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1. WELCOME

2. AFTAB K
MIRJANAYAK
PG22AGR14023
Sr. M.Sc. (Agri.)
Department of Agronomy
UNIVERSITY OF AGRICULTURAL SCIENCES, RAICHUR
COLLEGE OF AGRICULTURE, RAICHUR
DEPARTMENT OF AGRONOMY
Digital technology: Boon or bane in agriculture
Master’s Seminar - I

3. Sequence of seminar
 Introduction
 What is digital farming?
 Components of digital farming
 Applications of digital farming
 Conclusion

4. Introduction
 World population - 7.8 billion and predicted - 9.9 billion in 2050.
 India population- 1.4 billion (2023).
 Total world population - 16% live in developed world (Northern America,
Europe, and Oceania), while 84% - developing world.
 In 2030, 856 million additional people will be living in developing countries as
compared to 34 million in developed countries.
Fig. 1: Population and food supply

5. Digital farming
“Consistent application of the methods of precision
farming and smart farming, internal and external
networking of the farm and use of web-based data
platforms together with big data analytics”

6. Why digital farming?
 Increased efficiency
 Improved sustainability
 Enhanced crop quality
 Better risk management
 Increased profitability

7. Components of digital farming

8. Remote sensing
Remote sensing is acquisition of
information about an object or a
phenomenon without coming in
contact with it by using EM
radiations.
Applications:
Crop monitoring
Soil management
Precision agriculture
Yield forecasting
Land use planning

9. Global Positioning System (GPS)
It is a space-based satellite navigation
system that provides location and time
information in all weather conditions,
anywhere on or near the Earth where there
is an unobstructed line of sight to four or
more GPS satellites.
Applications:
Tracking livestock
Tractor guidance
Fertilization and crop protection
Yield monitoring
Soil sampling

10. Geographic Information System (GIS)
It is a type of data base containing geographic data combined with
software tools for managing, analyzing and visualizing the data.
Applications:
Agricultural mapping
Soil analysis
Precision farming
Historical data comparisions

11.  IoT is inter-networking of physical devices.
• This system has ability to transfer data over a network without
requiring human to human or human to computer interaction
Applications:
• Crop water management
• Pest management and control works
• Precision agriculture
Internet of Things (IoT)

12. Big data analytics
Big data: Enormous amount of information collected from
different sources and for the longer period like sensor data,
social networking data and business data.
Data collection:
Process oriented
Machine generated
Human source
The process used to combine
the business and traditional
analytics is called as
Big data analytics.

13. Artificial Intelligence (AI) is the simulation of human
intelligence processes by machines, especially computer
systems
AI is when machines
Exhibit intelligence
Perceive their environment
Make decision to maximize chance of success at a goal
Artificial Intelligence (AI)

14. AgroPad 10: AI-powered technology helping farmers
check soil and water health
Any device, tool or application that permits the exchange or
collection of data through interaction or transmission
Information and Communication Technology (ICT)

15. Fig. 2: Information-based management cycle for advanced agriculture
Rubio et al., 2020
VAU, Valencia

16. Research findings

17. Weather forecast

18. Dhulipala et al., 2021
ICRISAT, Hyderabad
 It is a joint initiative of IMD,
IITM, ICAR and ICRISAT
 24×7 access
 12 Indian languages and English
 Location specific weather based
agro-advisories
 More than 2,11,000 users, highest
in Karnataka (27,031)
 IMD- Temperature, RH, Rainfall,
wind direction and wind speed
 Notification in users mobile
regarding warnings about
cyclones, thunderstorms and
cloudbursts etc
Fig. 3: Meghdoot - a mobile app to access location specific weather based
agro-advisories

19. Fig. 4: Smart TV application that shows the weather forecast
University of Salamanca, Salamanca Villarrubia, 2017

20. Rebecca et al., 2020
University for Development Studies, Ghana
Fig. 5: Weather app used by the farmers

21. University for Development Studies, Ghana Rebecca et al., 2020
Fig. 6: Whats app group used by the farmers

22. Fig. 7: Farmer’s perceptions of the relevance or usefulness of the digital tools
and weather forecast information and data shared compared to
channels formerly used for dissemination of forecast data
University for Development Studies, Ghana Rebecca et al., 2020

23. Selection of suitable variety

24. Fig. 8: Identification of tobacco varieties suitable for Orissa through online
expert system
ICAR-CTRI, Rajahmundry Ravisankar et al., 2018

25. IASRI, New Delhi Singh et al., 2013
Fig. 9: Selection of barley variety through expert system

26. Land leveling

27. Fig. 10: Laser land leveling working mechanism
Tomar et al., 2020
RVSKVV, Gwalior

28. Table 1 : Time required and suitability of different land leveling
techniques
Land leveling
techniques
Capacity
(ha day-1
)
Leveling
accuracy
(cm)
Animal 0.08 +/- 4-5 cm
Hand tractor 0.12 +/- 4-5 cm
Blade 0.5-1.0 +/- 4-5 cm
Bucket 0.5-1.0 +/- 4-5 cm
Laser up to 2 ha +/- 1 cm
Anil, 2017
PJTSAU, Hyderabad
Uneven distribution of
irrigation water under
traditional land leveling
Laser-levelled field
prepared for rice
transplanting

29. Table 2: Comparison of conventional and laser guided land leveling
Parameters
Conventional
leveling
Laser land
leveling
Paddy Wheat Paddy Wheat
Irrigation depth (cm)
110-
115
30-35 90-95 20-25
Water productivity (kg m-3
) 0.37 1.5 0.47 2.44
Profit over convention(Rs. ha-1
)
1st
year 1000-1200
2nd
year 4000-5000
Saxena et al., 2021
CIAE, Bhopal

30. Sowing

31. Project: Andhra Pradesh (AP) Primary Sector Mission (Rythu Kosam Project)
Partners: Government of AP, Microsoft India (R&D) Pvt. Ltd. and ICRISAT
Anon., 2017
ICRISAT, Hyderabad
SMS received through
sowing advisory app
showing boron and zinc
deficiency in the field
Fig. 11: Sowing advisory app
Messages contained essential information such as:
Land preparation
Seed treatment
Sowing time
Soil test based fertilizer application
FYM application
Plant density
Preventive weed management
Observing boron and zinc deficiency in field and
applying nutrients if needed
Harvesting
Shade drying of harvested pods
Correct storage practices

32. Soil type
Composition (%)
(clay: silt: sand: gravel)
Seeding
accuracy (%)
Speed
(rpm)
Dry clay soil (fine) 40: 30: 20: 10 94.83 55
Sandy soil (medium
coarse)
20: 25: 25: 30 82.75 48
Very coarse soil 10: 15: 30: 45 72.41 42
Divya et al., 2013
DSCE, Bengaluru
Table 3: Automated robot seeding accuracy and machine speed
tested in different soil

33. Water management

34. Subeesh and Mehta, 2021
ICAR-CIAE, Bhopal
Fig. 12: Automated irrigation system using IoT

35. Nano Ganesh
Santosh, 2017
Source: FAO

36. Treatment
Grain
yield
(kg ha-1
)
WUE
(kg ha-
cm-1
)
Water
saved
(%)
COC
(Rs. ha-1
)
Gross
returns
(Rs. ha-1
)
Net
returns
(Rs. ha-1
)
B:C
ratio
T1 : Surface irrigation as per PoP 2580 39.1 - 36185 75593 39408 2.08
T2 : Drip irrigation at weekly
interval
2762 58.9 28.8 45170 80986 35816 1.79
T3 : Drip irrigation at 50% DASM 3560 92.7 41.7 46920 104176 57256 2.22
T4 : Drip irrigation at 75% DASM 2243 61.3 44.5 45670 65842 20172 1.44
T5 : Green SMI based drip
irrigation
3658 88.1 37 46870 106964 60094 2.28
T6 : Red SMI based drip irrigation 2445 65.2 43.1 45790 71684 25894 1.56
T7 : Sensor based automated drip
irrigation at 25% DASM
3984 79.4 23.8 50420 116465 66045 2.31
S. Em.± 157 - - - 1342 890 0.01
CD (p=0.05) 484 - - - 4165 2916 0.03
Nanda, 2021
GKVK, Bengaluru
Table 4: Grain yield, WUE, water saved and economics of sensor based automated
irrigation system in finger millet
SMI-Soil moisture indicator , DASM-Depletion of available soil moisture

37. Table 5: Effect of sensors on plant height, dry matter production, yield and
water use efficiency of maize
Treatment
Plant
height at
harvest
(cm)
Dry matter
production
at 90 DAS (g
plant-1
)
Kernel
yield
(kg ha-1
)
Stover
yield
(kg ha-1
)
Water use
efficiency
(kg ha-cm-1
)
T1 : Surface irrigation 184.6 350.4 6551 8007 91.6
T2 : Drip irrigation at 3 days interval 194.1 416.0 8331 9485 126.2
T3 : Green SMI based drip irrigation 200.0 436.0 10441 11975 148.1
T4 : Yellow SMI based drip irrigation 194.7 365.7 7548 9145 120.8
T5 : Sensor based drip irrigation at 25 %
DASM 203.4 479.5 10676 12273 149.3
T6 : Sensor based drip irrigation at 50 %
DASM 195.6 432.6 8436 9690 128.8
T7 : Sensor based drip irrigation at 75 %
DASM 187.3 354.4 6555 8033 110.2
Chaithra et al., 2020
GKVK, Bengaluru

38. 38
Table 6: Response of tomato yields, WUE for the two irrigation systems (AIS
and CIS) in the two seasons
King Saud University, Riyadh Ghobari et al., 2017
Character
First season (2013–2014)
Second season (2014–
2015)
AIS CIS AIS CIS
Plant height (cm) 44.0 39.0 46.7 38.7
Number of branches 6.0 5.0 6.63 5.10
Fruit length (cm) 6.3 5.7 7.1 6.3
Avg. fruit wt. (g) 95.0 93.0 93 84
Total yield (t ha-1
) 39.0 37.40 40.1 36.70
WUE (kg m-3
) 7.50 5.72 7.15 5.33
Note: AIS (Automated irrigation system): Automatically calculate Crop ET and local microclimate using
electronic sensors and controller, CIS (Conventional irrigation system): normal crop ET based irrigation
Drip: Tomato
WUE= Yiled/ETc (mm), Irrigation WUE= Yield/seasonal irrigation depth (mm)

39. Table 7: Estimated amount of water for different irrigation techniques in paddy
cultivation
Stages of growth
Manual–
flood
irrigation
(mm)
Drip
irrigation
(mm)
Smart drip
irrigation
method (mm)
Field preparation 200–300 200–300 200–300
Planting 400– 450 300–400 300–350
Flowering 400– 450 100–200 100–150
Maturity 100–150 50–100 10–25
Total span (100%) 1100–1350 650–1000 610– 825
Average 1225 825 717.5
Percentage utilization of water
With respect to flood
irrigation
100 67.35 58.57
With respect to drip
irrigation
148.8 100 86.97
With respect to Smart
drip irrigation
170.73 114.98 100
Barkunan et al., 2019
Anna University, Coimbatore

40. Nutrient management

41. Fig. 13: Spatial distribution of Nitrogen (N) soil nutrient for Tumkur district
SIT, Tumkur Leena et al., 2020

42. Character
Treatments
Manual
spray
Drone
spray
Control
Plant height (cm) 45.2 49.1 39.8
Dry matter production (kg ha-1
) 2106 2311 1827
Number of flowers plant-1
50.4 54.6 44.9
Number of pods plant-1
30.2 39.2 21.3
Grain yield (kg ha-1
) 677 747 574
Haulm yield (kg ha-1
) 1543 1684 1398
Grain protein (%) 22.1 23.5 21.2
Grain carbohydrate (%) 55.5 57.9 54.6
Table 8: Effect of foliar application of TNAU pulse wonder using agricultural
drone on greengram
Dayana et al., 2021
ADACRI, Tiruchirapalli
Manual spraying
Drone spraying
Dose: 2% TNAU pulse wonder @ 50 l ha-1

43. Chevinge and Sharma., 2019
IRRI, Phillippines
Fig. 14: Working of Rice crop manager (RCM)

44. Weed management

45. Table 09: Effect of weed management practices on wheat production
Weed management
practices
Weed density
(m-2
) 1000
kernel
weight
(g)
Grain
yield
(kg ha-1
)
Straw
Yield
(kg ha-1
)
BLW Grass
Clodinafop-propargyl
0.105 kg ha-1
14.26 12.89 23.30 1413 3878
Isoproturon 1.50 kg ha-1
15.58 11.68 28.53 1310 3728
Hand weeding at
tillering
12.96 5.26 25.10 1171 3552
Automated weeders 8.29 7.79 31.30 2177 5481
Handheld hyper spectral
radiometer
3.27 1.29 32.33 2207 5687
Sensor-controlled Field
Spraying
2.08 1.28 33.89 2548 5887
LSD (0.05) 4.62 0.42 1.48 51.07 266.4
CV(% ) 5.21 4.21 3.12 1.93 3.62
Kumar et al., 2016
RARI, Durgapura

46. Pest and disease management
46

47. Fig. 15: Electronic solutions against agricultural pests (e-SAP)
47
1. Farmers Registration 2. Pest identification, quantification and Advisory
3. Expert connect for undiagnosed pests
Prabhuraj et al., 2019
KVK- UAS, Raichur
4. Printable advisory

48. Name of district No. of villages covered Total advisories
Koppal 274 1779
Raichur 272 4526
Bellary 107 1367
Yadgir 60 619
Bidar 150 1011
Bijapur 117 468
Gulbarga 02 3
Total 982 9773
Table 10: Conducted district-wise surveys and advisories given in
cotton
Prabhuraj et al., 2019
KVK- UAS, Raichur

49. Category
Low
perception (%)
Medium
perception (%)
High
perception(%)
Raichur 37.11 10.31 52.58
Gulbarga 33.33 33.33 33.33
Yadgir 36.59 15.85 47.56
Bellary 31.65 27.85 40.51
Total 30.47 36.98 32.55
Table 11: Overall perception of farmers towards e-sap
Prabhuraj et al., 2019
KVK- UAS, Raichur

50. Wang et al., 2019
Fig. 16: Comparison of control efficacy on wheat aphids and working
efficiency in the wheat field of the unmanned aerial vehicle, boom
sprayer and two conventional knapsack sprayers
SCAU, Beijing
Note: (A)Unmanned aerial vehicle (UAV) sprayer (B) Self- propelled boom (SPB) sprayer
(C) Knapsack mist-blower (KMB) sprayer (D) Electric air-pressure knapsack (EAP) sprayer

51. 51
Fig. 17: Control efficacy of four sprayers against wheat aphids in the field
tests at one, three and seven days after treatment
Wang et al., 2019
SCAU, Beijing

52. Sprayer
Tank capacity
(L)
Spray area
(ha)
Spray time
(hr)
Working
efficiency
(ha hr-1
)
UAV Sprayer 5 0.39 ± 0.04 0.095 ± 0.01 4.11
SPB Sprayer 280 0.93 ± 0.06 0.39 ± 0.03 2.38
KMB Sprayer 18 0.22 ± 0.02 0.14 ± 0.01 1.57
EAP sprayer 16 0.039 ± 0.004 0.19 ± 0.01 0.21
Table 12: Working efficiency (ha hr-1
) of four sprayers
Wang et al.,
2019
SCAU, Beijing
NOTE: Unmanned aerial vehicle (UAV) sprayer, Self- propelled boom (SPB)
sprayer, Knapsack mist-blower (KMB) sprayer, Electric air-pressure
knapsack (EAP) sprayer

53. Jitendra et al., 2016
IARI, New Delhi
Fig. 18: Hyperspectral reflectance of aphid infested canopy measured
in mustard field

54. Jha et al., 2019
GIT, Ahmedabad
Fig. 19: Grape disease detection system using ML algorithm

55. Harvesting

56. Kadeghe et al., 2020
University of Georgia, Athens
Fig. 20: Automatic cotton harvesting robot

57. Particular
Divisions
Robot Experienced
labour
Average
labour
Beginner
labour
Work hours (h) 137 ± 0.03 170 ± 1.83 244 ± 1.28 336 ± 2.21
Number of days * (day) 5.7 7.1 10.2 14.0
Average production per
hour ** (kg/h)
8.9 42.8 29.9 21.7
Average production per day
*** (kg/day)
213.6 171.2 119.6 86.8
Average weight of each strawberry = 15 g
Average total production = 1215 kg
Based on this, the robot’s work hours and daily average production and the human’s required for daily average
production were calculated using the “Delmia Quest simulation program”.
*Working Time/24
** Robot = Total production/days × 24, Human= Total production/days/24 × 4
*** Robot =Average production per hour × 24, Human = Average production per hour × 4
Woo et al., 2020
Kyungpook National University, South Korea
Table 13: Average daily output of strawberry harvesting robot
and human labour as compared to total production

58. Conclusion
 Automated sensor based irrigation systems have high potential to
increase WUE and grain yield
 Foliar application of nutrients by agricultural drones is more profitable
compared to manual spray in greengram
 Harvesting of greenhouse vegetable/fruit crops by using automated
robots is found to be advantageous over human labour
 There is a need to develop holistic artificial intelligence and bid data
analytic technologies which guide the farmers from sowing to marketing
of the produce
 There is need to develop economically feasible smart farming
technologies for small and marginal farmers 58

59. Thank you