Develop an unmanned aerial vehicle based automatic aerial spraying system

Xue, Xian; Lan, Yubin; Sun, Zhaoqing; Chang, Ching-Ter; Hoffmann, W. C.

doi:10.1016/j.compag.2016.07.022

Cited by 158 publications

(83 citation statements)

References 4 publications

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“…Positioning accuracy is of prime importance for precise management of agricultural operations. Different PA applications require different positioning accuracies (Perez-Ruiz and Upadhyaya 2012): (i) low accuracy (meter level) can be used for asset management, tracking and tracing; (ii) medium accuracy (sub-meter level) can be used for tractor guidance, via manual control, for lower accuracy operations such as spraying (Xue et al 2016), spreading, harvesting bulk crops and for area measurement and field mapping (Auernhammer et al 1994); (iii) high accuracy (cm level) can be used for auto-steering systems on tractors and self-propelled machines (harvesters and sprayers) (Gan-Mor et al 2007;Dijksterhuis et al 1998;Bell 2000) and for precision operations such as planting (Sun et al 2010;Ehsani et al 2004). In PA, it is well recognized that Global Navigation Satellite Systems (GNSS) are the major enabler of 'precision' (Larsen et al 1994;Krüger et al 1994).…”

Section: Introductionmentioning

confidence: 99%

Multi-GNSS precise point positioning for precision agriculture

Guo

et al. 2018

Precision Agric

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The main objective of this research was to examine the feasibility of Multi-GNSS precise point positioning (PPP) in precision agriculture (PA) through a series of experiments with different working modes (i.e. stationary and moving) under different observation conditions (e.g. open sky, with buildings or with canopy). For the stationary test carried out in open space in the UK, the positioning accuracy achieved was 13.9 mm in one dimension by a PPP approach, and the repeatability of positioning results was improved from 19.0 to 6.0 mm by using Multi-GNSS with respect to GPS only. For the moving test carried out in similar location in the UK, almost the same performance was achieved by GPS-only and by Multi-GNSS PPP. However, for a moving experiment carried out in China with obstruction conditions, Multi-GNSS improved the accuracy of baseline length from 126.0 to 35.0 mm and the repeatability from 110.0 mm to 49.0 mm, The results suggested that the addition of the BeiDou, Galileo and GLONASS systems to the standard GPS-only processing improved the positioning repeatability, while a positioning accuracy was achieved at about 20 mm level in the horizontal direction with an improvement against the GPS-only PPP results. In space-constrained and harsh environments (e.g. farms surrounded with dense trees), the availability and reliability of precise positioning decreased dramatically for the GPS-only PPP results, but limited impacts were observed for Multi-GNSS PPP. In addition, compared to real time kinematic (RTK) GNSS, which is currently most commonly used for high precision PA applications, similar accuracy has been achieved by PPP. In contrast to RTK GNSS, PPP can provide high accuracy positioning with higher flexibility and potentially lower capital and running costs. Hence, PPP might be a great opportunity for agriculture to meet the high accuracy requirements of PA in the near future.

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Section: Introductionmentioning

confidence: 99%

Multi-GNSS precise point positioning for precision agriculture

Guo

et al. 2018

Precision Agric

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“…In recent years, UAVs have been developed to conduct aerial applications of crop protection chemicals . UAV sprayers are capable of treating agricultural areas over complex geographic terrain not easily accessible to personnel or ground‐based machinery .…”

Section: Introductionmentioning

confidence: 99%

“…7,9,10 In recent years, UAVs have been developed to conduct aerial applications of crop protection chemicals. [11][12][13][14][15][16] UAV sprayers are capable of treating agricultural areas over complex geographic terrain not easily accessible to personnel or ground-based machinery. 15 Commercial UAV pesticide applications have become available in the USA even though a literature gap concerning the need to optimize the use of this technology presently exists.…”

Section: Introductionmentioning

confidence: 99%

Integration of remote‐weed mapping and an autonomous spraying unmanned aerial vehicle for site‐specific weed management

Hunter

Gannon

Richardson

et al. 2019

Pest Management Science

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BACKGROUND Unmanned aerial vehicles (UAVs) have been used in agriculture to collect imagery for crop and pest monitoring, and for decision‐making purposes. Spraying‐capable UAVs are now commercially available worldwide for agricultural applications. Combining UAV weed mapping and UAV sprayers into an UAV integrated system (UAV‐IS) can offer a new alternative to implement site‐specific pest management. RESULTS The UAV‐IS was 0.3‐ to 3‐fold more efficient at identifying and treating target weedy areas, while minimizing treatment on non‐weedy areas, than ground‐based broadcast applications. The UAV‐IS treated 20–60% less area than ground‐based broadcast applications, but also missed up to 26% of the target weedy area, while broadcast applications covered almost the entire experimental area and only missed 2–3% of the target weeds. The efficiency of UAV‐IS management practices increased as weed spatial aggregation increased (patchiness). CONCLUSION Integrating UAV imagery for pest mapping and UAV sprayers can provide a new strategy for integrated pest management programs to improve efficiency and efficacy while reducing the amount of pesticide being applied. The UAV‐IS has the potential to improve the detection and control of weed escapes to reduce/delay herbicide resistance evolution. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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“…These early pesticide sprayers were normally petrol-powered, manually piloted, and relatively expensive. More recently, a wide range of low-cost, electronic, autonomously controlled multirotor UAVs, such as the DJI AGRAS series, have been developed (Ru, Zhou, Fan, Wu, 2011;Xue, Lan, Sun, Chang, Hoffmann, 2016;Shim, Han, Yeo, 2009;Pederi & Cheporniuk, 2015;Yun, Mazur, Pederii, 2017). The rapid technological advancements in both sensing and dispensing using UAVs promises a future where they can work together to enable precise and closed-loop pest management (Fig.…”

Section: Introductionmentioning

confidence: 99%

“…The spray distribution is unlikely uniform spatially. If a multi-rotor UAV is used as the dispensing platform (Ru et al, 2011;Xue et al, 2016;Shim et al, 2009;Pederi & Cheporniuk, 2015;Yun et al, 2017), even on a windless day, spray densities may vary considerably depending on the distance between the sprayed region and the UAV. Moreover, if wind is present, the downdraft from the UAV rotors will interact with the wind, causing drift and further complicating the spray distribution pattern.…”

Section: Introductionmentioning

confidence: 99%

Mitigating ground effect on mini quadcopters with model reference adaptive control

Wei

Chan

Lee

et al. 2019

Int J Intell Robot Appl

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Multirotor unmanned aerial vehicles (UAVs), or drones, are increasingly being used to spray liquid pesticides to control emerging pest infestations in field crops. In recent years, UAVs have been used to release predatory mites and other natural enemies to optimise and promote sustainable pest management practices by relying less on conventional insecticides. Drone dispensed samples of predatory mites are typically mixed with a granular material, vermiculite, which serves as a filler. The low density of the vermiculite and weather conditions (mainly wind), influences the distribution pattern of predatory mites when delivered by a UAV-based system. The purpose of this paper is to present a data-driven methodology to develop a mathematical model that can be used to optimise UAV-based autonomous dispensing of predatory mites. The model characterises the distribution of vermiculite as a function of wind speed and direction, and the UAVs altitude and forward speed. The model is constructed by first conducting outdoor experiments and then using machine-learning techniques on the collected data. The constructed model produced an average generalisation error of 12.8%, RMSE. Due to its parametric and predictive nature, the model is amenable for the future design of UAV flight controllers that can compensate for the targeting error caused by wind. The proposed modelling methodology could be useful not only for the dispensing of predatory mites, but also for other UAV dispensing applications, such as liquid or granular pesticide deliveries.Keywords: unmanned aerial vehicle, precision pest management, machine learning, natural enemies, precision agriculture, predatory mites. NomenclatureUAV unmanned aerial vehicle X the direction perpendicular to UAV flight path RMSE root mean squared error wx wind speed in the X direction, km h -1 ℎ UAV altitude, m wy wind speed along the flight path, km h -1 x lateral offset in the X direction, m R 2 statistic, coefficient of determination vermiculite density, g m -2 wind speed, km h -1 UAV forward speed, km h -1 wind direction with respect to UAV heading, °

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Develop an unmanned aerial vehicle based automatic aerial spraying system

Cited by 158 publications

References 4 publications

Multi-GNSS precise point positioning for precision agriculture

Multi-GNSS precise point positioning for precision agriculture

Integration of remote‐weed mapping and an autonomous spraying unmanned aerial vehicle for site‐specific weed management

Mitigating ground effect on mini quadcopters with model reference adaptive control

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