drone

1. TECHNICAL SEMINAR
ON
UTILIZATION OF DRONE FOR
AGRICULTURE
GUIDE: Sunil Kumar P
Ass.Prof
Dept of EEE

2.  ABSTRACT
 INTRODUCTION
 DRONES IN AGRICULTURE
 UAS FOR SMART FARMING
 REMOTE SENSING FOR SMART
FARMIG
 SOFTWARE
 CONCLUSION
 TABLE OF MAJOR ABBREVIATIONS
 REFFERENCES

3. Farmers explore the capabilities for applications of Robotic Process Automation (RPA) with image processing, pattern recognition
and machine learning, so its logical to ask where best to apply this technology for maximum effect. Machines can do automated
tasks better, cheaper and faster. One can use the camera from the sky and measure things on the earth especially on cropland
from drones and satellites. Over time, drones have increased in capabilities and fallen in cost, and their use has greatly expanded
especially in complex terrain. High quality remote sensing with spectral imaging using drones makes them interesting for regular
use in Precision Agriculture (PA). Drones are often used in agriculture in ways that were highly controversial only a short time ago
even there are no unified legislation on drones usage in agriculture. In this paper we address problem of Remote Sensing (RS)
technologies combined with Unmanned Aerial System (UAS) platforms to support and develop selected agriculture operations like
map or sensor-based Variable Rate Application (VRA).

4.  First RS usage for precise measurements of cropland have been documented in 1930s and practically applied since 1950s.
 The temperature indicates whether the crop is healthy. Currently by processing satellites data into evapotranspiration
images one can gather information about, how much carbon is taken, water is used on individual fields and even predict
how much water is being used by a certain field or a certain product.
 Most of farmers in the world cant effectively use that information. Access to satellite data is not common and cheap, and
such data hasn't a form of easy to use smart measurements combined with local farmers knowledge and isn't usable for
direct use in the field e.g. for accurate irrigation.
 UAS collected images have higher resolutions and can be acquired more often in comparison to images from satellite

5.  Drones are used not only in the fields of early soil analysis useful in
planting on the base of the precise 3D maps. They also acquire image-
based data helpful after processing in irrigation and nitrogen,
phosphorus or potassium (N/P/K) level management by determination
of the strength of nutrient uptake in different type of fields.
 Using hyperspectral, multispectral, thermal or LiDAR sensors mounted to
UASs, farmers and growers can identify which parts of crops need
improvements and react properly on time.
 Apart from crops monitoring drones also have been used for seed
planting by shooting seeds and plant nutrients into the field what can
decrease operating costs up to 70 percent.

6.  Other agricultural applications cover cattle herd counts or rustler monitoring using near-infrared (NIR) sensors or even
mapping agricultural damage by wild boars or other pests
 It would help farmers to monitor what is happening in the field, acquire, gather and process intelligent pixels and signals
from the ground into multimodal knowledge using machine learning and predictive modelling. It would eventually help to
operate effectively and produce more quality food with less water.
 Some of papers mention possible future applications , others also indicate potential difficulties and limitations, in
particular the limited load capacity of drones.
 There are hopes for the use of drones in harvesting fruits from trees, there are even concepts of experimental drones,
which are collecting coconuts.

7.  UAS used in precision farming operate on different elevation. Drones operators can
collect high quality photos from hundred meters height for automatic analysis of individual
leaves on a corn plant or operate like a sprayer at a very small elevation over a field.
 Most of flight platforms used by farmers can be classified as a rotary-wing, flapping-
wing or fixed-wing UAVs.
 To cover a lot of ground very fast for remote sensing farmers prefer fixed-wing UAVs like
Ag Drones or senseFly over multi-rotor drones. Equipped with high-quality camera they
can capture high-resolution, georeferenced RGB images for thousands of acres per day
at 100 meters above ground level.
n

8.  According to the division of civilian drones presented in, seven UAV classes are
based upon size, flight endurance and capabilities. The biggest platforms -
HALE UAS in comparison to satellites provide a significant cost savings
allowing for multiple returns to the Earth for upgrades.
 Ground Control Station is used to remote control of UAV, display information
and data in a real-time. It also can work as an interface for exchange of data
relay and transmission.
 Other methods used for measurements accuracy contain Differential GPS
approach (DGNSS), Real Time Kinematics (RTK), Satellite Based Augmentation
systems (SBAS) or Precise Point Positioning.

9. 

 Plenty of vendors provide spectrum of UAS sensors with additional equipment like
stabilizing gimbals. For example, Sentera produces lightweight (80 grams) dual 4K
sensor able to capture from drone TrueNDVI 30fps video or NDRE images and
broadcast in real-time to operator.
Miniature 31 grams xiSpec camera from Ximea is the worlds smallest hyperspectral
sensor.
This kind of sensor has been used for protecting against yield loss by early detection
and identification of diseases.
 Thermal imagery require optimal weather conditions and no external light, thus
thermal inspections can be equally done at night. By measuring canopy temperature
operator can gain insight into water use or plant metabolism.

10.  LiDAR is used for generation elevation maps to identify terrains requiring
drainage or for creating of 3D crops models to monitor drought stress at
different growth stages or plant height. Popular LiDAR data analysis software
contains ArcGIS, AutoCAD or ENVI.
 Results have a form of JPEG or TIFF files.
 An average volume of thermal, multispectral and LiDAR measurement reaches
gigabytes and of hyperspectral measurement exceeds terabytes.
 Multimodal information like soil, fertilizer, chemical, seeding and scouting
application maps are independent parts of complex data which is hard to read.

11.  For more detailed information not only constrained to imagery-based,
farmers can implement the Internet of Things (IoT) idea combining data
from devices connected to sensor network gathering different types of
information, covering fields, machinery and drones for better control, data
collection and operations.
 Fusion of IoT systems with image-based devices allow analysing
biomass quality as well as monitoring diseases and destructive insects
or other animals that attack crops.

12.  In RS a greenness indicator described by spectral transformation of visible to NIR reflectance bands is named a
vegetation index.
 Even the newest satellites like WorldView-2 have limitations to two day revisit period what makes them useless sensing
system in emergent situations. UAS based NDVI imagery gives high (sub-cm level) resolution at hundred meters height
with daily data imagery or acquisition on demand, providing faster and more accurate data, enabling 24/7 monitoring
and diagnoses.
 The application of NIR, enables to identify stress in a plant several days before it becomes discernible.
 UAS based imagery due to the high resolution is appropriate for crop conditions monitoring. Estimation of Leaf Area
Index (LAI) is required by models of growth and yield of crop canopy. Information computed from imagery data can be
used as a reference for farm management. Possible applications in precision agriculture includes classification of plant
species, detailed vegetation, crops, by vegetation index or per-field irrigated.

13.  Farmers also apply RS information to monitor and map soil properties,
crop pest management, nitrogen content estimation, detect stress in
plant water, to analyse chemical content in leafs (LAI), to monitor and
control of weed or just to control the growth of planted cereals.
 Other problems concerning UAS imagery are equal to classic satellite
imagery problems and were detailed in. Applications of satellite and
drones imagery in precision farming increased from 16% in 2004 to
30% in 2009 and to 55% in 2017.
Fig: Selected applications of image analysis & processing in agriculture: a) Plant counting and yield prediction, b) Individual tree crowns identification, c) Plant health indices, d)
Advanced plant health indices, e) Plant height measurement, f) Canopy cover mapping, g) Field performance assessment, h) Field water ponding mapping, i) Scouting reports, j)
Stockpile measuring, k) Chlorophyll to assess plants measurement, l) Nitrogen content in wheat measurement, m) Drought stress identification, n) Drainage mapping, o) Weed
pressure mapping, p) Canopy temperature measurement, q) Phenotyping and genotyping, r) Disease pressure map.

14. Software for image analysis & data processing from agriculture drones support farmers to make decisions based on data. Farmers
need farm data in useful form for planning and management decision making. This field is overwhelmed by commercial
applications but one can find also free, open-source solutions. Each vendor creates software using own polices, but it is possible to
indicate some key features that many solutions implement.
Data collection -collecting images and videos directly from drones and storing them in database.
Analysis and reports -applications analyse data and produce useful information's like crop & soil assessment. Collected data sets
help to assess characteristics of a soil: moisture, temperature, predicting yield by computer vision algorithms and techniques.
Maps generation -applications generate maps in high resolution or even detailed 3D models of a field.
Flight planning and automation -drones can fly over optimized route, farmer can plan, schedule and monitor flights directly in
application in the real-time.

15. RPA ROBOTIC PROCESS AUTOMATION
PA PRECISION AGRICULTURE
RS REMOTE SENSING
UAS/UAV UNMANNERD AERIAL SYSTEM
VRA VARIABLE RATE APPLICATION
LIDAR LIGHT DETECTION AND RANGING
NIR NEAR INFRARED REFLECTANCE
DLS DATA LINK SYSTEM
N/P/K NITROGEN,
PHOSPHOROUS,
POTASSIUM
GCS GROUND CONTROL STATION
GCP GROUND CONTROL POINTS
DGNSS DIFFERENTIAL GPS APPROACH
RTK REA TIME KINEMATICS

16. SBAS SATELLITE BASED AUGMENTATION
SYSTEMS
PPP PRECISE POINT POSITIONIG
DLS DATA LINK SYSTEM
IoT INTERNET OF THINGS
NDVI NORMALIZED DIFFERENCE
VEGETATION INDEX
SAVI SOIL ADJUSTED VEGETATION INDEX
GNDVI GEEN NORMALIZED DIFFERENCE
VEGETATION INDEX
MCARI MODIFIED CHLOROPHYLL
ABSORPTION RATIO INDEX
CCCI CANOPY CHLOROPHYLL CINTENT
INDEX
CWSI CROP WATER STERSS INDEX
NDRE NORMALIZED DIFFERENCE RED
EDGE INDEX
LAI LEAF AREA INDEX

17.  In this paper a literature review on Drones with emphasis on remote sensing applications in Smart Farming is conducted.
Global reports indicate interest and need of usage remote sensing technology with drones for an increase in farms
productivity and yields.
 Technologies, Markets, Players R&M 2018 report, an agriculture will be a major market for UAVs, and reach over $480M in
2027.
 Nevertheless drones and data processing in agriculture are still in very early stage of development and standardization.
 Currently available services for farmers are constrained to related with benchmarking, predictive modelling, risk
management or sensor deployment.

18.  The first and one of the most inhibiting factors is unification of the government directives, recommendations and policies in the
area of the modern farm models and simplification of law connected with drone usage
 The second and the more important are highly autonomous and accurate drones registering high quality data what implies
more sophisticated sensors, cameras and light robotic modules.

19. 
D. Pivoto, P
. D. Waquil, E. Talamini, C. P
. S. Finocchio, V. F. D. Corte, and G. de Vargas Mores, “Scientific development of
smart farming technologies and their application in brazil,” Information Processing in Agriculture, vol. 5, no. 1, pp. 21–32,
mar 2018.
 J. Awange, GNSS Environmental Sensing. Springer International Publishing, 2018.
 M. Hassanalian and A. Abdelkefi, “Classifications, applications, and design challenges of drones: A review,” Progress in
Aerospace Sciences, vol. 91, pp. 99–131, may 2017.
 P. Lottes, R. Khanna, J. Pfeifer, R. Siegwart, and C. Stachniss, “UAVbased crop and weed classification for smart farming,”
in 2017 IEEE International Conference on Robotics and Automation (ICRA).
 P. Reidsma, S. Janssen, J. Jansen, and M. K. van Ittersum, “On the development and use of farm models for policy impact
assessment in the european union – a review,” Agricultural Systems, vol. 159, pp. 111– 125, jan 2018.