ROLE OF IOT ,SENSORS AND NANOBIOSENSORS IN AGRICULTURE.pptx

WATER TECHNOLOGY CENTRE,PJTSAU

is a branch of (AI) and computer science which focuses on the
use of data and algorithms to imitate the way that humans
learn, gradually improving its accuracy.
is a subset of ML, which is essentially a neural network with
three or more layers,which attempt to simulate the behavior
of the human brain—though far from matching its ability—
allowing it to “learn” from large amounts of data.
It is the science and engineering of making intelligent
machines, especially intelligent computer programs. It is
related to the similar task of using computers to understand
human intelligence.

In 2021, MeitY (Ministry of
electronics and information
technology) introduced and
implemented numerous programs
to deploy rising technologies like
AI, ML, Blockchain, IoT, Robotics,
and Big Data.
Fig-1: Investments in AI all over the world

GOVERNMENT OF INDIA WITH IBM
PRADHAN MANTRI FASAL BIMA YOJANA
PM-KISAN
GOVERNMENT OF KARNATAKA WITH MICROSOFT
AGRI-UDAAN
MAHA AGRITECH PROJECT

WATER TECHNOLOGY CENTRE,PJTSAU 6
WHAT IS IOT?

Term was proposed by Kevin Ashton in 1999
Connection of each and everything to internet
Relationship will be people – people, people-things, things-things.

PHYSICAL
WORLD

 IoT is a combination of :Sensors & Actuators, Connectivity, People & Processes.
Fig-2: Network of Internet of Things

The use of IoT in agriculture is commonly referred to as Smart Farming or Smart Agriculture. It uses
various IoT sensors to send the farm’s data, like humidity, temperature, soil moisture, etc. to the
cloud which can be monitored and controlled from anywhere in the world.
How IoT is implemented in Agriculture FOR SMART AGRICULTURE:
IMPLEMENTATION OF
SMART AGRICULTURE
USING IoT
PRECISION FARMING
AGRICULTURAL DRONES
LIVESTOCK MONITORING
SMART GREENHOUSES

Precision Farming
• It is the practice of making agricultural processes
more accurate and controlled for rearing livestock
and crop growing.
• IoT introduces the idea of connecting automated
vehicles to devices using the internet for better
data storage and analytics.

Agricultural Drones
• Drones are being used to improve several
agricultural practices in Smart Farming Using IoT.
There are two types of agricultural drones,
1. surface-based
2. Aerial-based.
• They are used for activities such as:
Assessment of crop health
Spraying of crops
Soil and field analysis,
Crop monitoring and irrigation.

Livestock Monitoring
• Livestock monitoring is also become ‘Smart’ by
using IoT.
• It enables farmers to monitor the condition and
activities of animals.

Smart Greenhouses
• Traditional greenhouses relied on manual
interventions to control environmental parameters
for the growth of crops where there is energy and
production loss.
• By using IoT systems it can monitor and control
aspects such as
Temperatures
Luminosity
Soil and mineral content
Humidity

1 2 3 4
Efficient scaling Improved quality Be in complete
control
Manage pricing
and costs
What Ways IoT Technology can Enable and Transform Agriculture?

Efficient Scaling
• Automation of processes like fertilization, pest control ,
irrigation by utilization of smart devices and with this
agricultural capacity can be enhanced.
Improved Quality
• Crop diseases can be detected at field level before a
huge damage by using IoT.

Be in Complete Control
• Integrating IoT technology enables us to sustain and maintain
control over all types of processes, and this way, production risks
can be reduced.
Manage the Pricing and Costs
• Better control over the agriculture production leads to compact
waste levels and enables to handle the costs and pricing better.

• IoT makes it possible to avoid
challenges and remove all issues that
may arise during farming processes.
• Efficient water management .
 Continuous monitoring of land.
 Crop monitoring can be done easily.
Advantages: Disadvantages
 Continuous internet connection , which is
an issue in rural areas.
 Acclimatisation to the equipment for
farmers is an issue .
 Several kinds of network attacks.
 System designing, developing, and
maintaining the large technology is quite
complicated.

Lack of security
• Poor communication infrastructure
• Remote areas and less access to internet
• Connection issues
Lack of
infrastructure
• Expensive
High cost
• Difficult to protect the data collected
IoT challenges in the agriculture sector
Lack of security

Sensors
Fig-3: Building blocks of Internet of Things

What are Agriculture Sensors?
Introduction:
• Sensors used in smart farming are known as agriculture
sensors.
• Assist farmers to monitor and optimize crops by adapting to
changes in the environmental conditions.
• Installed on weather stations, drones and robots used in the
agriculture industry.
• Controlled using mobile apps
• They can be controlled directly using wifi or through cellular
towers with the help of mobile phone app.

Climatic conditions
Soil properties
Pest detection
Temperature and
humidity
Optical, electro
chemical ,
dielectric
Image capturing
and gas sensors
What sensors can monitor: Sensors

TYPES OF SENSORS
Location
Weather stations
Air flow
Yield monitoring
Weed mapping
Variable spraying
Salinity mapping
Mobile as a
tool
Soil electrical
resistivity
Mechanical
Electro chemical
Optical

Green Seeker Handheld Crop Sensor
• Active light source optical sensor
• Used to measure plant biomass/plant health
• Pull the trigger and NDVI measurement appears on LCD display
immediately.
• Measures plant NDVI readings where
• NDVI = (NIR-Red)/(NIR+Red).
• NDVI can range from 0.00 to 0.99.
Fig-4: Green seeker
operating in paddy field

Paddy Smart IoT sensor system
Fig-5: IoT sensor in paddy field

Exploded view illustration of the device
Optical images of the flexible sap flow sensor mounted
on a single plant leaf.
Plant-Wearable Sensor In Situ Monitors Water Transport in Plant

The vane-shaped sensor design can simulate the surface characteristics of a sheet, allowing
the situation of the surveyed sheet to be reflected more accurately. By varying the dielectric
constant of the top surface of the sensor, water vapor, mist, ice can be determined.
Leaf Wetness Sensor

Fig-6: Automated Irrigation System Drip Irrigation for Single Land Section
Irrigation system with brain

Fig-7:Sprinkler Irrigation for Multiple Land Sections

Factors Driving Adopting Sensors Within IoT
There are three primary factors driving the adoption of sensor technology i.e. price, capability, and size.
Cheaper sensors
• Sensors vary widely in price,
but many are now cheap
enough to support broad
business applications.
Smarter Sensors
• Sensor does not function by
itself, it is a part of a larger
system that comprises
microprocessors, modern
chips, power sources, and
other related devices.
Smaller Sensors
• Micro-electro-mechanical
systems (MEMS) sensors,
small devices that combine
digital electronics and
mechanical components, have
the potential to drive wider IoT
applications.

What is biosensor?
• An analytical device which converts a biological reaction into an electrical signal. Biosensors can be
defined as analytical devices which contain a combination of biological elements like sensor system
and a transducer.
• Nano biosensors are next generation of biosensor which are
more compact and linked to sensitized element to detect selective
analyte at ultra-low concentration through a physico-chemical
transducer.
•In book: New Pesticides and Soil Sensors .
Fig-8: Mechanism of biosensors.

Crop stress
management
• Detect chemical metabolites and phyto hormones in
response to biotic and abiotic stress and recovery
action can be taken quickly.
Smart farming
• Based on parameter recorded by the nano sensor,
need based action will increase the crop yields and
reduces the unwanted manpower resources like
fertilizer, pesticide
To maintain
seed purity
• To ensure the genetic purity is the detecting pollen
load that cause contamination and used to identify
the specific contaminating pollen and thus reduces
contamination.
Disease
detection
• Early prediction of the occurrence is the only
prevention to eradicate diseases at the root.
• Diagnose plant health issues before these actually
get visible to the farmer.
What nano biosensors can monitor?

Nano wire biosensors
Electronic nano biosensors
viral nano biosensors
PEBBLE nano biosensors
Nano shell biosensors

Improves crop productivity
Controls pollution
Lowers cost of cultivation

Very sensitive as it
detects minor changes
Highest efficiency
Increased surface
to volume ratio
Advantages Disadvantages
very sensitive and
error prone.
still under infancy
stage.

Fig-9 : Irrigation through Automated versus Manual with Drip and Sprinkler
Ramachandran et al. (2018)

Fig-10: Manual vs automated irrigation through drip
Kumar et al. (2017)

Fig-11: Response of sensor output voltage to soil moisture
Kumar et al. (2017)

Fig-12: RGB* results: Experiment 1,2,3 in vineyard for yield estimation
Experiment One: Individual Bunches under Laboratory Conditions, Experiment Two: Individual Bunches in Field
Conditions, Experiment Three: Individual Vines in Field Conditions
.

Fig-13: RGB-D results : Experiment 1,2,3 in vineyard for yield estimation
Hacking et al. (2018)

Fig-14 : Water used (ha-cm) and water use efficiency (kg ha-1 cm-1 ) as influenced
by sensor based irrigation management in rice
T1 : Puddled transplanted rice
T2 : Puddled transplanted rice with
alternate wetting and drying
T3 : Aerobic rice cultivation with
surface irrigation at two days interval
T4 :Aerobic rice cultivation with drip
irrigation at two days interval
T5 :Aerobic rice cultivation with sensor
based surface irrigation
based drip irrigation
based surface irrigation at 15% DASM
based drip irrigation at 15% DASM
based drip irrigation and fertigation
Lathashree et al. (2019)

Treatment Kg N ha-1 Total
nitrogen
(kg)
Grain
yield
(Mg ha−1 )
Total
nitrogen
uptake
(Mg ha−1 )
AE
(kg grain
kg−1 N
applied)
PE
(kg grain
kg−1 N
uptake)
Basal Crown root
initiation
5-6 stage
T1 0 0 0 0 1.83 37.1
T2 60 0 30 90 4.01 79.8 24.7 47.7
T3 30 30 24 84 3.87 73.9 24.8 51.5
T4 45 45 21 111 4.45 97.1 24.0 41.8
Table-1: Evaluation of Green Seeker-based N management in wheat (cultivar PBW 343) at Ludhiana, India during
2006–2007
Sharma et al. (2011)

Fig-15 : Relationship between NDVI (green seeker) at 5 growth stage and winter wheat grain
yield, Lahoma.
Walsh et al.(2013)

Fig-16: Effect of N application schedules by using Green seeker on yield in Rice
Biswal (2021)
T1: NITROGEN OMISSION
T2: N at basal and Active tillering
T3: N at basal , AT ,PI
T4:N at basal and NDVI <0.75
T5:N at basal and NDVI <0.8
1509
2742
3324
3770
4438
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
treatment 1 treatment 2 treatment 3 treatment 4 treatment 5
grain
yield
(kg
ha
-1
)
Treatments

Xiao et al. (2013)
Fig-17 : Integrated soil moisture and water depth sensor for paddy fields by wireless, integrated, frequency-
domain soil sensor (WFDSS).
Error analysis : (a) relative error analysis for moisture content (b) relative error analysis for water depth
The performances of the sensor were tested on measurement range, stability, system error, energy consumption and
transmission distance. Results indicated that the optimum range of the soil moisture content was from 30% to 50%,
and the measurement range of water depth was from 1 to 8cm.

Kumar et al.(2012)
Fig-18: Moisture content and water level depletion in soil measured by soil moisture sensors at 20
and 30 cm depths in drip, furrow , check basin irrigation.

Effect of irrigation methods and irrigation schedules on water productivity (kg m³) of
maize during rabi, 2017-18
S₁-Tensiometers (irrometer)
S2-Granulated gypsum blocks
(Water mark sensors)
S3-Profile probe (Delta-T)
S4-Nano sensors (IITB)
S5-Soil moisture indicator
(ICAR)
S6-IW/CPE ratio or Epan

Fig-19: Effect of varying soil moisture: (a) soil moisture, (b) humidity, (c) temperature, (d) fuzzy
output, and (e) diaphragm pump.

Fig-20: Effect of varying temperature and humidity: (a) soil moisture, (b) humidity, (c) temperature,
(d) fuzzy output, and Diaphragm pump (e) diaphragm pump.
Al-Ali et al.(2019)

Fig-21:Detailed maps based upon weed sensing by optoelectronic mapping system in all crop rows (sensors separated 0.75 m).
Green points indicate presence of weeds, i.e., weed coverage ≥15%. Yellow points indicate weed free areas, i.e., <15% weed cover.
Andujar et al.(2011)

Esposito et al.(2021)
Fig-22: UAV weed recognition and removing unwanted weeds.

Fig-23: Comparision between LiDAR-estimated heights of weeds and actual weed height in
wheat crop .
Shahbazi et al.(2021)

Fig-24: A. Interaction of leaf tissue with light depends on structural and leaf chemical properties.
B. During pathogenesis , leaf pathogen influence leaf structural and chemical properties and by
this leaf optics are altered, which can be detected by hyperspectral image sensors.
Mahlein (2016)

Conclusion:
• The irrigation system was automated by connecting solenoid values which helped increase
the agility of the control. Combining IoT, cloud-connectivity, and optimization models will help
enhance water efficiency of the agriculture systems.
• Soil moisture sensor is an instrument for making rapid measurement of the soil moisture
content in the root zone. It is essential tool for many applications, including understanding of
soil water dynamics, evaluation of water stress, and validation of soil moisture modelling.
• Using image sensors for yield estimation worked accurately and with insightful information
regarding the expected yield, facilitating managerial decisions to achieve maximum quantity
and quality and assisting the farmers with logistics.
• Using of soil moisture sensors to detect soil moisture is quickest and better method .
• To achieve high N use efficiency, a site specific N management strategy using Green Seeker,
an optical sensor was evaluated and it outperformed the blanket nitrogen recommendation.

• The fuzzy logic algorithm was developed to analyze the environmental and soil conditions to
decide when it should irrigate the farm and apply fertilizer .
• Optoelectronic sensors installed in field to detect the weed areas , compared to digital
images and the data is accurate upto 85%.
• Conventional methods of uniformly spraying fields to combat weeds, requires large
herbicide inputs at significant cost with impacts on the environment. More focused weed
control methods such as site-specific weed management (SSWM) have become popular by
using LiDAR .
• Nano sensors or nano biosensors also play vital roles in detecting and controlling the use of
pesticides, fertilizers, as well as many other growth parameters associated with crops, which
provide timely information for precise decision making and agricultural
management. Nevertheless, more field validation tests are needed for sensor applications in
precision farming.

• Therefore, IoT used in agriculture has a big promising future as a driving force of efficiency,
sustainability and scalability in this industry.
• It helps farmers in minimizing usage of fertilizers, pesticides , insecticides, herbicides and
improves efficiency .
• It helps in reducing environmental hostile effects.
• Sensors play a very important role in irrigating fields and help in detecting pathogens.
• Future line of work should be done in sensors and nano bio sensors and where they can be
efficiently used at large farm levels by farmers without any errors.

Submitted by:
K. Archana
RAM/2020-107
Submitted to:
Dr . T . L. Neelima
Senior scientist
(Agronomy)

ROLE OF IOT ,SENSORS AND NANOBIOSENSORS IN AGRICULTURE.pptx

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