The impact and retention of spray droplets on a horizontal hydrophobic surface

Zwertvaegher, Ingrid; Verhaeghe, Micheline; Brusselman, Eva; Verboven, Pieter; Lebeau, Frédéric; Massinon, Mathieu; Nicolaı̈, Bart; Nuyttens, David

doi:10.1016/j.biosystemseng.2014.07.013

Cited by 43 publications

(31 citation statements)

References 28 publications

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“…Agricultural spray adjuvants (ASA) can influence the spray deposition and retention of pesticide droplets on the plant surface. When a droplet impacts a leaf surface, three outcomes are possible: adhesion, bounce, or shatter (Zwertvaegher et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Spray Retention on Coffee Leaves Associated With Type and Concentration of Adjuvants

et al. 2019

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Agricultural spray adjuvants (ASA) are widely used in pesticide applications to enhance the performance of pesticides. The aim of this research was to investigate the effects of ASA on static surface tension (SST) and foliar spray retention on coffee leaves. The SST of ASA at different concentrations was determined by the drop weight method. Spray retention on adaxial and abaxial coffee leaf surfaces was performed using a micro-sprayer at solution concentrations of 0, 0.01, 0.1, 0.5, and 1.0% v v-1. The ASA assessed were: polyether-polymethylsiloxane-copolymer (PPC); Nonylphenol ethoxylate; nonyl polyethylene glycol ether; mineral oil; nonylphenoxy polyethoxy ethanol; carboxyl copolymer of styrene and butadiene; primary aliphatic oxyalkylated alcohol plus carboxyl copolymer of styrene and butadiene; and soyal phospholipids and propionic acid. All ASA reduced the SST of the aqueous solutions. PPC provided the best performance in decreasing SST, reaching values below 20 mN m-1 at a concentration of 0.05% v v-1. Spray retention on leaves was influenced by adjuvant type as well as concentration. A very strong positive correlation between SST and spray retention on coffee leaves was observed. Decreasing the SST of the solution provided a reduction of spray retention when spraying was performed until runoff point.

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Section: Introductionmentioning

confidence: 99%

Spray Retention on Coffee Leaves Associated With Type and Concentration of Adjuvants

et al. 2019

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“…Furthermore, in this study, logistic regressions are used to demonstrate the probability of each impact outcome depending on the impact energy. Hierarchical multinomial logistic regressions were performed with the impact classes as binary dependent variables, and the droplet Weber number as the independent variable (Zwertvaegher et al, 2014). Probability distributions for adhesion, bounce and shatter were determined for a range of Weber numbers using the mnrfit function of the Matlab statistical toolbox, which uses an iteratively weighted least squares algorithm to find the maximum likelihood estimates, according to the model (one predictor, Wen):…”

Section: Droplet Impact Outcomes Classification and Logistic Regressimentioning

confidence: 99%

Spray droplet impaction outcomes for different plant species and spray formulations

et al. 2017

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A track-sprayer combined with a high-speed camera were used to visualize and identify droplet impaction outcomes for three formulations (water, 0.1% LI 700® (lecithin, a mixture of soya oils, propionic acid and surfactants) in water and 0.1% Pulse® (non-ionic surfactant, trisiloxane ethoxylate) in water) on four plant species (bean (Vicia faba L.), avocado (Persea americana L.), barnyard grass (Echinochloa crus-galli L. P. Beauv.) and cabbage (Brassica oleracea L.)) selected to represent a wide range of leaf surface characters. Droplet sizes and velocities were measured by image analysis and a multiple hypothesis tracking algorithm. Impaction outcomes were categorized into adhesion, bounce, or shatter. The probability of each outcome was estimated from logistic regression models related to the dimensionless Weber number. This approach is in contrast to various deterministic threshold criteria for droplet bounce or shatter that have been used to model droplet impaction events on leaves. It also provides a simple visual and numerical presentation of the complexity of impaction processes, and the relative influence of leaf surface character versus formulation for droplets with different impaction energies.

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“…To date, numerous scholars have focused on the theory surrounding the droplet-leaf interaction to identify the characteristics of target leaves and the optimal parameters of the air-assisted spray for droplet deposition on leaves [31], and the spray operation parameters can be optimized to suit different characteristics of the target leaves and orchard canopy structures [23,24]. However, research on droplet-leaf interaction has been based on static target leaves [32]. Minimal research has been conducted on dynamic target leaves (The movement of the leaf in a wind-driven state) and spray droplets.…”

Section: Introductionmentioning

confidence: 99%

Orchard Spray Study: A Prediction Model of Droplet Deposition States on Leaf Surfaces

Cui

et al. 2020

Agronomy

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During air-assisted spraying operations in orchards, the interaction between the droplets and the target leaves has a decisive influence on the retention of the droplets on the leaves and the final deposition state. Based on the observation of the final deposition effect of the droplets in the spray test, the retention state of the droplets on the leaves is divided into three categories: uniform distribution (hereinafter referred to as uniform), accumulation, and loss. During the initial interaction between the droplets and the leaves, the adhesion or sliding state of the droplets has an important influence on the final deposition state of the droplets, which is determined by the target leaf adhesion work in this paper. Based on obtaining the characteristic parameters of the leaf surface, a theoretical model of adhesion work related to parameters such as the contact angle, rough factor, and initial tilt angle of the leaf is established. Afterward, through the connection of the droplet coverage on the macro level, the establishment of the deposition state model of the droplet group on the leaf is completed. By conducting the experiment test based on the Box-Behnken design of response surface methodology (RSM), the droplet deposition states under the influence of the spray distance, fan outlet wind speed and droplet size were studied and compared with the predicted values. The test results show that the prediction accuracies of the three states of uniform, accumulation, and loss were 87.5%, 80%, and 100%, respectively. The results of the study indicate that the established prediction model can effectively predict the deposition states of droplets on leaves and provide a reference for the selection of spray operation parameters.

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The impact and retention of spray droplets on a horizontal hydrophobic surface

Cited by 43 publications

References 28 publications

Spray Retention on Coffee Leaves Associated With Type and Concentration of Adjuvants

Spray Retention on Coffee Leaves Associated With Type and Concentration of Adjuvants

Spray droplet impaction outcomes for different plant species and spray formulations

Orchard Spray Study: A Prediction Model of Droplet Deposition States on Leaf Surfaces

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