Precision Agriculture and Remote Sensing

Varella, Carlos Alberto Alves; Gleriani, José Marinaldo; Santos, Ronaldo Medeiros dos

doi:10.1016/b978-0-12-802239-9.00009-8

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…Finch et al (2014) [14] primarily emphasize agronomic aspects (the matching of agronomic inputs and practices to localized conditions within a field and the improvement of the accuracy of their application). Varella et al (2015) [15] point out the importance of remote sensing (a new management technology based on georeferenced information for the control of agricultural systems), while Leonard (2015) [16] states that PF is the practice of managing variability in space and time.…”

Section: Literature Review 21 Agricultural Innovation and Precision Farmingmentioning

confidence: 99%

Economic and Social Barriers of Precision Farming in Hungary

et al. 2021

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Precision farming may play an important role in agricultural innovation. The study focuses on the attitude of Hungarian farmers toward precision farming. Based on the relevant technical literature, we performed a nationally representative questionnaire survey of 594 farmers and deep interviews with experts and farmers (30 persons). As regards the questionnaire, the authors found that the management of the average farm size in Hungary has the highest willingness to innovate and the second highest level of education among the developed clusters. The survey shows undertrained farmers with large farms to be the second most open group, which may result in the partial application of precision farming techniques. One of the most unexpected results of the Precision Farmers’ cluster is that the positive socio-economic utility of precision farming is rated as extremely low. In-depth interviews prove that the use of precision technologies does not increase local social cohesion. Strong organisational isolation of precision farmers prevents the spread of innovation knowledge and precision farming amongst the farming community, and the challenges of competitiveness alone do not force farmers to apply precision farming. Our results may be useful for the establishment of agricultural strategy.

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Section: Literature Review 21 Agricultural Innovation and Precision Farmingmentioning

confidence: 99%

Economic and Social Barriers of Precision Farming in Hungary

et al. 2021

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“…52 It is a local estimation technique of the best linear unbiased estimator for the unknown values of spatial and temporal variables. 53 The Kriging model is used to build the surrogate model for training error with respect to all the training parameters. 54 As a widely used experimental design method in the construction of surrogate model, Latin-Hypercube models are commonly used because they allow flexible sample sizes, have a good space filling capability and are capable of representing the whole space with relatively few sample points.…”

Section: Ann Model Tuningmentioning

confidence: 99%

Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks

Elgamal

Doddamani

et al. 2020

Journal of Composite Materials

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Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus ( E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems.

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“…The anisotropic Semi-variogram is affected by distance and direction, while the isotropic Semi-variogram is only affected by distance. The most commonly used Semivariograms are: Spherical, exponential, and gaussian [14]. Co-kriging has evolved alongside science and technology, and it is already used in a wide range of situations and cases.…”

Section: Introductionmentioning

confidence: 99%

Geometric anisotropic Semi-variogram analysis of ozone levels in Daerah Istimewa Yogyakarta, Indonesia

Siswanto,

Mirna,

Heryanto

et al. 2024

Phys. Scr.

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Identification of areas with high O3 levels that pose a risk to public health is necessary. Ordinary co-kriging is a geostatistical method that determines the value of primary variables at specific locations using weighted values of secondary parameters. A Semi-variogram is required to demonstrate the spatial correlation between the observations measured using this method. This study aims to determine the best Semi-variogram model and produce a map of the predicted O3 level interpolation results using the ordinary co-kriging method with a geometric anisotropic Semi-variogram. Data from the first quarter of 2018’s air quality monitoring in Daerah Istimewa Yogyakarta (DIY) were used to interpolate O3 levels, with 72 points for CO levels and 53 points for O3 levels. The results showed that the Semi-variogram model with the lowest mean error (ME) value is a gaussian model that differs from the spherical model by only 0.003. The Gaussian model has the lowest root mean squared error (RMSE), but it is only 0.002 different from the spherical model. However, by comparing the mean squared deviation ratio (MSDR) values of the three models, the spherical model's MSDR value is the lowest. A comprehensive analysis showed that the spherical geometric anisotropic Semi-variogram model performed superior, resulting in the smallest minimum mean error (ME), root mean square error (RMSE) and minimum squared deviation ratio (MSDR) values. These findings highlight the potential of this approach to accurately map the spatial distribution of O3 and support evidence-based decision-making related to public health.

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Precision Agriculture and Remote Sensing

Cited by 13 publications

References 5 publications

Economic and Social Barriers of Precision Farming in Hungary

Economic and Social Barriers of Precision Farming in Hungary

Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks

Geometric anisotropic Semi-variogram analysis of ozone levels in Daerah Istimewa Yogyakarta, Indonesia

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