increase in population, especially since 2003. Air pollution sources have multiplied by the increase in the number of vehicles and electricity generators, causing the emission of large quantities of hydrocarbon gases, including carbon dioxide, CO2. The discharge of such gases into the atmosphere and large amounts, will surely have a role in contributing to global warming. Therefore, it will have prominent adverse effects in influencing the rise in temperatures in the city. The research aimed to show the applied aspect of remote sensing and geographic information systems techniques in estimating the CO2 and its relationship to thermal balance for Baghdad city through fifteen stations distributed throughout the city. Remote sensing data adopted from US Geological and the European Centre, in addition to CO2 data for the Atmospheric Infrared sounder (AIRS) from Giovanni for the extended period (2003-2018), were used. Processing and statistical analysis were performed on data using GIS 10.6 and Origin 2018 software. The monthly rates of CO2 showed seasonal fluctuations between winter and summer, where the highest value of CO2 in July and the lowest value in February. Inverse Distance Weighting (IDW) technology was used to represent the spatial distribution of CO2 concentrations in the city. Residential and industrial regions experienced higher levels compared to agricultural areas. Pearson correlation coefficient was used to find out the relationship between carbon dioxide and temperatures. The correlation coefficient showed a high positive relationship between increased gas concentrations and high temperatures for all study stations over the entire study period. It can be concluded the concentration of carbon dioxide differs locally in regions of Baghdad, such as residential, commercial, traffic, industrial, and rural areas, as well as during the months of the year.

Evaluation of herbage yield in a forage grass breeding program: comparison of visual rating versus measurement in single-row plots or swards

2001 ◽  
Vol 41 (8) ◽  
pp. 1161 ◽  
Author(s):  
K. F. Smith ◽  
M. Tasneem ◽  
G. A. Kearney ◽  
K. F. M. Reed ◽  
A. Leonforte
Keyword(s):  
Correlation Coefficient ◽  
Perennial Ryegrass ◽  
Dry Matter ◽  
Rank Correlation ◽  
Breeding Program ◽  
Yield Potential ◽  
Dry Matter Yield ◽  
Single Row ◽  
Visual Rating ◽  
Herbage Yield

To refine selection methods for a perennial ryegrass (Lolium perenne L.) breeding program, half-sib families and commercial cultivars were evaluated for 3 years with treatments sown as both single-drill rows or swards. Dry matter yield of the perennial ryegrass treatments was evaluated several times in each year as a visual score which was subsequently calibrated against a regression determined by cutting a subset of plots or by cutting all plots. Thus, the experiment evaluated 2 aspects of herbage-yield determination in a perennial ryegrass breeding program: (i) the use of visual estimates of herbage yield to reduce the time spent cutting plots, and (ii) the use of single-row plots compared with swards. The correlation (either as Pearsons correlation coefficient, or Spearmans rank correlation coefficient) between visual estimates of herbage yield was always significant (P<0.01), with the exception of the rank correlation for sward plots in the summer 1995 (r = 0.4; P<0.05). However, the extent of the correlation varied (r = 0.4–0.9), and at some harvests calibrated visual ratings only explained a small proportion of the variance observed in harvested dry matter yields. These data suggest that visual ratings of herbage yield would be accurate enough to be used to detect large differences between families, breeding lines, cultivars or accessions of perennial ryegrass. However, when differences between lines are likely to be small, then harvesting all plots would give a more accurate estimate of the yield of perennial ryegrass lines. Likewise, the herbage yield of perennial ryegrass in single-row plots was significantly correlated with the herbage yield of perennial ryegrass sown as swards (P<0.01 or P<0.05). However, the correlation was again variable leading to the conclusion that evaluation of perennial ryegrass as single-row plots was not always an accurate indicator of sward yield. For those 4 (of 13) harvests over 3 years where the interaction between sward yield and row yield of the perennial ryegrass lines was significant (P<0.05), this interaction was shown not to be due to significant rank changes but rather to an increase in the differences of yield in swards or yield in single-row plots. We conclude that the harvesting of swards was the most reliable method of estimating the dry matter yield of perennial ryegrass cultivars. However, significant correlations between visual rating of treatments, or yield in single-row plots and measured yield as swards illustrated that these methods (visual ratings and single-plot yields) could be used to reduce the cost of evaluating differences in the herbage yield potential of perennial ryegrass, especially when these differences were likely to be large or when seed is limited, such as during the evaluation of accessions.

scholarly journals ESTIMATION METHODOLOGY FOR FOREST BIOMASS IN MONGOLIA USING REMOTE SENSING

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 7-12
Author(s):  
T. Altanchimeg ◽  
T. Renchin ◽  
P. De Maeyer ◽  
E. Natsagdorj ◽  
B. Tseveen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Co2 Concentration ◽  
Global Perspective ◽  
Biomass Estimation ◽  
Area Index ◽  
Ground Truth Data ◽  
Concentration Changes

Abstract. The forest biomass is one of the most important parameters for the global carbon stock. Information on the forest volume, coverage and biomass are important to develop the global perspective on the CO2 concentration changes. Objective of this research is to estimate forest biomass in the study area. The study area is Hangal sum, Bulgan province, Mongolia. Backscatter coefficients for vertical transmit and vertical receive (VV), for vertical transmit and horizontal receive (VH) from Sentinel data and Leaf Area Index (LAI) from Landsat data were used in the study area. We developed biomass estimation approach using ground truth data which is DBH, height and soil moisture. The coefficient α, β, δ, γ were found from the approach. The output map from the approach was compared with VV and VH, LAI data. The relationship between output map and VH data shows a positive result R2 = 0.61. This study suggests that the biomass estimation using Remote sensing data can be applied in forest region in the North.

scholarly journals Does the Acknowledgement of αS1-Casein Genotype Affect the Estimation of Genetic Parameters and Prediction of Breeding Values for Milk Yield and Composition Quality-Related Traits in Murciano-Granadina?

Animals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 679 ◽  
Author(s):  
María Gabriela Pizarro Inostroza ◽  
Vincenzo Landi ◽  
Francisco Javier Navas González ◽  
Jose Manuel León Jurado ◽  
Amparo Martínez Martínez ◽  
...  
Keyword(s):  
Milk Yield ◽  
Milk Fat ◽  
Dry Matter ◽  
Production Efficiency ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Increase Production Efficiency ◽  
Breeding Programs ◽  
Breeding Values ◽  
Males And Females

A total of 2090 lactation records for 710 Murciano-Granadina goats were collected during the years 2005–2016 and analyzed to investigate the influence of the αS1-CN genotype on milk yield and components (protein, fat, and dry matter). Goats were genetically evaluated, including and excluding the αS1-CN genotype, in order to assess its repercussion on the efficiency of breeding models. Despite no significant differences being found for milk yield, fat and dry matter heritabilities, protein production heritability considerably increased after aS1-CN genotype was included in the breeding model (+0.23). Standard errors suggest that the consideration of genotype may improve the model’s efficiency, translating into more accurate genetic parameters and breeding values (PBV). Genetic correlations ranged from −0.15 to −0.01 between protein/dry matter and milk yield/protein and fat content, while phenotypic correlations were −0.02 for milk/protein and −0.01 for milk/fat or protein content. For males, the broadest range for reliability (RAP) (0.45–0.71) was similar to that of females (0.37–0.86) when the genotype was included. PBV ranges broadened while the maximum remained similar (0.61–0.77) for males and females (0.62–0.81) when the genotype was excluded, respectively. Including the αS1-CN genotype can increase production efficiency, milk profitability, milk yield, fat, protein and dry matter contents in Murciano-Granadina dairy breeding programs.

On the potential of multi-source remote sensing data in characterizing the Total Suspended Matter variability in inland waters.

2021 ◽  
Author(s):  
Emanuele Ciancia ◽  
Alessandra Campanelli ◽  
Teodosio Lacava ◽  
Angelo Palombo ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Methodological Approach ◽  
Remote Sensing Data ◽  
Spatiotemporal Variability ◽  
Water Level Fluctuations ◽  
Inland Waters ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Sensing Data ◽  
Multi Temporal

&lt;p&gt;The assessment of TSM spatiotemporal variability plays a key role in inland water management, considering how these fluctuations affect water transparency, light availability, and the physical, chemical, and biological processes. All the above-mentioned topics highlight the need to develop innovative methodologies of data analysis that are able to handle multi-mission and multi-source remote sensing data, fostering the implementation of integrated and sustainable approaches. Sentinel-2A multispectral instrument (MSI) and Landsat 8 operational land instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) mainly owing to their spatial resolution (10&amp;#8211;60 m). Furthermore, the joint use of these sensors offers the opportunity to build time series with an improved revisiting time thus enabling limnologists, aquatic ecologists and water resource managers to enhance their monitoring efforts. In this framework, the potential of MSI&amp;#8211;OLI combined data in characterizing the multi-temporal (2014&amp;#8211;2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy) has been evaluated in this work. In particular, a customized MSI-based TSM model (R&lt;sup&gt;2&lt;/sup&gt;=0.81) has been developed and validated by using ground truth data acquired during specific measurement campaigns. The model was then exported on OLI data through an inter-calibration procedure (R&lt;sup&gt;2&lt;/sup&gt;=0.87), allowing for the generation of a TSM multi-temporal MSI&amp;#8211;OLI merged dataset. The analysis of the derived multi-year TSM monthly maps has shown the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side appears more affected by inflowing rivers and water level fluctuations, whose &amp;#160;effects&amp;#160; tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results highlight how the proposed methodological approach (i.e. in situ data collection, satellite data processing and modeling) can be exported in other inland waters that deserve to be investigated for a better management of water quality and monitoring systems.&lt;/p&gt;

scholarly journals Prospects for Measurement of Dry Matter Yield in Forage Breeding Programs Using Sensor Technologies

Agronomy ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 65 ◽  
Author(s):  
Alem Gebremedhin ◽  
Pieter E. Badenhorst ◽  
Junping Wang ◽  
German C. Spangenberg ◽  
Kevin F. Smith
Keyword(s):  
Dry Matter ◽  
Low Cost ◽  
Large Data ◽  
Breeding Systems ◽  
Full Potential ◽  
Dry Matter Yield ◽  
Forage Crops ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
High Dry Matter

Increasing the yield of perennial forage crops remains a crucial factor underpinning the profitability of grazing industries, and therefore is a priority for breeding programs. Breeding for high dry matter yield (DMY) in forage crops is likely to be enhanced with the development of genomic selection (GS) strategies. However, realising the full potential of GS will require an increase in the amount of phenotypic data and the rate at which it is collected. Therefore, phenotyping remains a critical bottleneck in the implementation of GS in forage species. Assessments of DMY in forage crop breeding include visual scores, sample clipping and mowing of plots, which are often costly and time-consuming. New ground- and aerial-based platforms equipped with advanced sensors offer opportunities for fast, nondestructive and low-cost, high-throughput phenotyping (HTP) of plant growth, development and yield in a field environment. The workflow of image acquisition, processing and analysis are reviewed. The “big data” challenges, proposed storage and management techniques, development of advanced statistical tools and methods for incorporating the HTP into forage breeding systems are also reviewed. Initial results where these techniques have been applied to forages have been promising but further research and development is required to adapt them to forage breeding situations, particularly with respect to the management of large data sets and the integration of information from spaced plants to sward plots. However, realizing the potential of sensor technologies combined with GS leads to greater rates of genetic gain in forages.

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