A methodological approach to assess canopy NDVI–based tomato dynamics under irrigation treatments

Grados, Diego; Reynarfaje, X.; Schrevens, Eddie

doi:10.1016/j.agwat.2020.106208

Cited by 16 publications

(5 citation statements)

References 56 publications

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“…The plant-growth-promoting activity of several bacteria on different crops has already been reported in grasses, Fabaceae , and some weeds [ 15 ]. In this study, the results of enzymatic activity and plant weight were correlated with NDVI and NGBVI, such as the relationship of NDVI and deficit irrigation [ 24 ] or the Standardized Precipitation and Evapotranspiration Index [ 25 ]. In order to develop a better understanding of the correlation results, it is important to remember that NDVI represents an indirect estimation of photosynthetic activity, and high NDVI values are related to high chlorophyll fluorescence due to NIR emissions.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress

Solano-Alvarez

Valencia-Hernández

Vergara-Pineda

et al. 2022

Plants

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Precision agriculture has the objective of improving agricultural yields and minimizing costs by assisting management with the use of sensors, remote sensing, and information technologies. There are several approaches to improving crop yields where remote sensing has proven to be an important methodology to determine agricultural maps to show surface differences which may be associated with many phenomena. Remote sensing utilizes a wide variety of image sensors that range from common RGB cameras to sophisticated, hyper-spectral image cameras which acquire images from outside the visible electromagnetic spectrum. The NDVI and NGBVI are computer vision vegetation index algorithms that perform operations from color masks such as red, green, and blue from RGB cameras and hyper-spectral masks such as near-infrared (NIR) to highlight surface differences in the image to detect crop anomalies. The aim of the present study was to determine the relationship of NDVI and NGBVI as plant health indicators in tomato plants (Solanum lycopersicum) treated with the beneficial bacteria Bacillus cereus-Amazcala (B. c-A) as a protective agent to cope with Clavibacter michiganensis subsp. michiganensis (Cmm) infections. The results showed that in the presence of B. c-A after infection with Cmm, NDVI and NGBVI can be used as markers of plant weight and the activation of the enzymatic activities related to plant defense induction.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress

Solano-Alvarez

Valencia-Hernández

Vergara-Pineda

et al. 2022

Plants

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“…This is likely because the VIs responded to the effect of the spatial variability of soils on plant growth that was exaggerated under rainfed conditions compared to irrigated conditions. Under irrigated conditions, crop water stress can be better managed and promote more uniform growth across the field despite underlying soil variability [39][40][41]. This difference in VIs between the irrigated and rainfed fields is related to reflectance in the NIR, in which reflectance in this band is more evident than in the visible spectrum in plants under stress.…”

Section: Discussionmentioning

confidence: 99%

Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks

et al. 2021

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Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.

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“…Many time-series models have been proposed for analyzing crop phenology. Such models include shape-model fitting (Sakamoto et al, 2013;Zhou et al, 2020), random regression with the Legendre polynomial (Campbell et al, 2018;Campbell et al, 2019), segmented linear regression (Toda et al, 2021), and non-linear growth curves (Chang et al, 2017;Grados et al, 2020;Poudel et al, 2022). Anderson et al (2019) applied a three-parameter logistic model (S-shape non-linear curve) to maize CH time-series data measured by UAV over 1 year, applied a linear mixed effects (LME) model to the logistic parameters, decomposed the parameter variance into genetic and environmental effects: they showed that some of the parameters could be used as predictors of grain yield.…”

Section: Convergence and Future Prospectsmentioning

confidence: 99%

High-Throughput Field Phenotyping to Advance Precision Agriculture and Enhance Genetic Gain, Volume II

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

show abstract

A methodological approach to assess canopy NDVI–based tomato dynamics under irrigation treatments

Cited by 16 publications

References 56 publications

Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress

Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress

Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks

High-Throughput Field Phenotyping to Advance Precision Agriculture and Enhance Genetic Gain, Volume II

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