Quantifying Spray Deposition from a UAV Configured for Spot Spray Applications to Individual Plants

Brian Richardson; Carol A. Rolando; Mark O. Kimberley

doi:10.13031/trans.13724

Quantifying Spray Deposition from a UAV Configured for Spot Spray Applications to Individual Plants

Transactions of the ASABE ◽

10.13031/trans.13724 ◽

2020 ◽

Vol 63 (4) ◽

pp. 1049-1058

Author(s):

Brian Richardson ◽

Carol A. Rolando ◽

Mark O. Kimberley

Keyword(s):

Wind Speed ◽

Normal Distribution ◽

Spray Deposition ◽

Bivariate Normal Distribution ◽

Model Parameters ◽

Two Dimensional ◽

Bivariate Normal ◽

Sampling Grid ◽

Droplet Sizes ◽

Spray Deposit

HighlightsSpot spraying is a method for applying pesticides to individual tree crowns.A new method is presented to quantify and analyze the two-dimensional spot spray deposit pattern produced by a UAV.A bivariate normal distribution provided a good fit to the observed deposition data for all treatments.Model parameters effectively described the shape of the ground deposits.Abstract. The purpose of this study was to develop a method for quantifying and analyzing the two-dimensional spray deposit pattern produced from a UAV spot spraying system for applying pesticides to individual plants with crown diameters in the range of 1 to 2 m. An XAG P20 UAV was flown over the center of a sampling grid, and spray deposits from three droplet size treatments, with nominal volume median diameters (VMDs) of 335, 430, and 1150 µm, were measured using horizontal steel plate collectors placed on blocks on the ground. A colorimetric tracer in the spray mix was used to quantify spray deposition. The positioning accuracy of the UAV was excellent, but the droplet sizes produced were much larger than expected. A bivariate normal distribution provided a good fit to the observed deposition data for all treatments. Model parameters effectively described the shape of the ground deposits. Displacement of the deposit distribution center was in a downwind direction. While there were no statistically significant effects of wind speed on the shape or degree of displacement of the center of mass of the observed ground deposit pattern, this was probably a result of the low wind speeds during the study, which were often close to or below the lower sensitivity threshold of the anemometer used. The actual spray coverage on a 2 m tall artificial tree target of 1 or 2 m diameter placed in the center of the plot was consistent across the range of droplet sizes and operating conditions tested. Nevertheless, it is hypothesized that targeting could be further improved if the UAV was slightly offset in an upwind direction and, conceptually, the degree of this displacement would increase as wind speed increased. A sampling grid spacing of 1.0 m would have produced results similar to the 0.5 m spacing actually used. Keywords: Aerial spraying, Pesticides, Spot spraying, Spray deposition, UAV, Unmanned aerial vehicle.

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A note on the generalization of bivariate normal quadrant probabilities to bivariate elliptical distributions

10.31234/osf.io/j4vm3 ◽

2018 ◽

Author(s):

Oscar Lorenzo Olvera Astivia

Keyword(s):

Normal Distribution ◽

Bivariate Normal Distribution ◽

Elliptical Distributions ◽

Bivariate Normal ◽

Geometric Argument

I present a geometric argument to show that the quadrant probability for the bivariate normal distribution can be generalized to the case of all elliptical distributions.

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PSIII-5 Comparison of Bivariate Machine Learning and Linear Model for Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios

Journal of Animal Science ◽

10.1093/jas/skaa278.424 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 230-231

Author(s):

Sunday O Peters ◽

Mahmut Sinecan ◽

Kadir Kizilkaya ◽

Milt Thomas

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Normal Distribution ◽

Genomic Prediction ◽

Network Models ◽

Bivariate Normal Distribution ◽

Snp Markers ◽

Neural Network Models ◽

Bivariate Normal ◽

Artificial Neural

Abstract This simulation study used actual SNP genotypes on the first chromosome of Brangus beef cattle to simulate 0.50 genetically correlated two traits with heritabilities of 0.25 and 0.50 determined either by 50, 100, 250 or 500 QTL and then aimed to compare the accuracies of genomic prediction from bivariate linear and artificial neural network with 1 to 10 neurons models based on G genomic relationship matrix. QTL effects of 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals were sampled from a bivariate normal distribution. In each QTL scenario, the breeding values (Σgijβj) of animal i for two traits were generated by using genotype (gij) of animal i at QTL j and the effects (βj) of QTL j from a bivariate normal distribution. Phenotypic values of animal i for traits were generated by adding residuals from a bivariate normal distribution to the breeding values of animal i. Genomic predictions for traits were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons and linear (GBLUP) models. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations from 10-fold cross validation for traits were used to assess predictive ability of bivariate linear (GBLUP) and artificial neural network models based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that the trait with high heritability (0.50) resulted in higher correlation than the trait with low heritability (0.25) in bivariate linear (GBLUP) and artificial neural network models. However, bivariate linear (GBLUP) model produced higher correlation than bivariate neural network. Panel1 performed the best correlations for all QTL scenarios, then Panel2 including QTL and SNP markers resulted in better prediction than Panel3.

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