1. The document discusses Internet of Things (IoT) based irrigation practices for efficient water management in rice cultivation. It outlines the flow of a presentation on this topic, including an introduction to IoT in water management and various irrigation systems that use IoT. 2. Research findings show that smart drip irrigation systems can save up to 42.8% of water compared to traditional flood irrigation. Sensor-based irrigation management in rice can improve water use efficiency by up to 27.2% and yield by up to 11%. 3. Automatic IoT-based irrigation systems for rice cultivation have been found to reduce irrigation water use by 13-20% compared to manual systems, while maintaining or increasing yields. This

1. 1
WELCOME

2. 2
Acharya N G Ranga Agricultural University
Agricultural College, Bapatla
Department of Agronomy
AGRON-691
Doctoral Seminar -I
on
Internet of Things (IoT) based irrigation practices for
efficient water management in rice cultivation
Course in-charge
Dr. K. Chandrasekhar
Professor& Head
Department of Agronomy
AgriculturalCollege, Bapatla
Presented by:
D. V. S. Akshay
PhD Scholar,
Department of Agronomy
AgriculturalCollege, Bapatla

3. FLOW OF PRESENTATION
3
1.
• Introduction
2.
• IoT in Water Management
3.
• Various irrigation systems with IoT
4.
• Advantages & Disadvantages
5.
• Research findings
6.
• Conclusion

4. 4
“Water is fundamental for life and health. The human right
to water is indispensable for leading a healthy life in human dignity.
It is a pre-requisite to the realization of all other human rights”
-The United Nations Committee on Economic, Cultural and Social Rights,
Environment News Service, 27 Nov 2005
The period 2005-2015 is the International Decade
for action ‘Water for Life’
Water…!
Introduction

5. Fig.1 : Water scarcity situation
-Micro irrigation report (2019)
5

6. Fig.2 : Trends and projection of population and per capita availability of
water per year in India
361 395
846 1027
1394
1640
5177
4732
2209 1820
1341 1140
0
1000
2000
3000
4000
5000
6000
1951 1955 1991 2001 2025 2050
Population
(Million),
Water
(m
3
)
Year
Population (Million) Per Capita Water Availability (m3)
Usage in (%) World India
Agriculture 69 82
Industry 23 12
Domestic use 8 6
Table 1: World’s Water Usage
Micro irrigation report (2018)
6

7. Why Irrigation ??
• Uncertainty of monsoon
• Uneven distribution of rainfall
• Crop requirements
• Nature of the soil
• To maximise the production
Why water to plants ???
7

8. Evolution of
irrigation from
1970 to present
Ramachandran et al., 2022
Water, 14, 719 8

9. Internet of Things (IoT)
‘The Internet of Things (IoT) describes the network of physical
objects-“things”-that are embedded with sensors, software and
other technologies for the purpose of connecting and exchanging
data with other devices and systems over the internet’
9

10. 1 Soil health assessment
2 Climate based
3 Water management
4 Nutrient management
5 Weed management
6
Harvesting related
IoT in Agricultural production
10

11. Architecture/ Deployment Models for IoT in Agriculture
Irrigation Management
• Employs a variety of methodologies for a variety of
applications.
• Hence, the design and deployment patterns are diverse.
• There is no one-size-fits-all approach to IoT architecture.
• Consolidation of the Internet of Things architecture is based on
a three- or four-layer architecture.
11

12. Fig.3 : Generic three-layer architecture 12

13. Fig.4 : Four-layer architecture 13

14. Major components of IoT based crop management
1. Sensors
2. Controller or gateway
3. Cloud platform
4. Pump Controller
5. Mobile application
14

15. Sensors in Agriculture
• The sensor collects data from the field and aggregates it for
processing in an IoT application
• The following are some of the more obvious sensors mentioned in
the cited article(s):
1. Water related sensors
2. Weather station
3. CO2 sensor
4. Sensor for GHGs
6. PIR motion sensor
7. Soil quality sensors 15

16. Sensors In Agricultural Water Mangement
16
1. Ground water monitoring sensors
2. Irrigation water quality sensors
3. Soil moisture sensors
4. Plant based sensors

17. Advantages of IoT in Agricultural Water Management
• Efficient resource utilization
• Minimize water usage
• Minimize human effort
• Save time
• Enhance Data Collection
• Improve security
17

18. Disadvantages of IoT in Agricultural Water Management
• Higher costs
• Lack of infrastructure
• Requires internet connectivity
• Farmers’ knowledge on technology
• Very complicated to plan, build and
manage
18

19. Various irrigation systems
developed using IoT
19

20. Automated Irrigation System Using a Wireless Sensor Network
and GPRS Module
• In the proposed system it consists of in-field Wireless Sensor Network
(WSN) and Wireless Information Unit (WIU) that allows the transfer of soil
moisture and temperature data
• WSN is responsible for the all sensing challenges, whereas WIU is used for
the data transmission through General Packet Radio Service (GPRS) module
to web page for the analysis purposes
• The information can be remotely monitored online through a graphical
application
Gutiérrez et al., 2014
IEEE Transactions on Instrumentation and Measurement, 63(1) 20

21. Fig.5 : Configuration of the automated irrigation system. WSUs and a WIU,
based on microcontroller, ZigBee and GPRS technologies
21

22. Fig.6 : Wireless Sensor Unit (WSU). (a) Electronic component Printed Circuit Board (PCB)
(b) Radio modem ZigBee (c) Temperature sensor (d) Moisture sensor (e) Rechargeable
batteries (f) Photovoltaic cell (g) Polyvinyl chloride container
22

23. Remote monitoring system for agricultural information based
on wireless sensor networks
• Proposed system has the sensor node for the collection of data
regarding the soil moisture
• Fetched data is transmitted to the base station with the use of
ZigBee
• Where user can analyze the data and this system work effectively in
controlling the proper required irrigation
Zhang et al., 2017
23
Journal of the Chinese Institute of Engineers, 40(1): 75-81

24. Fig.7 : Diagram of system design
24

25. Internet of Things based Application Smart Agricultural System
• Authors have used the Kalman filter to improve the accuracy of the
sensed data
• A protocol is used which works on the gathered data and the
weather conditions to achieve the automation of the irrigation system
and roofing system
• System works according to the irrigation requirement of the crops
while all the sensed data is stored to the cloud as the size of the
data fetched by the sensors from its surroundings
25
Navinay & Gedam, 2017
International Journal of Latest Engineering and Management Research, 2(4): 69-71

26. Automated Irrigation System-IoT Based Approach
• The proposed system checks the amount of water required by
a plant, with predefined values in the program
• A threshold value has been set according to which the smart
irrigation will take place so that the crop gets the required
amount of water needed by it
• Automatic irrigation is achieved through water pumps
according to the requirement of the crops
26
Mishra et al., 2018
3rd International Conference On Internet of Things: Smart Innovation and Usages, IoT-SIU, 1-4

27. Fig.8 : Flowchart of
process used
27
START
THRESHOLD VALUE IS
SET FOR THE PLANT
CHECK HUMIDITY
LEVEL IN THE SOIL
SWITH ON THE PUMP
GREEN LIGHT GLOWS
IF
HUMIDITY <
THRESHOLD
VALUE
NO
YES

28. Smart Water Dripping System for Agriculture/Farming
• On the basis of the sensed data by the consisting sensors in the system
regarding the soil parameters or condition of the soil
• Predefined values were given to the systems. These predefined values
further instruct the system whether to turn the system on or off
• If there is any fault in the system regarding the irrigation, an alert system is
there to alert the user through an android application
• If the conditions are rainy this system works perfectly and allow user to have
knowledge regarding the weather condition as at this time, there is a lesser
need of irrigation
• To achieve these functions authors have used Arduino microcontroller
28
Padalalu et al., 2017
2nd International Conference for Convergence in Technology (I2CT)

29. Fig.9 : System Block Diagram
29
Fig.10 : Android Application

30. Vegetable Traceability with Smart Irrigation using IoT
• The proposed system is based on Microcontroller, and the
system could provide real-time feedback
• The system can be used to monitor and control all the required
processes effectively
• The system can reduce water wastage
30
Hate et al., 2018
International Conference on Smart City and Emerging Technology, ICSCET

31. Fig.11 : System Architecture
31

32. Development of Smart Drip Irrigation System Using IoT
• In this paper, the authors presented that Deep learning and AI can be used
to detect disease and pest in the crop with the help of image processing
• They have developed an android based app to keep the records; the
database will store all the information received from the sensors
• As the backup, if there is no connectivity via the internet system, will keep
updated its user through SMS
• A whole dedicated project is to create a smart and effective irrigation
system using the Internet of Things
32
Math et al., 2019
DISCOVER 2018 - Proceedings

33. 33
Fig.12 : Block Diagram of the System
Fig.13 : Relay Status on the webpage

34. Research findings
34

35. Table 2. 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
Barkunan et al., 2019
Coimbatore, India 35
Computers and Electrical Engineering, 73 : 180-193

36. 36
Fig.14 : Estimated amount of water for different irrigation techniques in paddy
Barkunan et al., 2019
Coimbatore, India Computers and Electrical Engineering, 73 : 180 -193
Manual Flood Irrigation Drip Irrigation Smart Drip Irrigation
Different Irrigation Methods
Water
level
(in
mm)

37. 37
Fig. 15 : Percentage utilization of water for different irrigation systems in paddy
Barkunan et al., 2019
Coimbatore, India Computers and Electrical Engineering, 73 : 180-193
With respect to Flood With respect to drip With respect to Smart
Different Irrigation Methods

38. Table 3. Water Use Efficiency (WUE) and water saved (%) as influenced by
sensor based irrigation management in Rice
Treatments
Water use efficiency
(kg ha-cm-1)
Water saved
(%)
T1 : Surface irrigation 91.6 -
T2 : Drip irrigation at 3 days interval 126.2 19.64
T3 : Green SMI based drip irrigation 148.1 3.57
T4 : Yellow SMI based drip irrigation 120.8 30.5
T5 : Sensor based drip irrigation at 25% DASM 149.3 -
T6 : Sensor based drip irrigation at 50% DASM 128.8 27.2
T7 : Sensor based drip irrigation at 75% DASM 110.2 42.8
38
Chaitra, 2019
GKVK, Bangalore Ph.D.(Ag.) Thesis
( Note: SMI = Soil Moisture Indicator )

39. Table 4. Average volume (m3 ha-1crop-1) of irrigation water
requirement in different treatments in rice
39
Treatments
Project sites
Can Tho Tra Vinh An Giang
IoT AWD 1100 1430 1645
Man. AWD 1375 1673 1891
Control treatment 2063 2192 2364
Water savings over Man. AWD -20% -15% -13%
Note: Man. AWD: Manual Alternate Wetting and Drying; IoT AWD: Internet of Things
Alternate Wetting and Drying
Pham et al., 2021
Agronomy for Sustainable Development, 41 (3) :43
Mekong Delta, Vietnam

40. Fig.16 : Water requirement and yield of rice as influenced by
two irrigation methods
40
The Pharma Innovation Journal, 11(5): 292-296
Kalyan et al., 2022
Conventional v/s IoT
WR
(litres
kg
-1
)
Yield
(t
ha
-1
)
Conventional method IoT method
Water Yield

41. Treatments
Grain yield
(kg ha-1)
Straw yield
(kg ha-1)
Harvest
Index (HI)
T1 : Puddled transplanted rice 7589 8057 0.49
T2 : Puddled transplanted rice with alternate wetting and drying 4569 4998 0.48
T3 : Aerobic rice cultivation with surface irrigation at two days interval 5479 6129 0.47
T4 : Aerobic rice cultivation with drip irrigation at two days interval 6159 6831 0.47
T5 : Aerobic rice cultivation with sensor based surface irrigation 5614 6158 0.48
T6 : Aerobic rice cultivation with sensor based drip irrigation 6657 7302 0.48
T7 : Aerobic rice cultivation with sensor based surface irrigation at 15 % DASM 5822 6417 0.48
T8 : Aerobic rice cultivation with sensor based drip irrigation at 15 % DASM 7599 8069 0.49
T9 : Aerobic rice cultivation with sensor based irrigation and drip fertigation 8233 9032 0.48
S.Em ± 386 412 -
CD (p=0.05) 1047 1123 -
Table 5. Grain yield and straw yield of rice as influenced by sensor based
irrigation management
41
Lathashree, 2019
GKVK, Bangalore Ph.D.(Ag.) Thesis

42. Table 6. Evaluation of Water Productivity for Lowland Rice
Under Sensor based Deficit Irrigation System
Treatment Yield
[t ha-1]
Water productivity
[kg m-3]
Yield reduction
[%]
Water Savings
[%]
T1: 3cm of ponding
water level
4.93 1.08 5.70 56.8
T2 : IoT with -150mbar 4.33 1.49 17.2 72.4
T3 : IoT with -300mbar 3.15 1.78 39.7 83.1
Reference 5.23 0.58 - -
Note: Reference yield and Water productivity are long term average values for Bg 300 rice
variety under Sri Lankan tropical climate and flooded conditions
42
Dias et al., 2016
Mechanization in Agriculture, 62(6): 20-21
Dresden, Germany

43. Table 7. Average grain yields (ton ha-1) at 14% moisture
content in different treatments in rice
43
Treatments
On-farm trial 1 On-farm trial 2
Can
Tho
Tra
Vinh
An
Giang
Can
Tho
Tra
Vinh
An
Giang
IoT AWD 7.14 7.49 3.15 6.11 4.68 4.46
Man. AWD 6.45 7.26 3.10 5.88 5.31 4.30
Control treatment 5.92 7.00 3.20 5.96 4.81 4.27
Difference
(IoT AWD - Man. AWD)
11 % 3 % 2 % 4 % -12% 4%
ANOVA test result-treatments (Prob > F) 0.0055
Pham et al., 2021
Agronomy for Sustainable Development, 41 (3): 43
Mekong Delta, Vietnam

44. 44
Year Irrigation
Method
Rainfall
(mm)
Irrigation
Water (mm)
Rice Yield
(kg ha-1)
Water Productivity
(kg m-3)
(IW) 1 (IW + R) 2
2017 Conventional 1120.4 428.9 7172 1.67 0.46
Automatic 356.9 7087 1.99 0.48
2018 Conventional 806.4 699.0 8589 1.23 0.57
Automatic 621.5 8391 1.35 0.59
2019 Conventional 606.2 708.1 8549 1.21 0.65
Automatic 604.1 7992 1.32 0.66
Note: 1 (IW) denotes irrigation water; 2(IW + R) denotes irrigation water with rainfall.
Table 8. Evaluation of Automatic Irrigation System for Rice
Cultivation and Sustainable Agriculture Water Management
Lee, 2022
Sustainability, 14: 11044
Hwaseong City, South Korea

45. Table 9. Plant height, grain yield, and yield components of rice
irrigated by MIS, AWD and basin irrigation
Treatments
Plant
height (cm)
Grain yield
(tons ha-1)
Grain number
per Panicle
Panicles
Unfilled
grain (%)
MIS : Modern Irrigation System
(IoT based irrigation)
70.23a 3.56a 129 25.70 37.16
AWD : Alternate Wetting and
Drying
56.78b 1.71c 114 23.13 51.44
Basin method 60.92b 2.56b 126 25.80 45.95
Note: Means followed by the same letter are not significantly different at the 5% LOS
45
Laphatphakkhanut et al., 2021
Paddy and Water Environment, 19: 699–707
Nakhon Pathom, Thailand

46. Fig.17 : Water foot print of rice cultivation system which
irrigated by MIS, AWD and basin irrigation
46
Laphatphakkhanut et al., 2021
Paddy and Water Environment, 19: 699–707
Nakhon Pathom, Thailand
Green WF Direct blue WF Indirect blue WF
WF
(m
3
ton
-1
of
paddy)
MIS AWD Basin

47. Treatments
Days after sowing
30-40 40-50 50-60 60-70 70-80 80-90 90-100
100-
110
Total
T1 : Puddled transplanted rice 0.77 7.87 8.70 13.9 15.53 17.30 18.94 13.8 96.85
T2 : Puddled transplanted rice with alternate wetting
and drying
0.57 7.46 8.45 13.6 15.01 16.59 18.25 13.4 93.35
T3 : Aerobic rice cultivation with surface irrigation at
two days interval
0.23 3.90 4.10 4.23 4.32 5.23 6.99 3.58 32.58
T4 : Aerobic rice cultivation with drip irrigation at two
days interval
0.18 2.22 3.15 3.22 3.71 4.53 6.11 3.21 26.33
T5: Aerobic rice cultivation with sensor based surface
irrigation
0.22 3.41 3.85 3.98 4.01 4.98 6.78 3.51 30.74
T6 : Aerobic rice cultivation with sensor based drip
irrigation
0.16 2.18 3.02 3.11 3.67 4.21 5.87 2.95 25.17
T7 : Aerobic rice cultivation with sensor based surface
irrigation at 15 % DASM 0.20 3.38 3.45 3.67 3.85 4.67 6.53 3.33 29.08
T8 : Aerobic rice cultivation with sensor based drip
irrigation at 15 % DASM
0.14 2.04 2.95 2.82 3.45 3.92 5.10 2.89 23.31
T9 : Aerobic rice cultivation with sensor based
irrigation and drip fertigation
0.11 2.01 2.34 2.65 2.78 3.17 3.75 1.47 18.28
CD (p=0.05) 0.04 0.57 0.31 0.34 1.42 1.53 1.77 1.23 3.83
GKVK, Bangalore
Table 10. Methane emission (kg ha-1) as influenced by sensor based irrigation
management in rice
47
Lathashree, 2019
Ph.D.(Ag.) Thesis

48. Table 11. Irrigation energy cost (Vietnam dongs per ha; the exchange rate
of VND versus USD was 23,181) in different treatments in rice
Treatments
On-farm trial 1 On-farm trial 2
Can Tho An Giang Can Tho An Giang
IoT AWD 115 47 79 183
Man. AWD 152 91 106 177
Control treatment 176 136 230 292
Energy cost savings
(IoT AWD - Man. AWD)
-24 % -48 % -25 % 0%
ANOVA test result-treatments (Prob > F) 0.0021
Two-sample t test result-locations (Pro T < t) 0.0036
48
Pham et al., 2021
Agronomy for Sustainable Development, 41(3): 43
Mekong Delta, Vietnam

49. Conclusion
• Agriculture is an application-specific domain in which the
implementation of the IoT and other emerging modern
techniques and tools can provide new solutions for traditional
problems
• IoT improves the crop and water productivity, efficiency and
reduces human intervention in rice cultivation
• However, higher capital requirement and initial resistance to
new technologies at farmer level are major constraints
49

50. 50
THANK YOU