Laboratory and Full Boom-Based Investigation of Nozzle Setup Error Effects on Flow, Pressure, and Spray Pattern Distribution

Forney, Shane H.; Luck, Joe D.; Kocher, Michael F.; Pitla, Santosh K.

doi:10.13031/aea.12043

Cited by 18 publications

(29 citation statements)

References 21 publications

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“…) at the University of Nebraska‐Lincoln in Lincoln, NE, USA to evaluate how nozzle type, boom pressure, and PWM duty cycle influenced spray pattern uniformity. Patternator construction and operation were described in detail in previous literature. In short, the patternator measured the amount of time needed to fill fixed‐volume (166 mL) individual collection tubes spaced 2.5 cm apart.…”

Section: Methodsmentioning

confidence: 99%

“…Greater CV values indicate greater dispersion and variability within the spray pattern. A CV below 10% indicates a desirable spray pattern uniformity, while a CV greater than 15% is unacceptable for an application

CV (proportion) = \frac{\sqrt{\frac{\sum_{i}^{n} {((), x_{i} - \overset{x}{true})}^{2}}{n - 1}}}{\frac{\sum_{i}^{n} x_{i}}{n}}

where:…”

Section: Methodsmentioning

confidence: 99%

“…In addition to CV, alternative methods of evaluating spray pattern uniformity were tested as previous hypotheses have indicated CV may not be a good representation of the entire spray pattern variation present . The root mean square error (RMSE) and average percent error (APE) were calculated using theoretical uniform flow rate data based on an assumption of an ideal uniform spray pattern across the collection width paired with the expected theoretical flow output.…”

Section: Methodsmentioning

confidence: 99%

“…However, it was previously noted that venturi nozzles have greater variability in spray pattern distribution, especially at low application pressures, compared to non‐venturi nozzles, which in turn contributes to a loss in weed control . Additionally, a multitude of nozzle factors were observed to influence spray pattern uniformity, including tip material, orifice wear, lateral angle, spacing, pitch angle, and incorrect selection …”

Section: Introductionmentioning

confidence: 99%

“…Narrow nozzle spacing (<51 cm) reduced CV values and buffered the negative effects of reduced boom heights and spray pressures on pattern uniformity. However, improper sprayer setup, specifically in regards to nozzle selection and placement, may be the greater cause of spray pattern deformities in current pesticide applications . Krishnan et al (1988) showed that crosswinds increased pattern CV values compared to headwinds of the same velocity, especially at increased spray pressures .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluation of spray pattern uniformity using three unique analyses as impacted by nozzle, pressure, and pulse‐width modulation duty cycle

Butts

Luck

Fritz

et al. 2019

Pest Management Science

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BACKGROUND: The increasing popularity of pulse-width modulation (PWM) sprayers requires that application interaction effects on spray pattern uniformity be completely understood to maintain a uniform overlap of spray, thereby reducing crop injury potential and maximizing coverage on target pests. The objective of this research was to determine the impacts of nozzle type (venturi vs. non-venturi), boom pressure, and PWM duty cycle on spray pattern uniformity. Research was conducted using an indoor spray patternator located at the University of Nebraska-Lincoln in Lincoln, NE, USA. Coefficient of variation (CV), root mean square error (RMSE), and average percent error (APE) were used to characterize spray pattern uniformity. RESULTS: Generally, across nozzles and pressures, the duty cycle minimally impacted the CV of spray patterns. However, across nozzles and duty cycles, increasing pressure decreased CV values, resulting in more uniform spray patterns. The RMSE values typically increased as pressure and duty cycle increased across nozzles. This may be the result of a correlation between RMSE values and flow rate as RMSE values also increased as nozzle orifice size increased. Generally, APE increased as the duty cycle decreased across nozzles and pressures with significant increases (40%) caused by the 20% duty cycle. Within non-venturi nozzles, increasing pressure reduced APE across duty cycles, while venturi nozzles followed no such trend. CONCLUSION: Overall, results suggest PWM duty cycles at or above 40% minimally impact spray pattern uniformity. Further, increased application pressures and the use of non-venturi nozzles on PWM sprayers increase the precision and uniformity of spray applications.

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Section: Methodsmentioning

confidence: 99%

CV (proportion) = \frac{\sqrt{\frac{\sum_{i}^{n} {((), x_{i} - \overset{x}{true})}^{2}}{n - 1}}}{\frac{\sum_{i}^{n} x_{i}}{n}}

where:…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of spray pattern uniformity using three unique analyses as impacted by nozzle, pressure, and pulse‐width modulation duty cycle

Butts

Luck

Fritz

et al. 2019

Pest Management Science

View full text Add to dashboard Cite

show abstract

Boom sprayer optimizations for bed‐grown carrots at different growth stages based on spray distribution and droplet characteristics

Zwertvaegher

Lamare

Douzals

et al. 2022

Pest Management Science

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BACKGROUND Pesticide losses and uneven spray distribution should be avoided as much as possible as they reduce the effectiveness of spraying and increase environmental contamination as well as costs. Within the H2020‐project OPTIMA the goal is to develop a smart sprayer for bed‐grown carrots, including optimizations such as air support and variable nozzle spacing. This paper focuses on selecting the most optimal nozzle types, spacing and height for spraying bed‐grown crops, while taking into account different target zone widths depending on the growth stage, based on spray distribution and droplet characterization measurements. RESULTS The results indicate that four bed spray configurations consisting of four nozzles per bed, i.e. XR8004/XR8004/XR8004/XR8004, AIUB8504/AI11004/AI11004/AIUB8504, AI8004/AI8004/AI8004/AI8004 and XR8002/XR8002/XR8002/XR8002, spraying at 300 kPa and recalculated to 12.0 km h−1 forward speed, are appropriate for spraying different target zone widths (ranging from 1.2 to 2.2 m) with high uniformity (CV < 12%) and minimal losses out of the target zone (<17%) when applied at the most appropriate nozzle spacing and height (varying from 0.35 to 0.65 m). Droplet characterization measurements showed that for the same nozzle size and spray pressure, air inclusion nozzles produced larger but slower droplets than standard flat‐fan nozzles. Air support increased the droplet velocities but had only a very limited effect on droplet size. CONCLUSION Laboratory spray distribution and droplet characterization measurements allowed selection of the most optimal nozzle type, spacing and height for bed spray applications in terms of reduced pesticide losses compared to conventional broadcast applications. © 2022 Society of Chemical Industry.

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Generalized data-driven optimal path planning framework for uniform coverage missions using crop spraying UAVs

Nanavati¹,

Meng²,

Coombes³

et al. 2023

Precision Agric

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Unmanned aerial vehicle (UAV) based crop spraying has become a popular alternative in the field of precision agriculture. One of the key goals of UAV based spraying is achieving spray coverage that is as uniform as possible to ensure maximum spray efficacy. Most of the existing studies in the literature focus on analysing the effects of spraying parameters on the uniformity of coverage distribution using experimental studies. However, in this work, we propose a novel generalized data-driven optimal path-planning framework aimed at finding the optimal operational flight parameters (flight speed and pass widths) for a lawnmower coverage path plan to meet the specified spray coverage rate while ensuring the uniformity. The framework takes a spray distribution model as an input and computes the optimal operational parameters for the coverage path plan to minimize coverage non-uniformity without making any assumptions on the UAV type. Furthermore, we also propose a neural network structure using Gaussian kernel neurons to design the spraying model using experimental data. The neural network structure makes no assumption about the type of UAV, onboard nozzle placement, or the flight parameters. The accuracy of the modelling solution only depends on the quality of the training data. In other words, higher diversity of the training data in terms of the flight and spraying parameters would result in a modelling solution that is more representative of the spraying distribution and consequently improve the quality of the operational parameters obtained from the proposed optimization framework. In this work, we present a case study to demonstrate the use case and test the performance of the proposed framework via simulation and experiments using the DJI AGRAS-T10 drone. The results showed that the optimal pass-width solutions for low forward speeds were similar to optimizing the positioning of the nozzles on a boom sprayer to achieve uniform coverage. Whereas, at high speeds, the pass-width was comparatively higher as the spread of the effective coverage over each pass increased. A discussion contextualized in the case study is provided to highlight the salient features and limitations of the proposed framework.

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Laboratory and Full Boom-Based Investigation of Nozzle Setup Error Effects on Flow, Pressure, and Spray Pattern Distribution

Cited by 18 publications

References 21 publications

Evaluation of spray pattern uniformity using three unique analyses as impacted by nozzle, pressure, and pulse‐width modulation duty cycle

Evaluation of spray pattern uniformity using three unique analyses as impacted by nozzle, pressure, and pulse‐width modulation duty cycle

Boom sprayer optimizations for bed‐grown carrots at different growth stages based on spray distribution and droplet characteristics

Generalized data-driven optimal path planning framework for uniform coverage missions using crop spraying UAVs

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