Precision Farming

1. Precision Farming
Dr. Pooja Goswami
Assistant Professor
Department Of Agronomy
College of Agriculture,
Balaghat, JNKVV, Jabalpur
(M.P.)

2. Definition of Precision Farming-
• “It is defined as the application of technologies and principles to
manage spatial and temporal variability associated with all aspects
of agricultural production” (Pierce and Nowak, 1999)
• “Precision agriculture is broadly defined as monitoring and
control applied to agriculture, including site specific application on
inputs, timing of operations and monitoring of crops and
employees” (Lowenberg-DeBoer & Boehlje,1996)
• It basically means adding the right amount of treatment
at the right time and the right location within a field.

3. Objectives of Precision Farming
• Aims
• To Replace
• - Big machinery
• - High energy consumption
• - Chemicals / at least over application
• With
• - Intelligent machines
• - Intelligent processes

4. Basic concept of Precision Farming
•Existing variabilityAssessing variability
Managing variability •Land leveling
•VRT
•Site specific planting
•Site Specific Nutrient Management
•Precision water management
•Site specific weed management
•Variations occur in crop or soil properties within a field.
•These variations are noted, and often mapped.
•Management actions are taken as a consequence of the spatial variability within
the field.

5. Assessing variability:-
• In precision farming, inputs are to be applied precisely in
accordance with the existing variability
• Spatial variability of all the determinants of crop yield should
be well recognized, adequately quantified and properly located
• Construction of condition maps on the basis of the variability
is a critical component of precision farming
• Condition maps can be generated through
(i) Surveys, (ii) Point sampling & interpolation, (iii) Remote
sensing (high resolution) and (iv) Modeling

6. Managing Variability
• Variations occur in crop or soil properties within
a field.
• These variations are noted, and often mapped.
• Management actions are taken as a consequence
of the spatial variability within the field.
• Land leveling
• VRT
• Site Specific Nutrient Management
• Precision water management
• Site specific weed management

7. Component of precision farming
1. Global Position System
2. Geographical information System
3. Remote sensing
4. Farmers

8. 1-Global Positioning System (GPS)
 All phases of precision agriculture
require positioning information and it can
be provided by the GPS.
 GPS provides the accurate positional
information, which is useful in locating
the spatial variability with accuracy.
 This is the satellite-based information
received by a mobile field instrument
sensitive to the transmitting frequency.
 GPS help in identifying any location in
the field to assess the spatial variability
and site specific application of inputs.

9. Geographical Information
System (GIS)
• A geographic information
system (GIS) is a computer
system capable of capturing,
storing, manipulating, and
displaying spatially referenced
information. Intermediate step
because it combines the data
collected based on sampling
regimes, to develop the process
models, expert systems, etc.

10. Geographical Information System
(GIS)
• GIS is the key to extracting
value from information on
variability.
• It is the brain of precision
farming system and it is the
• spatial analysis capabilities of
GIS that enable precision
farming.
• The GIS is tool used to
capture, store and update,
• manipulate, analyze and
display in map like form,
spatially referenced
geographical information.

11. DATA LAYERS
Geo-image
Base map
Yield map
Fertility map
Crop map
Plant indices
Water bodies
Irrigation channels
Field bunds
Soil map
GIS – layers of related information
i) Bare soil imagery
ii) Topography
iii) Farmer’s experiences

12. Sensing and control technology on precision
farming

13. Topologies of spatial data in GIS
The spatial data in GIS is generally described by X,Y co-ordinates and
descriptive data are best organized in alphanumeric fields.
GIS features can be classified in to three categories :
Points
Lines
polygons
Refer to single place and usually considered as no dimension.
Represents the linear feature and consists of series X, Y co-
ordinate pairs with discrete beginning and ending points.
Polygons are characterized by area and perimeter and closed
features defined by set of linked lines enclosing an area.

14. What are Common Data Formats?

15. GIS represents these features in different types of structure.
1. Raster Model
2. Vector Model
3. Quadree model
Fist two are most popular in GIS packages available in the market.
Data structure
Raster Model Represents the image with help of square lattice grids.
Represents the geographical features by a set of co-
ordinates vectors as X Y co-ordinates define points,
lines and polygons.
Vector Model

16. Data in GIS
Spatial data
Non-spatial data
Maps prepared either with the help of field surveys or with
the help of interpreted remote sensed data.
Attribute as complementary to the spatial data and
discrete what is at a point, along a line or in a polygon and
as a socio-economics characteristics from census or other
sources.

17. Data obtaining
ApplicationMapping

18. Developments which prompted PF
Many technological developments, which occurred in 20th century
contributed to the development of the concept of precision
farming. These technological developments are as follows.
1. Global Navigation Satellite System
2. GPS-Guided agricultural machinery
3. Geographical Information Systems (GIS)
4. Remote Sensing

19. Technology for precision farming
The new tools applicable to this PF are such as RS, GPS and GIS. Three
aspects such as
Mapping The generation of maps for crop and soil properties is the most
important and first step in PF. Data collection occurs both before
and during crop production and is enhanced by collecting
precise location coordinates using the GPS. Grid soil sampling,
yield monitoring, RS and crop.
Scouting Mapping can be done by RS, GIS and manually during field
operations.
Technologies required are as follows:
•Data collection, Analysis or processing Recommendations

20. A geographic information system (GIS) is a computer system capable of
capturing, storing, manipulating, and displaying spatially referenced
information. Intermediate step because it combines the data collected based
on sampling regimes, to develop the process models, expert systems, etc.
The manipulation of spatial information had begun in the 1960s,
•Weed control,
•Pest control and
•Site-specific Fertilizer application
•Drought monitoring,
•Yield estimation,
•Pest infestation monitoring and forecasting
GIS coupled with GPS, microcomputers, RS and sensors
GISspatial data computer

21. GIS Data base design
The GIS has two distinct utilization capabilities
1. First pertaining to querying and obtaining information.
2. Second pertaining to integrated analytical modeling.

22. Important GIS packages
•ARC/INFO
•PAMAP
•MAPINFO
•GRASS
•ISROGIS
•IDRISI
•GRAM

23. Geographic Position System
Historically, GPS has been embraced as a
GIS data collection tool.
Today, GPS is being bound directly to GIS
applications for a variety of applications,
but principally real-time GIS data use in the
field and for database update.
•Positioning
•Navigation

24. Other applications of the GPS-generated grid method
The grid generated by GPS is stored in the computer and used for site-
specific evaluation and monitoring of numerous functions involved in
crop production to achieve peak efficiency in farm management.
• Planting variable rates of seeding
• The GPS-guided grid system helps to apply variable rates of chemicals
• This enables the farmer to side dress application of fertilizers
• Irrigation rates are tailored to the requirement of each grid
• Scouting for pest information and pest control
• At harvest, crop yield information is recorded on a grid section basis.
• It involves the ability of the farmer to achieve greater efficiency
Some of these areas are listed below:

25. Some of the areas in agriculture where precision farming is taking
hold with implications for the economics of farming are listed below.
1. Soil Fertility - Management
a) “Grid Sampling.”
b) Samples are tested
c) Composite colour–grams through computer simulation (17 parameters)
d) The colour-grams are stored as stencils in the computer
Derived Soil fertility

26. Control strategies
Field operations in a spatially variable manner will need the following
equipments:
The instruments SOILECTION10 and FALCON18, developed by AgChem,
Control computer
Locator
Actuator
to co-ordinate field operations
to determine the current location of the equipment
to receive the command from the control computer.

27. Variable Rate Technology?
VRT, is a technology that allows variable rates of fertiliser
application, seeding, chemical application and tillage
throughout a single paddock. The rate is changed according to a
preset map or through information gathered "on the go" by
sensors.
Two approaches to VRT
Map based
Sensor based

28. Map based VRT allows farmers to make decisions based on the detailed maps
and knowledge of the paddocks before they are in the field.
A map-based system

29. It utilises sensors to collect data, such as soil properties or crop characteristics,
"on the go".
A sensor based VRT

30. A controller is a computer that uses application maps to vary the rate of input.
Dickey-John Land Manager SE
MidTech 6100 controller
Raven 660 controller

31. selective N spreader from N-Tech.
GreenSeeker WeedSeeker

32. 3. Remote Sensing
• “The science and art of
obtaining information about
an object, area, or
phenomenon through the
analysis of data acquired by
a device that is not
incontact with the object,
area, or phenomenon u nder
investigation“.(L&K,1994)

33. Our Focus
Properties of EMR
Interaction of
radiation with target
Interaction of
radiation with
atmosphere

34. Elements of Remote Sensing
1. Energy Source or Illumination
(A)
2. Radiation and the Atmosphere
(B)
3. Interaction with the Object (C)
4. Recording of Energy by the
Sensor (D)
5.Transmission, Reception and
Processing (E)
6. Interpretation and Analysis (F)
7. Application (G)

35. Remote sensing
•Sensing are two types
1. Passive remote sensing
2. Active remote sensing
• Electromagnetic radiation
• The electromagnetic spectrum
• Reflection from vegetation
• Data resolutions
1. Spectral
2. Spatial
3. Radiometric
4. Temporal
• Digital image processing
1. Prepocessing
2. Resampling
3. False colour composite
4. True colour view
5. Contrast streching and density slicing
Passive Sensor
Active Sensor

37. •Requirements for instrument calibration,
•Atmospheric correction,
•Normalization of off-nadir effects on optical data,
•Cloud screening for data especially during monsoon
period,
•Processing images from airborne video and digital
cameras(Moran et al, 1997).
Use of RS data for mapping has many inherent
limitations, which includes,

38. i) Detection
ii) Identification
iii) Measurement
iv) Monitoring of agricultural phenomena.
The specific application of remote sensing
techniques can be used for
1. Crop identification
2. Crop acreage
3. Crop vigor
4. Crop density
5. Crop maturity
6. Growth rates
7. Yield forecasting
8. Actual yield
9. Soil fertility
Applicable to crop survey
10. Effects of fertilizes
11. Soil toxicity
12. Soil moisture
13. Water quality
14. Irrigation requirement
15. Insect infestations
16. Disease infestations
17. Water availability
18. Location of canals

39. Major Indian remote sensing missions for agriculture
Mission Year of launch Sensors
IRS-IA 1998
1991
LISS-I(72.5 m resolution; 148 km swath)
LISS-II (36.25 m resolution; 142 km swath)
IRS-IC 1994 LISS-II (36.25 m resolution; 142 km swath)
IRS-IC,CD 1995 PAN (5.8 m resolution; 70 km swath)
IRS-P3 1996 WiFS (188.3 m resolution; 774 km swath)
TES 2001 PAN (1 m resolution; 14 km swath)
CARTOSAT- 1 2002 PAN Stereo (2.5 m resolution; 30 km swath)
CARTOSAT- 2 2003 PAN Stereo (1 m resolution; 12 km swath)
(Gowrisankar and Adiga, 2001)

40. Important remote sensing application in agriculture
Project User organization
•Crop Acreage and Production Estimation
(CAPE)
Ministry of Agriculture
•Cotton Access and Condition Assessment
(CACA)
Ministry of textiles
•Forecasting of Agriculture output based on
satellite, Agrometerology and Land based
obsrevation (FASAL)
State Department of Agriculture
•National wide mapping of soil resources on
1:250000 scale
Ministry of Agriculture
•Mapping of saline/alkanine soils of India Central and State Agricultural
Department
•Integrated Mission for Sustainable Development
(IMSD)
Planning Commission, Ministry
of Rural Development,
Agriculture, Environment and
Forests, State Govt.
(Venkantaram, 2001)

41. Integration of GIS,GPS and RS
Longitude
L
A
L
T
I
T
U
D
E
Remote sensing
Geographical Information
System
Global Positioning
System
Farmers
Computer

43. Contour Mapping
Photogrammetry
Remote Sensing
Cartography

44. STATSGO Derived Soil Order

45. Derived Soil Temperature

46. Derived Water Bodies

47. Role of Remote sensing and GIS in Agronomy
Remote sensing technology is used in getting near real time information on
various aspects of agriculture. Variety of satellites in orbit providing a routine
and continuous coverage of the globe.
•Crop type
•State of maturity
•Crop density
•Crop vigor
•crop geometry
•Crop moisture
•Crop temperature
•Crop health etc.
Increase production, reduce input costs, and manage the land more effectively
in combination with new technology and farming practices.

48. Birla Technical Services (BTS)
BTS in association with RMSI Pvt. Ltd. has been undertaking an innovative study
since the last 4 years for Basmati Export Development Foundation (BEDF),
APEDA under the Ministry of Commerce, Govt. of India.
This project includes
Crop classification
Acreage estimation
Yield forecasting
Health monitoring
Using Satellite Remote Sensing/ GIS based tools for basmati in Five states i.e.
Punjab, Haryana, Uttranchal, U.P. and J& K (covering an area of over 1,80,000 sq
km)

49. Utility of Basmati Agricultural Intelligence (AI) data
RMSI understands that AI data generated is used in different
ways:
•Industries - Use this data mainly for procurement and supply chain
•Management Boards - Use this data to streamline supply chain as well as to fix
the price in the market
•Insurance companies – Create an insurance product out of the agricultural
yield data
To create such intelligent data, RMSI followed two different aspects,
namely,
•Geospatial data validity of comparing Mandi data with remote sensing based
outputs in rice production
•Supply chain management methodology evolved from the above survey

50. Study area
Major rice growing districts from the Indian Ganges fl ood plains. This includes 13
districts each in Punjab and Haryana, 29 districts in Uttar Pradesh, 4 districts in
Uttarakhand and 2 districts of Jammu & Kashmir. Geographically, this spreads
across 25° 83’ North to 33° 07’ North latitude and 73° 87’East to 81° 86’ East
longitude covering an area of about 189,000 sq km.
Input data
The entire region was carried out using IRS P6 AWiFS satellite images with
spatial resolution of 56m. Information, from regional to local, were extracted
using medium and high resolution satellite images of IRS series 1C, 1D, P6
LISS III and LISS IV with spatial resolution of 23.5m and 5.6m, respectively.
Secondary sources of information like Survey of India Toposheet on 1:50,000 scale,
district maps

51. Methodology
SCM included Market survey, assessment, Agriculture and Land Resource
Mapping.
RMSI undertook a hybrid approach to compare and assess the accuracy of
remote sensing-based production estimation of Basmati rice for kharif 2005
against the mandis arrivals and other related sources at the end of the Kharif
season-2005.
Geo-spatial data validity

52. RS data being always higher than total supply chain sources confirms the reliability
of this data. Table 2 gives the results so produced and the comparison analysis.

53. 31st March 2006 and considering certain calculated and
logical assumptions and limitations, it is concluded that
the remote sensing based estimated results for Kharif
2005 is matching up to an accuracy of 90% to 94% in the
states of the study area.

54. Space-borne of remote sensing data for crop
acreage estimation and production forecasting was
experimented in early 1980s in selected districts for wheat,
rice and groundnut to estimate state-levels wheat acreage
using Landsat MSS data for Haryana and Punjab in 1985-
86. The results were encouraging and the project namely
“Crop Acreage and Production Estimation”
Crop Acreage and Production Estimation

55. Crop Acreage and Production Estimation (CAPE)
CAPE was launched covering wheat, rice, groundnut and Rabi sorghum in
selected major growing states/districts.
SAR of RADARSAT was operationally used for kharif rice in 12 districts of
Karnataka.
The multidate WiFS data (Coarse resolution and high receptively) is used to
explore the possibility of making national level forecasts.
Software package - CAPEWORKS/CAPEMAN for acreage estimation of
mango and banana plantations in Krishna district of AP and Thiruchirapalli
district in Tamilnadu with accuracy of 94% injoint venture of IIHR

56. Case IH
Advanced Farming Systems (AFS) have been at the forefront of
precision farming for more than a decade, giving farmers the ability to control
the entire crop production cycle with repeatable accuracy from sub-inch
levels and beyond, reduce overlaps and cut input costs – and maximize
your yield potential.
PRECISON SOLUTIONS FOR ALL SEASONS

57. AFS Precision
Farming Systems
Compatibility
Guidance &
Steering
Combines, sprayers, tractors
Application
Control
Planters, seeders, sprayers,
spreaders
Machine &
Implement Control
Anhydrous applicators, balers,
combines, drills, planters, seeders,
sprayers, spreaders, tractors
Yield, Moisture &
Monitoring
Combines, cotton pickers
Mapping Analysis
Combines, planters, seeders,
sprayers, tractors

58. Commercialization of PF
The interest in PF and its introduction has resulted in a gap between the
technological capabilities and scientific understanding of the relationship
between the input supplies and output products.
Issues
There are certain issues which need further attention, research and
development to deliver the best in field. The issues are as follows:
•Area coverage and data management
•Scale bias
•Infrastructure
•Ownership and privacy

59. 1.Small land holdings
2.Cost/benefit aspect of PF system
3.Heterogeneity of cropping systems
4.Lack of local technical expertise
5.Knowledge and technological gaps
In India more than 57.8 per cent of operational holdings has size less
than 1 ha.
The reasons for limited implementation of PF in Asian
countries are following:

60. Indian perspective
Several negative ecological consequences such as
•Depletion of lands,
•Decline in soil fertility,
•Soil salinization,
•Soil erosion,
•Deterioration of environment,
•Health hazards,
•Poor sustainability of agricultural lands and
•Degradation of biodiversity.
Declining use efficiency of inputs and dwindling output–input ratio have
rendered crop production less remunerative.
The M. S. Swaminathan Research Foundation, Chennai, India has joined
hands with Israel to initiate PF on an experimental basis, including
conducting training programmes

61. Challenges for precision farming
•Identification of crops and estimation of area and
production of short duration crops grown in fragmented
land holding
•Forecasting of drought and/ floods.
•Detection of crop stress due to nutrients, pests and diseases
and quantification of their effects on crop yield.
•Automation of land evaluation procedures for a variety of
applications using GIS techniques.
•Information on sub surface horizons.
•Extending precision farming database to smaller farm size
•Estimation of depth of water in resevoirs and quality
assessment of ground water.

62. THANKING YOU!