Agriculture drones: A modern breakthrough in precision agriculture

Puri, Vikram; Nayyar, Anand; Raja, Linesh

doi:10.1080/09720510.2017.1395171

Cited by 283 publications

(153 citation statements)

References 4 publications

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“…Consequently, in recent years, a drone-deployed solution has become increasingly attractive. The benefits of such methods for precision agriculture have been discussed extensively [62,63], but in summary, their advantage over other methods is due to a combination of factors, namely i) the potential for flexible, timely data capture, ii) an ability to capture higher spatial and spectral resolution imagery, iii) an increasing choice of sensors, iv) lower tasking and setup costs and v) the promise of quick data-to-information turn around. Drone-deployed, multispectral agri-sensors typically employ 4-6 bands, covering the Green, Red, RE and NIR regions of the EMS and are also typically centered in regions associated with Chl sensitivity, or "greenness" sensitivity, and thus complement the spectral ranges of many hand-held sensors (Figure 8).…”

Section: Selecting An Appropriate Agri-sensor For Frost-stress Detectionmentioning

confidence: 99%

Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing

et al. 2020

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Radiant frost during the reproductive stage of plant growth can result in considerable wheat (Triticum aestivum L.) yield loss. Much effort has been spent to prevent and manage these losses, including post-frost remote sensing of damage. This study was done under controlled conditions to examine the effect of imposed frost stress on the spectral response of wheat plant components (heads and flag leaves). The approach used hyperspectral profiling to determine whether changes in wheat components were evident immediately after a frost (up to 5 days after frosting (DAF)). Significant differences were found between frost treatments, irrespective of DAF, in the Blue/Green (419–512 nanometers (nm)), Red (610–675 nm) and Near Infrared (NIR; 749–889 nm) regions of the electromagnetic spectrum (EMS) in head spectra, and in the Blue (415–494 nm), Red (670–687 nm) and NIR (727–889 nm) regions in the leaf spectra. Significant differences were found for an interaction between time and frost treatment in the Green (544–575 nm) and NIR (756–889 nm) in head spectra, and in the UV (394–396 nm) and Green/Red (564–641 nm) in leaf spectra. These findings were compared with spectral and temporal resolutions of commonly used field agricultural multispectral sensors to examine their potential suitability for frost damage studies at the canopy scale, based on the correspondence of their multispectral bands to the results from this laboratory-based hyperspectral study.

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Section: Selecting An Appropriate Agri-sensor For Frost-stress Detectionmentioning

confidence: 99%

Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing

et al. 2020

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“…Thus, several reviews exist for their application in Precision Agriculture and Smart Farming. Most of the reviews focus mainly on the different types of applications that UAVs can have in agricultural crops [7][8][9][10][11][12] or environmental monitoring in general [13]. In [14], the authors reviewed the hyperspectral imagery and the techniques used in these cases.…”

Section: Introductionmentioning

confidence: 99%

A Review on UAV-Based Applications for Precision Agriculture

2019

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Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

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“…Their applications vary, but usually involve remote sensing, mapping, land modelling and precision agriculture. The most common use of UAVs in weed management is to acquire multispectral images in the visible and infrared spectrum for weed identification and mapping, however, a number of other potential uses have emerged, including the application of pesticides and other agrochemicals by the UAV itself [2]. Mapping weeds using UAVs provides high spatial resolutions, usually on the order of a few centimetres.…”

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confidence: 99%

Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

et al. 2018

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Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications.hard to control using herbicides in fields, and its thorny leaves and high posture are a nuisance to grazing animals in pastures. It grows in dense strands and is more competitive than grass weeds, growing taller to overshadow adjacent species that compete for light [1].In recent years, unmanned aerial vehicles (UAVs) have proven to be useful in agricultural management. Their applications vary, but usually involve remote sensing, mapping, land modelling and precision agriculture. The most common use of UAVs in weed management is to acquire multispectral images in the visible and infrared spectrum for weed identification and mapping, however, a number of other potential uses have emerged, including the application of pesticides and other agrochemicals by the UAV itself [2]. Mapping weeds using UAVs provides high spatial resolutions, usually on the order of a few centimetres. This is vital in order to detect small weeds, weeds at a young stage, or within low crop cover with high background soil signal. In addition to the advantage of spatial resolution, UAVs offer temporal flexibility and can collect data at any time that fits the user's requirements, as compared to the satellite images. Moreover, UAVs can acquire images during overcast conditions, thus bypassing a frequent problem with satellite remote sensing, although cloud shadows and varying illumination conditions during image acquisition may affect data quality [3,4].A chall...

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Agriculture drones: A modern breakthrough in precision agriculture

Cited by 283 publications

References 4 publications

Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing

Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing

A Review on UAV-Based Applications for Precision Agriculture

Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

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