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International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal
Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016)
ISBN-978-93-85822-33-9 38
Drones for Normalized Difference Vegetation
Index (NDVI), to Estimate Crop Health for
Precision Agriculture: A Cheaper
Alternative for Spatial Satellite Sensors
Ujjwal Mahajan
1
and Bharat Raj Bundel
2
1,2
Lingaya’s University, Student (Department of Mechanical & Automobile Engineering.), Faridabad, Haryana, INDIA
2
Lingaya’s University, Department of Mechanical Engineering.), Faridabad, Haryana, INDIA
Abstract—Normalized Difference Vegetation Index (NDVI) data
used to estimate the health of green vegetation and post processed
high definition images for precision agriculture. Drone provide high-
resolution image taken of crops, it compares the reflected intensities
of near infrared (NIR) and visible light. Autonomous aircrafts are
improved and cost effective instruments for data acquisition, real-
time thermal imagery to the ground control station (GCS), and fastest
medium for quick time and critical analysis of the crop. The paper
represents an alternative and cheaper methodology to estimate the
growing crop health and stress by acquisition of data using drone
with modified airborne cameras and sensors. This paper represents
an alternative and cheaper method to estimate the crop health/stress
by acquisition of data using remotely piloted aircraft with airborne
sensors.
1.
INTRODUCTION
Drones can fly autonomously with dedicated software which
allows to make a flight plan and deploy the system with GPS
and feed in various parameters such as speed, altitude, ROI
(Region of Interest), geo-fence and fail-safe modes. Drones
are preferred over full size aircrafts due to major factors like
combination of high spatial resolution and fast turnaround
capabilities together with low operation cost and easy to
trigger. These features are required in precision agriculture
where large areas are monitored and analyses are carried out
in minimum time. Using of aerial vehicle is possible due to
miniaturization of compact cameras and other sensors like
infrared and sonar.
2.
SYSTEM DESCRIPTION
2.1 Aerial Platform
Quad-rotor helicopter of 700mm diameter (see Fig. 1). This
vehicle is capable of carrying 1kg payload with endurance of
35mins flight at top speed of 25Knots. The major advantage of
this equipment is its vertical takeoff and landing capabilities
and can operate from very confined places. This aircraft is
automated using Arduino open source development board and
an open source software (PIX4D) is used to plan the flight
path (waypoints, altitude, heading direction & speed). User
interface is simplified in such a way that anyone with two
hours of training can operate this instrument and render useful
information.
Fig. 1: Quadrotor helicopter and Gyro-stabilized High
resolution camera.
2.2 Image Sensor
Commercially available cameras are small enough to fly on a
drone platform and record in NIR, they are both expensive and
low in resolution. This hurdle can be taken as an affront and
the solution is to modify a canon SX220 camera into NDVI
camera with very minimal modifications the output was a high
resolution camera which could observe NIR at a reasonable
cost.
Drones for Normalized Difference Vegetation Index (NDVI), to Estimate Crop Health for Precision Agriculture: A Cheaper
Alternative for Spatial Satellite Sensors
International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal
Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016)
ISBN-978-93-85822-33-9 39
Fig. 2 (a.) Modified Canon SX260 camera (without the infrared
filter)
Fig. 2(b.) showing difference in wavelength capture between
standard and modified cameras
Fig. 2(c.) Output of the modified camera (NDVI image)
Standard digital cameras capture RGB red, green and blue
light. Modified cameras (see Fig. 2(a.)) captures the
combination of near infrared, red, green and blue light (see
Fig. 2(b.)). Removing infrared filter allows camera to capture
near infrared image NIR (see Fig. 2(c.)). Normalization
difference vegetation index is a simple metric which indicates
the health of green vegetation. The basic theory is chlorophyll
strongly reflects near infrared light (NIR, around 750nm)
while red and blue are absorbed. Chlorophyll reflects strongly
which is why plants appear green to us but reflection in NIR in
even greater, this plays a very important role and helps in
rendering precise data for analysis. The principle of NDVI
relies on the fact that, due the spongy layers found on their
backsides, leaves reflect a lot of light in the near infrared, in
stark contrast with most non-plant object. When the plant
becomes dehydrated or stressed, the spongy layer collapses
and the leaves reflect less NIR light, but the same amount in
the visible range. Thus, mathematically combining these two
signals can help differentiate plant from non-plant and healthy
plant from a sickly one.
3.
HARDWARE AND SOFTWARE TECHNOLOGIES
Fig. 3(a.) Autopilot unit, GPS, Radio Link
Fig. 3(b.) GCS software for flight planning
Technology used in Drone is very sophisticated and advanced
as it has to compensate the absence of the pilot and thus
enable the flight of unmanned aerial vehicle and its
autonomous behavior. They are mainly based on sensors and
microcontrollers, communication system, artificial
intelligence. Drone is an automated system and is separated
into two parts. Hardware control unit of the machine is called
Autopilot System, which is used to control the flight and
various characteristics. Autopilot unit consist of waypoint
navigation with altitude and airspeed, fully integrated IMU
(gyro, acc.), GPS system, Barometer pressure sensor. All these
MEMS (micro-electro mechanical systems) are integrated on
the board (see Fig. 3(a.)). All the sensors have independent
fail-safe program, in case of failure such as altitude, position,
communication modem, aircraft will start heading towards the
actual takeoff point, flight path and recorded data will be
saved in the autopilot chipset and can be easily downloaded on
the ground station computer for post flight analysis. Other
Modem (downlink) is connected to the Ground Control
Station Software updating the real time data of the aircraft.
(see Fig. 3(b.)) GCS provides an interface between aircraft and
computer. It enables flight programming, pre-flight
Ujjwal Mahajan and Bharat Raj Bundel
International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal
Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016)
ISBN-978-93-85822-33-9 40
simulations, tracking the flight path and monitoring its
heading and position on the map. It also generates a log file
for post flight analysis.
4.
PRECISION AGRICULTURE USING UAS
Fig. 4(a.) NIR analysis of Leaf Area Index
Fig. 4(b.) NDVI image of a playground
Vegetation indices in remote sensing is very common, some
indices use the RGB spectral bands. Indices used are :
Green Red Ratio Vegetation Index (GRRVI)
Or
Normalized Green Red Difference Index (NGRDI)
reflectance in green and red part of the spectrum.
Leaf Area Index (LAI) it characterizes plant canopies. One
sided green leaf area per unit ground surface area. (LAI = leaf
area/ground area).
NDVI ratio of reflectance in near infrared and red portions of
the electromagnetic spectrum (Fig. 2(b.)).
5.
PHOTOGRAMMETRIC WORKFLOW
Fig. 5(a.) NDVI image of Sugarcane Field
Fig. 5(b.) Input NIR image, Output PIX4D image.
Fig. 5 (c.) NDVI image showing stressed and well watered crop.
Drones for Normalized Difference Vegetation Index (NDVI), to Estimate Crop Health for Precision Agriculture: A Cheaper
Alternative for Spatial Satellite Sensors
International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal
Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016) ISBN-978-93-85822-33-9 41
6. CONCLUSION
Use of Drone technology is beneficial in agriculture. The
output is encouraging the development and use of drones in
agriculture as a tool for site specific precision farming in small
field area. These can be used by farmers for data acquisition
and analysis, continuously monitoring fields for learning and
developing modern farm management skills. NDVI is a
method to absorb healthy vegetation into a picture the stressed
vegetation due to water stress, nutrition deficiency, other than
the diseased plants. This information is kind of actionable
intelligence that should be delivered to the farming
community. Currently drone analysis shows the variation in
agriculture production, new generation systems will not only
show the variations but also what is causing these variations in
the agricultural production. Drone will produce high precision
data to become the key components of the agriculture
industry. The future of agriculture industry is bright with
drones as a valuable tool that will increase profitability and
healthy crop production.
REFERENCES
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