Low-cost assessment of wheat resistance to yellow rust through conventional RGB images

Zhou, Bangwei; Elazab, Abdelhalim; Bort, J.; Vergara, Omar; Serret, María Dolores; Araus, José Luís

doi:10.1016/j.compag.2015.05.017

Cited by 53 publications

(35 citation statements)

References 52 publications

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“…The GA vegetation index was chosen by the model as the first independent variable, explaining 66% of GY variability under SI-2 without N fertilizer. Moreover, various studies have reported that RGB-based indices may perform far better than the NDVI for GY prediction in wheat [32,56,65]. In our study, the NDVI failed to assess GY under irrigation.…”

Section: Relationship Of Vegetation Indices and Water Status Traits Wcontrasting

confidence: 62%

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

et al. 2019

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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.

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Section: Relationship Of Vegetation Indices and Water Status Traits Wcontrasting

confidence: 62%

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

et al. 2019

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“…The use of indexes derived from RGB (red-green-blue) images taken with conventional cameras is a simple, non-destructive and cost-effective method employed in assessing crop status in field conditions, including N status and water stress [27]. RGB images have proven useful in studies evaluating the effect of abiotic stresses but have yet to be fully exploited to phenotype disease resistance [28], although RGB indexes have proven to be accurate predictors of grain yield as well as in assessing damage caused by Fusarium in wheat kernels [29] or assessing grain yield losses and resistance to yellow rust in wheat [30,31]. To the author's knowledge, although other remote sensing technologies have been used in assessing VWO, this was the first time that RGB vegetation indexes were used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

“…Indexes fitting this criteria are the green area index (GA) [32], which calculates percentage of green pixels in a given image by defining green as 60 • < Hue < 180 • , or the greener area index (GGA) which has a stricter definition with green as 80 • < Hue < 180 • , thus excluding pixels with a yellowish-green hue generally associated with senescent plant matter. The difference between these two is used to determine the crop senescence index (CSI) which estimates the percent of senescent plant matter relative to total canopy [30].…”

Section: Introductionmentioning

confidence: 99%

“…Axes u* and v* in CIELUV space are analogous to a* and b* respectively, although calculated using a different formula [33]. kernels [29] or assessing grain yield losses and resistance to yellow rust in wheat [30,31]. To the author's knowledge, although other remote sensing technologies have been used in assessing VWO, this was the first time that RGB vegetation indexes were used for this purpose.…”

Section: Introductionmentioning

confidence: 99%

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Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios

et al. 2019

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Verticillium Wilt of Olive, a disease caused by the hemibiotrophic vascular fungus Verticillium dahliae Kleb. presents one of the most important constraints to olive production in the world, with an especially notable impact in Mediterranean agriculture. This study evaluates the use of RGB vegetation indexes in assessing the effects of this disease during the biotrophic phase of host-pathogen interaction, in which symptoms of wilt are not yet evident. While no differences were detected by measuring stomatal conductance and chlorophyll fluorescence, results obtained from RGB indexes showed significant differences between control and inoculated plants for indexes Saturation, a*, b*, green Area (GA), normalized green-red difference index (NGRDI) and triangular greenness index (TGI), presenting a reduction in plant growth as well as in green and yellow color components as an effect of inoculation. These results were contrasted across two scenarios of mineral fertilization in soil and soil amended with two different olive mill waste composts, presenting a clear interaction between the host-pathogen relationship and plant nutrition and suggesting the effect of V. dahliae infection during the biotrophic phase was not related to plant water status.

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“…Some studies have been done to identify and separate the vegetation in crop images [33,34] using vegetation indexes (excess green index, ExG; vegetative index, VEG, others). Other studies have used conversions from the RGB color model to HSI, CIELAB and CIELUV color models, extensively, and successfully in order to separate the green color (vegetation) from the images and provide phenotypic and genotypic data at different growth conditions [35][36][37][38]. The CIELAB color model depends less on illumination [39] defining colors more according to the color perception by the human eye.…”

Section: Introductionmentioning

confidence: 99%

Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates

et al. 2020

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The number of plants, or planting density, is a key factor in corn crop yield. The objective of the present research work was to count corn plants using images obtained by sensors mounted on an unmanned aerial vehicle (UAV). An experiment was set up with five levels of nitrogen fertilization (140, 200, 260, 320 and 380 kg/ha) and four replicates, resulting in 20 experimental plots. The images were taken at 23, 44 and 65 days after sowing (DAS) at a flight altitude of 30 m, using two drones equipped with RGB sensors of 12, 16 and 20 megapixels (Canon PowerShot S100_5.2, Sequoia_4.9, DJI FC6310_8.8). Counting was done through normalized cross-correlation (NCC) for four, eight and twelve plant samples or templates in the a* channel of the CIELAB color space because it represented the green color that allowed plant segmentation. A mean precision of 99% was obtained for a pixel size of 0.49 cm, with a mean error of 2.2% and a determination coefficient of 0.90 at 44 DAS. Precision values above 91% were obtained at 23 and 44 DAS, with a mean error between plants counted digitally and visually of ±5.4%. Increasing the number of samples or templates in the correlation estimation improved the counting precision. Good precision was achieved in the first growth stages of the crop when the plants do not overlap and there are no weeds. Using sensors and unmanned aerial vehicles, it is possible to determine the emergence of seedlings in the field and more precisely evaluate planting density, having more accurate information for better management of corn fields.

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Low-cost assessment of wheat resistance to yellow rust through conventional RGB images

Cited by 53 publications

References 52 publications

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

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

Use of RGB Vegetation Indexes in Assessing Early Effects of Verticillium Wilt of Olive in Asymptomatic Plants in High and Low Fertility Scenarios

Digital Count of Corn Plants Using Images Taken by Unmanned Aerial Vehicles and Cross Correlation of Templates

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