Droplet distributions in cotton harvest aid applications vary with the interactions among the unmanned aerial vehicle spraying parameters

Chen, Pengchao; Ouyang, Fan; Wang, Guobin; Huang, Qi; Xu, Weicheng; Yang, Weiguang; Zhang, Yali; Lan, Yubin

doi:10.1016/j.indcrop.2021.113324

Cited by 46 publications

(26 citation statements)

References 16 publications

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“…According to the study developed in Part 3, the drift of UASS is simultaneously affected by multiple operating parameters. Current research focuses on the influence of a specific factor or a small number of factors on drift while ignoring the interactions between multiple factors ( Chen P. et al, 2021 ). The choice of operating parameters can be easily affected by the experience level of the operator, and the use of models can reduce the risk of drift caused by human decision-making errors ( Chen et al, 2022 ).…”

Section: Discussion and Further Recommendationsmentioning

confidence: 99%

“…This chapter reviews the factors that affect the drift of the UASS including atomization, downwash airflow, and the relative movement. The atomization factor caused by the structural design of the spraying system includes the selection of nozzles, the layout of nozzles and rotors, and the properties of the liquid ( Chen P. et al, 2021 ). For the downwash airflow, the number and size of rotors and payload were investigated.…”

Section: Introductionmentioning

confidence: 99%

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Characteristics of unmanned aerial spraying systems and related spray drift: A review

et al. 2022

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Although drift is not a new issue, it deserves further attention for Unmanned Aerial Spraying Systems (UASS). The use of UASS as a spraying tool for Plant Protection Products is currently explored and applied worldwide. They boast different benefits such as reduced applicator exposure, high operating efficiency and are unconcerned by field-related constraints (ground slope, ground resistance). This review summarizes UASS characteristics, spray drift and the factors affecting UASS drift, and further research that still needs to be developed. The distinctive features of UASS comprise the existence of one or more rotors, relatively higher spraying altitude, faster-flying speed, and limited payload. This study highlights that due to most of these features, the drift of UASS may be inevitable. However, this drift could be effectively reduced by optimizing the structural layout of the rotor and spraying system, adjusting the operating parameters, and establishing a drift buffer zone. Further efforts are still necessary to better assess the drift characteristics of UASS, establish drift models from typical models, crops, and climate environments, and discuss standard methods for measuring UASS drift.

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Section: Discussion and Further Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characteristics of unmanned aerial spraying systems and related spray drift: A review

et al. 2022

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“…In the literature, there are various studies that discuss the uniformity of coverage achieved by UAV based spraying. In studies like (Chen et al, 2021;Guo et al, 2021a, b;Meng et al, 2019Meng et al, , 2020Wang et al, 2018), the effects of operational parameters on the coverage uniformity with the UAV flying in lawnmower path were discussed. The primary focus of these studies was to map the effect of the flight heights, speeds, droplet size, crop phenotype and spray volumes on the coverage distribution.…”

Section: Related Workmentioning

confidence: 99%

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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“…The changing in flight speed, flight height, and vertical altitude of the boom after the rotor and the adjacent distance among the nozzles significantly affect the droplet deposition and drift behind the fuselage [18]. Depressing drone flying height increases the spray capacity and improves droplet distribution at the cotton canopy bottom [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spray Droplet and Deposition of Selected Nozzles Using Image Processing Techniques

Kailashkumar¹,

Sivakumar²,

Gunasekar³

et al. 2022

ujar

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Droplet distributions in cotton harvest aid applications vary with the interactions among the unmanned aerial vehicle spraying parameters

Cited by 46 publications

References 16 publications

Characteristics of unmanned aerial spraying systems and related spray drift: A review

Characteristics of unmanned aerial spraying systems and related spray drift: A review

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

Analysis of Spray Droplet and Deposition of Selected Nozzles Using Image Processing Techniques

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