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Page 1: drones presentation.pptx

welcome

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UNMANNED AERIAL VEHICLE AND IMAGE ANALYSIS IN PLANT DISEASE

DIAGNOSIS2

UNIVERSITY OF AGRICULTURAL SCIENCES, DHARWAD

AGRICULTURE COLLEGE, VIJAYPUR

DEPARTMENT OF PLANT PATHOLOGY

RASHMI U.S.PGS14AGR6604

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INTRODUCTION…• An unmanned aerial vehicle (UAV), commonly

known as a drone and referred to as a Remotely Piloted Aircraft (RPA) by the International Civil Aviation Organization (ICAO), is an aircraft without a human pilot aboard.

• Its flight is controlled either autonomously by onboard computers or by the remote control of a pilot on the ground or in another vehicle.

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So DRONES stand for……

DYNAMIC REMOTELY OPERATED

NAVIGATION SYSTEM

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• In 1906, George Lawrence used a kite to take a panoramic view of the ruins of San Francisco after the earthquake.

• WORLD WAR 1, 1918 in Palestine general Allenby used 5 pilots to photograph a 1620 sq km

• 1935, Commercial aerial photography.• During World War Two 1939-1945, aerial reconnaissance was one

of the key methods of obtaining intelligence about the enemy and their activities

• Cold war: In the 40s and 50s secret surveys were undertaken by the US, UK and Russian Military because of security issues and the onset of the cold war

• 1980 – 1990: arrival of colour film.

HISTORY OF AERIAL PHOTOGRAPHY

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• During the 90’s aerial photography had entered the digital age. It was now possible to scan the photos and distribute them as large seamless mosaics, thus paving the way for GIS.

• 2004- Large format cameras available. These large format aerial survey cameras are

often fitted with GPS and inertial Measurement units, which improved on the accuracy and time to create orthophotos.

DIGITAL AERIAL PHOTOGRAPHY

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2004 UNMANNED AERIAL VEHICLES

“Since the development of digital cameras, low cost GPS/INS systems and other sensors have meant, the technology has now advanced toward the automation of the aerial unmanned vehicle operations and the acquisition of high resolution images using off-the-shelf cameras”

Falcon F8 Octocopter

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Types of DRONES11

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Parameters for classification

1. Flight altitude2. Endurance3. Speed4. Payload5. Maximum take off weight6. Size

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Agricultural Drones includes..• Relatively cheap drones with advanced sensors

and imaging capabilities.• Equipped with autopilot using GPS and a

standard point and shoot camera.• Collects data such as patterns in irrigation, soil

variation, pest infestation etc.

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Aerial Photography Pipe/Power line Surveillance

Real-estate Mapping Border Surveillance

Transport Crowd Control

Suspect Tracking Mail/Fright Transport

Traffic Monitoring Agricultural Applications

Disaster Response/Relief Movie Productions

Damage Assessment Sporting Event Coverage

Infrastructure Monitoring Mining detection

Fertilizer Management Fishery Control

Overview where Drones are used today ?

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Application in agriculture

In precision agriculture,

• Possibilities include monitoring plant health,

• Detecting early outbreak of pests and diseases,

• Refining fertiliser applications or scheduling irrigation.

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Mini UAV(mainly agriculture)

1. Maximum take off weight: < 30 kg

2. Maximum Flight altitude: 150-300 m

3. Endurance: < 2 hours

4. Data link range: < 10 km

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FIXED WING

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Quadcopter Octocopter

Mini UAV MK Octo (Hisystems, GmbH) equipped with thermal imaging system

HELICOPTER TYPE

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Three main components

Horizon Scanning Spectral Response

Imaging

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Horizon Scanning Reviewing remote sensing solutions

SatellitesManned UAV

Future Platforms: High Altitude Platforms – Stratollites, HALE UAV’s

Future : Miniaturisation and Hyper spectral LIDAR, SAR, Imaging Spectrometers

Costs (dedicated) €/year 1M to 4.5M 0.1M to 2.6M 3M to 14.5M

Imaging Capability 2 - 20cm 0.5 - 20cm 50 - 1500cmFOV 0.5 to 5km 50m – 5km 10 – 50km

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How does UAV work

• Flight Plan• Image analysis.

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Multispectral imaging• A multispectral image is one that captures

image data at specific frequencies across the electromagnetic spectrum.

• The wavelengths may be separated by filters or by the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range, such as infrared. 

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WAVE LENGTH APPROXIMATES• Blue: 450-515 to 520 nm, is used for atmosphere and deep

water imaging, and can reach depths up to 150 feet (50 m) in clear water.

• Green: 515-520 to 590-600 nm, is used for imaging vegetation and deep water structures, up to 90 feet (30 m) in clear water.

• Red: 600-630 to 680-690 nm, is used for imaging man-made objects, in water up to 30 feet (9 m) deep, soil, and vegetation.

• Near infrared: 750-900 nm, is used primarily for imaging vegetation.

• Mid-infrared: 1550-1750 nm, is used for imaging vegetation, soil moisture content, and some forest fires.

• Mid-infrared: 2080-2350 nm, is used for imaging soil, moisture, geological features, silicates, clays, and fires.

• Thermal infrared: 10400-12500 nm, uses emitted instead of reflected radiation to image geological structures, thermal differences in water currents, and fires, and for night studies.

• Radar: and related technologies are useful for mapping terrain and for detecting various objects.

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Hyperspectral Sensing Provides reflectance values over a continuous spectrum ( Visible, NIR and MIR)

Useful in estimation of crop vigor and yield prediction

Discrimination between weeds and crops/genotypes

Quantitative measurements of Crop Water Content and LAI

In MIR band - Plant nutrient status and soil properties

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Normalized Difference Vegetation Index – NDVI Imaging

• Plants typically absorb visible blue and visible red light while reflecting green and Near Infrared (NIR) light.

• The reason we see a plant as green is because it is reflecting the green light to our eyes.

• Plants also reflect the NIR light because the infrared light doesn’t have enough energy to support photosynthesis. Healthy plants reflect green and NIR while absorbing blue and red light.

• As plants become sick, they don’t reflect green and NIR as well. There is a mathematical algorithm (ENDVI) that works in conjunction with a special camera that captures both visible and infrared bands of light.

• By processing the picture with ENDVI algorithm, you get a new picture that shows where plants are happy and where they are not.

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NDVI can provide……• Plant Stress Health • Fertilizer Optimisation • Plant disease diagnosis • Insect & Pest plant diagnosis • Help Develop farm plan • Guide cultivation plan • Develop harvest plan based on vigor

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PHOTOGRAMETTRIC SOFTWARES

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PHOTOGRAMMETRIC DATA PROCESSING

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Fig: Classical Photogrammetric Workflow for Data Processing

Fig: Photogrammetric Data Processing using Computer Vision

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Particulars

Aerial Photogrammetry UAV Photogrammetry

Data Acquisition Manual/Assisted Assisted/Manual/ AutomaticAerial Vehicle Highly stable specially designed

aircraftsSmall aerial Vehicles with certain payload capacity

GPS/INS Configurations cm-dm level accuracy cm-10 m

Image Resolution cm-m mm-mGround Coverage Km2 m2-km2

Cameras Well calibrated cameras especially designed for photogrammetric applications

Can work with normal digital cameras

Fudicial Marks Present AbsentFlying Height 100 m-10 km m-km

(not more than 1 km)

Data Processing Workflows

Standard Photogrammetric Workflow

No standard workflows

Salient Feature Better control over the output image quality

High temporal accuracy with real time applications

Com

parison between A

erial and UA

V P

hotogramm

etry

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Photogrammetric softwares Menci software APS suite EnsoMOSAIC UAV DroneMapper SimActive Pix4D SoftPlotter INPHO PHOTOMOD UASMasper 3DFZephyr

• Australis• Linearis3D• Photogrammetry• 3D VIA

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•The small unmanned aircraft systems market set to reach $582.2 million by the end of 2019 (FAA, 2014).

•Indian researcher (II Sc. Bengaluru) uses UAV for mapping farmlands.

•Half a dozen startups in India: AirPix, Asteria ,Aerospace, OM UAV systems;

•UAS Dharwad is taking the lead.

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Precision farming

• The American National Research Council defined precision agriculture (or precision farming) as “a management strategy that uses information technology to bring data from multiple sources to bear on decisions associated with crop production”.

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Conclusion

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