scholarly journals Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 285 ◽  
Author(s):  
Salima Yousfi ◽  
Adrian Gracia-Romero ◽  
Nassim Kellas ◽  
Mohamed Kaddour ◽  
Ahmed Chadouli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Water Status ◽  
Low Cost ◽  
Vegetation Indices ◽  
Carbon Isotopic Composition ◽  
Canopy Temperature ◽  
N Fertilization ◽  
Canopy Area ◽  
Rgb Images

Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

scholarly journals Comparison of Proximal Remote Sensing Devices for Estimating Physiological Responses of Eggplants to Root-Knot Nematodes

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 9
Author(s):  
Alex Silva-Sánchez ◽  
Julia Buil-Salafranca ◽  
Andrea Casadesús Cabral ◽  
Naroa Uriz-Ezcaray ◽  
Helio Adán García-Mendívil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Low Cost ◽  
Canopy Temperature ◽  
P Value ◽  
Plant Responses ◽  
Root Knot Nematodes ◽  
Single Leaf ◽  
Rgb Images ◽  
Visual Ranking ◽  
Time Required

Proximal remote sensing devices are becoming widely applied in field plant research to estimate biochemical (e.g., pigments or nitrogen) or agronomical (e.g., leaf area, biomass, or yield) parameters as indicators of stress. Non-invasive characterization of plant responses allows for the screening of larger populations faster than conventional procedures which, in addition to requiring more time, either imply the destruction of material or are subjective (e.g., visual ranking). This study explores the comparison of a set of remote sensing devices at single-leaf and whole-canopy levels based on their performance in assessing how the eggplant and its yield responds to grafting as a way to tolerate root-knot nematodes. The results showed that whole-canopy measurements, such as the Green Area (GA) derived from the Red-Green-Blue (RGB) images (r = 0.706 and p-value = 0.001**) or the canopy temperature (r = −0.619 and p-value = 0.005**), outperformed single-leaf measurements, such as the leaf chlorophyll content measured by the Dualex (r = 0.422 and p-value = 0.059) assessing yield. Moreover, other parameters, such as the time required to measure each sample or the cost of the sensors, were taken into account in the discussion. In sum, indices derived from the RGB images demonstrated their robust potential for the assessment of crop status as a low-cost alternative to other more expensive and time-consuming devices.

scholarly journals Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2439
Author(s):  
Haixiao Ge ◽  
Fei Ma ◽  
Zhenwang Li ◽  
Changwen Du
Keyword(s):  
Grain Yield ◽  
Low Cost ◽  
Vegetation Indices ◽  
Model Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Rice Breeding ◽  
Yield Estimation ◽  
Phenological Data ◽  
Significant Difference

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

scholarly journals Thermal Imaging Reliability for Estimating Grain Yield and Carbon Isotope Discrimination in Wheat Genotypes: Importance of the Environmental Conditions

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2676 ◽  
Author(s):  
Sebastián Romero-Bravo ◽  
Ana María Méndez-Espinoza ◽  
Miguel Garriga ◽  
Félix Estrada ◽  
Alejandro Escobar ◽  
...  
Keyword(s):  
Grain Yield ◽  
Thermal Imaging ◽  
Mediterranean Climate ◽  
Vapor Pressure Deficit ◽  
Stress Condition ◽  
Water Status ◽  
Canopy Temperature ◽  
Spring Bread Wheat ◽  
Wheat Genotypes ◽  
The Relationship

Canopy temperature (Tc) by thermal imaging is a useful tool to study plant water status and estimate other crop traits. This work seeks to estimate grain yield (GY) and carbon discrimination (Δ13C) from stress degree day (SDD = Tc − air temperature, Ta), considering the effect of a number of environmental variables such as the averages of the maximum vapor pressure deficit (VPDmax) and the ambient temperature (Tmax), and the soil water content (SWC). For this, a set of 384 and a subset of 16 genotypes of spring bread wheat were evaluated in two Mediterranean-climate sites under water stress (WS) and full irrigation (FI) conditions, in 2011 and 2012, and 2014 and 2015, respectively. The relationship between the GY of the 384 wheat genotypes and SDD was negative and highly significant in 2011 (r2 = 0.52 to 0.68), but not significant in 2012 (r2 = 0.03 to 0.12). Under WS, the average GY, Δ13C, and SDD of wheat genotypes growing in ten environments were more associated with changes in VPDmax and Tmax than with the SWC. Therefore, the amount of water available to the plant is not enough information to assume that a particular genotype is experiencing a stress condition.

Low-cost assessment of grain yield in durum wheat using RGB images

2019 ◽  
Vol 105 ◽  
pp. 146-156 ◽  
Author(s):  
Jose A. Fernandez-Gallego ◽  
Shawn C. Kefauver ◽  
Thomas Vatter ◽  
Nieves Aparicio Gutiérrez ◽  
María Teresa Nieto-Taladriz ◽  
...  
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Low Cost ◽  
Cost Assessment ◽  
Rgb Images

scholarly journals La importancia del intervalo de la floración en el mejoramiento para la resistencia a sequía en maíz tropical

2016 ◽  
Vol 1 ◽  
pp. 45 ◽  
Author(s):  
J. Bolaños ◽  
G. O. Edmeades
Keyword(s):  
Grain Yield ◽  
Recurrent Selection ◽  
Water Status ◽  
Effective Means ◽  
Chlorophyll Concentration ◽  
Canopy Temperature ◽  
Shoot Elongation ◽  
Potential Yield ◽  
Erect Leaves ◽  
Bidirectional Selection

The length of the Interval between anthesis and silking (ASI) is increased by drought which coincides with flowering. Four elite CIMMYT lowland tropical populations are undergoing recurrent selection (S1 or full-sib) for improved grain yield and several other traits under drought and well watered conditions. Data collected from more than 200 families per population grown In single row plots under three water stress levels (pre and post-flowering stress; post-flowering stress; normal irrigation, all in the absence of rain) showed weak or no correlation between grain yield and traits related to plant water status, such as leaf rolling and senescence, photooxidation, leaf chlorophyll concentration, shoot elongation rate, canopy temperature and predawn water potential. Yield under all levels of stress was significantly negatively correlated with AST, and as AST increased due to drought, kernels and ears per plant were significantly reduced. In all populations yield decreased by approximately 10 % per day increase in AST up to 8 days. In several stress situations broad-sense heritability of AST was greater than that of grain yield and the genetic correlation between grain yield and AST approached -1.00. Synthetics formed from one population following bidirectional selection and tested under drought showed adaptive advantage of cool canopy temperature, delayed leaf senescence, reduced AST and erect leaves, especially when all were combined with grain yield in a single index during selection. Selection for reduced AST and high grain yield under drought can be an effective means of improving drought tolerance in tropical maize.

scholarly journals CROP ROW DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 349-356 ◽  
Author(s):  
M. Hassanein ◽  
M. Khedr ◽  
N. El-Sheimy
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Weed Management ◽  
Low Cost ◽  
Color Space ◽  
Orientation Angle ◽  
Weed Detection ◽  
Agriculture Industry ◽  
Rgb Images ◽  
Scan Line

<p><strong>Abstract.</strong> Precision Agriculture (PA) management systems are considered among the top ten revolutions in the agriculture industry during the last couple decades. Generally, the PA is a management system that aims to integrate different technologies as navigation and imagery systems to control the use of the agriculture industry inputs aiming to enhance the quality and quantity of its output, while preserving the surrounding environment from any harm that might be caused due to the use of these inputs. On the other hand, during the last decade, Unmanned Aerial Vehicles (UAVs) showed great potential to enhance the use of remote sensing and imagery sensors for different PA applications such as weed management, crop health monitoring, and crop row detection. UAV imagery systems are capable to fill the gap between aerial and terrestrial imagery systems and enhance the use of imagery systems and remote sensing for PA applications. One of the important PA applications that uses UAV imagery systems, and which drew lots of interest is the crop row detection, especially that such application is important for other applications such as weed detection and crop yield predication. This paper introduces a new crop row detection methodology using low-cost UAV RGB imagery system. The methodology has three main steps. First, the RGB images are converted into HSV color space and the Hue image are extracted. Then, different sections are generated with different orientation angles in the Hue images. For each section, using the PCA of the Hue values in the section, an analysis can be performed to evaluate the variances of the Hue values in the section. The crop row orientation angle is detected as the same orientation angle of the section that provides the minimum variances of Hue values. Finally, a scan line is generated over the Hue image with the same orientation angle of the crop rows. The scan line computes the average of the Hue values for each line in the Hue image similar to the detected crop row orientation. The generated values provide a graph full of peaks and valleys which represent the crop and soil rows. The proposed methodology was evaluated using different RGB images acquired by low-cost UAV for a Canola field. The images were taken at different flight heights and different dates. The achieved results proved the ability of the proposed methodology to detect the crop rows at different cases.</p>

scholarly journals Application of RGB Images Obtained by UAV in Coffee Farming

2021 ◽  
Vol 13 (12) ◽  
pp. 2397
Author(s):  
Brenon Diennevam Souza Barbosa ◽  
Gabriel Araújo e Silva Ferraz ◽  
Luana Mendes dos Santos ◽  
Lucas Santos Santana ◽  
Diego Bedin Marin ◽  
...  
Keyword(s):  
Low Cost ◽  
Pearson Correlation ◽  
Vegetation Indices ◽  
Digital Camera ◽  
Production Cycle ◽  
Practical Application ◽  
Area Index ◽  
Rgb Images ◽  
Pearson Correlation Analysis ◽  
Coffee Farming

The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R2 values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way.

scholarly journals High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 655
Author(s):  
Marta García-Fernández ◽  
Enoc Sanz-Ablanedo ◽  
José Ramón Rodríguez-Pérez
Keyword(s):  
Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Amino Nitrogen ◽  
Correlation Coefficients ◽  
Total Polyphenol ◽  
Grape Quality ◽  
Rgb Images ◽  
Index 2 ◽  
Maturation Index

Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Georeferenced berry samples were collected and analyzed in the laboratory, and RGB images were taken using a low-cost drone from which an orthoimage was made. Transformation equations were calculated to obtain absolute reflectances for the different bands and to calculate 10 vegetation indices plus two new proposed indices. Correlations for the 12 indices with values for 15 must quality variables were calculated in terms of Pearson’s correlation coefficients. Significant correlations were obtained for 100-berries weight (0.77), malic acid (−0.67), alpha amino nitrogen (−0.59), phenolic maturation index (0.69), and the total polyphenol index (0.62), with 100-berries weight and the total polyphenol index obtaining the best results in the proposed RGB-based vegetation index 2 and RGB-based vegetation index 3. Our findings indicate that must variables important for the production of quality wines can be related to the RGB bands in conventional digital images, potentially improving and aiding management and increasing productivity.

scholarly journals An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-Resolution, Low-Cost Satellite Images

2019 ◽  
Vol 11 (21) ◽  
pp. 2539
Author(s):  
Azadeh Abdollahnejad ◽  
Dimitrios Panagiotidis ◽  
Lukáš Bílek
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Sustainable Forest Management ◽  
Low Cost ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Monitoring Method ◽  
Gis And Remote Sensing ◽  
Very High

Advanced monitoring and mapping of forest areas using the latest technological advances in satellite imagery is an alternative solution for sustainable forest management compared to conventional ground measurements. Remote sensing products have been a key source of information and cost-effective options for monitoring changes in harvested areas. Despite recent advances in satellite technology with a broad variety of spectral and temporal resolutions, monitoring the areal extent of harvested forest areas in managed forests is still a challenge, primarily due to the highly dynamic spatiotemporal patterns of logging activities. Our goal was to introduce a plot-based method for monitoring harvested forest areas from very high-resolution (VHR), low-cost satellite images. Our method encompassed two data categories, which included vegetation indices (VIs) and texture analysis (TA). Each group of data was used to model the amount of harvested volume both independently and in combination. Our results indicated that the composition of all spectral bands can improve the accuracy of all models of average volume by 23.52 RMSE reduction and total volume by 33.57 RMSE reduction. This method demonstrated that monitoring and extrapolation of the calculated relation and results from smaller forested areas could be applied as an automatic remote-based supervised monitoring method over larger forest areas.

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