Normalized Difference Vegetation Index versus Dark Green Colour Index to estimate nitrogen status on bermudagrass hybrid and tall fescue

Caturegli, Lisa; Gaetani, Monica; Volterrani, Marco; Magni, Simone; Minelli, Alberto; Baldi, A.; Brandani, Giada; Mancini, Marco; Lenzi, Anna; Orlandini, Simone; Lulli, Filippo; Bertoldi, Claudia de; Dubbini, Marco; Grossi, Nicola

doi:10.1080/01431161.2019.1641762

Cited by 41 publications

(31 citation statements)

References 47 publications

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“…Therefore, researchers have been analyzing the potential vegetation indices (VI) obtained with visible sensors mounted to unmanned aerial vehicles (UAV), along with digital image processing techniques, to monitor the spatial variability of the nitrogen content in different kinds of crops like barley (Escalante et al, 2019), grasses (Caturegli et al, 2019;Näsi et al, 2018), potatoes (Hunt Junior et al, 2018), wheat (Schirrmann et al, 2016), and maize (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

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Nitrogen is an essential element for coffee production. However, when fertilization do not consider the spatial variability of the agricultural parameters, it can generate economic losses, and environmental impacts. Thus, the monitoring of nitrogen is essential to the fertilizing management, and remote sensing based on unmanned aerial vehicles imagery has been evaluated for this task. This work aimed to analyze the potential of vegetation indices of the visible range, obtained with such vehicles, to monitor the nitrogen content of coffee plants in southern Minas Gerais, Brazil. Therefore, we performed leaf analysis using the Kjeldahl method, and we processed the images to produce the vegetation indices using Geographic Information Systems and photogrammetry software. Moreover, the images were classified using the Color Index of Vegetation and the Maximum Likelihood Classifier. As estimator tool, we created Random Forest models of classification and regression. We also evaluated the Pearson correlation coefficient between the nitrogen and the vegetation indices, and we performed the analysis of variance and the Tukey-Kramer test to assess whether there is a significant difference between the averages of these indices in relation to nitrogen levels. However, the models were not able to predict the nitrogen. The regression model obtained a R 2 = 0.01. The classification model achieved an overall accuracy of 0.33 (33%), but it did not distinguish between the different levels of nitrogen. The correlation tests revealed that the vegetation indices are not correlated with the nitrogen, since the best index was the Green Leaf Index (R = 0.21). However, the image classification achieved a Kappa coefficient of 0.92, indicating that the tested index is efficient. Therefore, visible indices were not able to monitor the nitrogen in this case, but they should continue to be explored, since they could represent a less expensive alternative.

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Section: Introductionmentioning

confidence: 99%

Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

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“…Many works have been made using DGCI for measure the correlation between dark green and fertilization, manly with nitrogen and phosphorus. Caturegli et al (2019) found positive correlation when compared the DGCI with doses of nitrogen applied in For Simões et al (2019), is extremely important to quantify incident radiation during plant development, in order to evaluate their effects in various physiological functioning processes, in addition, it is possible to establish management practices that enable better use of this resource by species of grass, which contributes to a specific aesthetic expectation.…”

Section: Resultsmentioning

confidence: 99%

Light Levels Influence on Development and Leaves Reflectance Index of Imperial Zoysia Grass / Influência De Níveis De Luz No Desenvolvimento E Índice De Refletância Das Folhas De Grama Esmeralda Imperial

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“…This index can indicate when the lawn is exposed to stresses such as trampling/compaction, utilization, and water deficit (Kerr et al, 2019;Straw et al, 2020). NDVI can be used as an alternative to evaluate the DCGI in the turfgrass (Caturegli et al, 2020). Higher NDVI indexes can be obtained according to the increase in turfgrass cover, density, and above-ground biomass, which may vary between grass species (Bremer et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

Regrowth and ornamental traits of bermudagrass fertilized with sewage sludge

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The sewage sludge is a low-cost material and sustainable alternative to substitute chemical fertilizers on ornamental lawns and gardens. Thus, the objective was to evaluate the effects of the application of sewage sludge on the regrowth and ornamental traits of DiscoveryTM bermudagrass. The experiment was carried out during the fall/winter of 2019. The turf was removed and left the soil exposed for a new grass regrowth. The treatments applied were 0, 357, 714, 1,071 and 1,428 g m-2 sewage sludge spread evenly on the lawn in a single dose. The evaluations were carried out after 120 days and the soil solution (EC and NO3 -), Normalized difference vegetation index, root length, root + rhizome + stolon + leaves volume and digital image analysis were evaluated. The results showed that the increase of sewage sludge positively influenced the turfgrass development, both in the aesthetic aspect and on bermudagrass regrowth. The soil solution can show that the sludge increased the electrical conductivity and NO3- ions; however, it did not hinder the development of the lawn, even having positive correlations between these variables and the biometric evaluations of the plant. It is concluded that the dose of 1,428 g m-2 presented the best results for the evaluated characteristics, being the recommended one for use in the fertilization of bermudagrass DiscoveryTM.

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Normalized Difference Vegetation Index versus Dark Green Colour Index to estimate nitrogen status on bermudagrass hybrid and tall fescue

Cited by 41 publications

References 47 publications

Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

Using unmanned aerial vehicle and machine learning algorithm to monitor leaf nitrogen in coffee

Light Levels Influence on Development and Leaves Reflectance Index of Imperial Zoysia Grass / Influência De Níveis De Luz No Desenvolvimento E Índice De Refletância Das Folhas De Grama Esmeralda Imperial

Regrowth and ornamental traits of bermudagrass fertilized with sewage sludge

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