Hitaishi Kuriyal 58111 biosenser.pptx

IMPORTANCE OF BIOSENSOR IN
PRECISION VEGETABLE FARMING AND
THEIR ECONOMIC FEASIBILITY
HITAISHI KURIYAL,
Id No. 58111
Ph.D Vegetable Science
Doctoral Seminar : 01
1
Importance of biosensor in precision vegetable farming and their economic feasibility

Content
1. Needs of
Precision
farming
2. Introduction
of PF and
Biosensor
3. Benefits,
Prospects and
Concept of PF
4. Components
of PF and
Biosensor
6. Challenges
and Future
Thrust
2
5. Research
findings
7.Conclusion

NEED OF
BIOSENSOR
IN PRECISION
FARMING
The economical and environmental benefits
could be visualized through reduced use of
water, fertilizers, herbicides and pesticides.
Managing an entire field
Offers the potential to automate and
simplify the collection and analysis of
information.

Introduction
It is an approach to farm management that uses
localized information to ensure that the crops and
soil receive exactly what they need for optimum
health and productivity.
Professor Pierre C. Robert is considered as the
Father of Precision farming.
4

5

RIGHT
INPUT
AT RIGHT
TIME
IN RIGHT
AMOUNT
AT RIGHT
PLACE
IN RIGHT
MANNER
Prospects of Precision farming in Indian situations
6

Concept of Precision farming
Agronomic
perspective
Environmental
perspective
Technical
perspective
Economical perspective
7

GLOBAL
POSITIONING
SYSTEM (GPS)
Components of Precision Farming
GEOGRAPHICAL
INFORMATION
SYSTEM (GIS)
REMOTE
SENSING (RS)
VARIABLE
RATE
TECHNOLOGY
(VRT)
YIELD
MONITORING
(YM)
8

GEOGRAPHICAL
INFORMATION SYSTEM
(GIS)
It is computer based system which
provide information on field
topography, soil types, surface
drainage, subsurface drainage, soil
parameters, water management,
fertilizer application rates and crop
yield .
9

GLOBAL POSITIONING
SYSTEM (GPS)
GPS allows farmers to accurately
navigate to specific locations in the
field, year after year, to collect soil
samples or monitor crop
conditions.
10

REMOTE SENSING (RS)
a. It is a tool which gather information
in the form of map with the help of
satellite
b. The specific application of remote
sensing techniques can be used for -
• Detection
• Identification
• Measurement
• Monitoring of insect, pest and disease
incident.
11

Key steps of RS
12
Source of electromagnetic
radiation
Energy transmission between
the sources of the earth’s
surface and interaction with the
atmosphere
Interaction of electromagnectic
radition with the earth;s surface,
absorption, transmission or
reflection
Pre processing of sensor
Transmission of reflected
radiation to the sensor
Signal detection by sensor and
conversion to photographic or
electrical output
Ground tooth and other
information
Interpretation of data and
analysis of information

RS are classified as passive or active sensors
depending on the light sources
Passive sensors measures the amount of solar
energy reflected from the objects. Generally they
are mounted on satellite or airplanes. Eg:
LANDSAT, LISS, Quick Bird.
Active sensors have defined or fixed
wavelengths and generally mounted on satellite or
ground vehicles. Eg: RADARSAT, LIDAR
Types of RS
13

Land
monitoring
Crop condition
monitoring
Soil moisture
status
monitoring
Crop pest /
diseases
detection
Land use, land
cover mapping
Use of Remote Sensors
14

YIELD MONITORING (YM)
It enable to a producer to measure
yield and grain moisture in a field
using crop yield measuring devices
installed on harvesting equipment.
15

It is fitted on
the harvester
to determine
the actual
quantity of
grain being
harvested.
It measures
the
capacitance
of the grain.
It measures
the speed of
the combine
harvester for
accurate
results.
It is fitted on
the grain
combine which
constantly
gives locations
to each
measurements
taken
It is the
component that
is fitted inside
the cab of the
harvester
where the
farmer is
located.
Grain flow -
sensor
Yield Mapping Components
Grain
moisture
sensor
Ground
speed sensor
GPS
receiver
Yield monitor
display
16

How mapping can
help farmer’s?
Geo-
informatics
sensor
Digital
cartography
Internet of
things (IoT)
Processing
Analytics
17

GeoPard to provide Precision Farming solution
 Geopard provide multi-layer analysis and
visualizes several attributes of the yield
data like moisture, mass, volume, speed
etc.
 They also offer professional programs and
suggest their client on how to choose
viable seeds, the science of planting and
harvesting crops and soil samples.
18

19 Importance of biosensor in precision vegetable farming and their economic feasibility

VARIABLE RATE
TECHNOLOGY (VRT)
a. It is the implementation of
gathered information for site
specific agriculture.
b. Uses of VRT-
• Nutrients application
• Pesticides
• Seeding
• Irrigation
20

Biosensor is an analytical device
which converts a biological reaction
into an electrical signal.
The term biosensor was introduced
by Clark and Lynos in 1962.
21
Biosensor

22
Biosensor
Physical
component
Transducer
Amplifier
Biological
component
Sensitive bio-
element
Analytic
Components of Biosensor

WORKING OF BIOSENSOR
23

24 Importance of biosensor in precision vegetable farming and their economic feasibility

25
TYPES OF
BIOSENSOR

ELECTROCHEMICAL BIOSENSOR
26
 It is a very simple device.
 It measures the measurement of
electronic current, ionic or by
conductance changes conceded by
bio- electrodes.

Volatile Organic Compounds (VOCs) Sensor for Stress Management in
crops
27
• VOCs are chemical substances produced and emitted by plants and other organisms
in gaseous forms
• Plants emit these volatile organic compounds through flowers, leaves, roots or other
tissues with complex processes.
• The emission of VOCs is significantly affected by the surroundings plants
atmosphere.
• During the process of biosynthic conversion of solar energy to chemical energy,
multiple organic compounds are produced.
• These released VOC from plants can interact with other organism or neighbouring
plants, during signalling and protecting plant from biotic or abiotic stress that are
affecting our crops globally.

a. They gather information on plant’s
health, the interaction of plants with
animals/insects, pesticides and other
factors influceing the plants
b. Electronic Aroma Detection (EDA) had
led to the development of e-nose sensor
c. E-nose can find the age, product
consistency, freshness of the fruit,
ripeness, quality of the product, purity
and can also estimate the shelf life.
a. Micro- electro mechanical system
(MEMS) technology is rapidly
increasing
b. This technology has a great potential in
long terms and on demand measuring
plant VOC and other biomarkers and
can be a new tool for crop health
diagnostic in near future.
28
Use of E-Nose for VOCs Analysis
Plant wearable sensors for
Monitoring plant stress

OPTICAL BIOSENSOR
• These sensors have the potential to
detect the stress at the beginning in
the plants with the help of nIR
imagining devices.
• One of the most widespread optical
remote sensing technologies used for
robot localization, mapping and
obstacle avoidance is the Light
Detection And Ranaging (LIDAR).
•
29

PIEZOELECTRIC BIOSENSOR
 Piezoelectric biosensors are a group
of analytical devices working on a
principle of affinity interaction
recording. A piezoelectric platform or
piezoelectric crystal is a sensor part
working on the principle of
oscillations change due to a mass
bound on the piezoelectric crystal
surface.
30

How Robots helps in sensing in greenhouse
31
 Greenhouse robots guide and navigate, however their main task is- spraying or for transporting a
robot from plant to plant during the harvesting process.
 Sensor used in robots-
1. Global navigation satellite system (GNSS sensor) – this is more appropriate for outdoor
navigation, some greenhouse robots adopt a GPS or differential GPS receiver.
 It comprises Accelerometers, Gyroscopes and other Electro-mechanical devices
 Most commonly used GNSS is Real Time Kinematic (RTK) it provides centimetre accuracy
2. Electromagnetic and Ultrasonic sensors :
 Ultra – wideband (UWB) indoor positioning system : This technology allow for relative
localization without visual contact and avoid the limitation of GPS when moving near to high
vegetation.
 Most commonly use UWB is Spread Spectrum Sound (SSSound) technology, which is able to
determine the position within a 30m × 30 m area to an accuracy of 20mm.

32
3. Crop detection system : it is the activity carried by the robots through the use of various
visual sensors like RGB-D camera
 RGB-D camera : are adopted to have a 3D location of the vegetable, shape and colour
 Arad et al used RGD-B picture to estimate the angle at which fruit was positioned around
the stem in order to find the stem relative to the fruit.
4. Fruit grasping system : Lehert et al presented a new sweet pepper harvester, which was
characterised by a suction cup as a gripper and effectors and an oscillating blade to cut the
peduncle above. The sweet pepper is collected by gripping it through a vacuum gripper,
which is generated by vacuum pump, then cut by the oscillating blade.
5. Spraying system : Rincon et al have developed a remote controlled self propelled electric
sprayer in which 4 different configuration were tested in a greenhouse tomato crop to
evaluate the efficiency of the treatment application.

BIOSENSOR USED IN PRECISION FARMING
33
Experiment name SENSOR USED/
machine used
Crop REFERENCE
Microbiological Risk Assessment of Ready-to-Eat Leafy Green
Salads via a Novel Electrochemical Sensor
BIONOTE for
Liquids
Lettuce Grasso et al., 2022
Ground-based remote sensing for assessing water and nitrogen
status of broccoli§
AgIIS Broccoli Shikha et al., 2007
Correlation of gaseous emissions to water stress in tomato and
maize crops: From field to laboratory and back
VOCs- Metal-Oxide
(MOX) gas sensors
Tomato and
maize
Fabbari et al., 2020
Performance Evaluation of a Harvesting Robot for Sweet Pepper Fin Ray; Lip type Sweet pepper Bac et al., 2017
A Vision Servo System for Automated Harvest of Sweet Pepper in
Korean Greenhouse Environment
Stereo vision system Sweet pepper Lee et al., 2019
A Robust Mature Tomato Detection in Greenhouse Scenes Using
Machine Learning and Color Analysis
Support Vector
Machine with
Histograms of
Oriented Gradients
Tomato Liu et al., 2019

How Drones can help farmer’s?
34
• Farmers need to
add technical GPS
information of the
required crop area
into the drone’s
navigation system.
Analysis of Crop
Area
• Autonomous
drones can enter
the flight location
and patterns in
their system for
data capturing and
analysis.
Using
Autonomous
Drones
• These drones are
self-reliant in
collecting required
data or
information
through their
multispectral
sensor/RGB
sensors.
Data Uploading
• Once the data is
collected, the
technology helps
format it so
farmers or end
users can easily
interpret or
analyse it.
Simplifying the
Output

PM Kisan Drone Yojana
35
 PM Kisan Drone Yojana (FEB, 2022) : Under this scheme farmers will get 40 to 75 %
subsidy on the cost of the drone. After joining the scheme , the govt. will provide financial
assistance upto a maximum of Rs. 5 lakh as a grant to the farmer.
 Certificate to fly drone : It is necessary to be officially trained to promote drones in
agriculture. For this, drone certification has to be obtained. Drone operators can easily get
drone certification by visiting the official website Digital Sky. 100 rupees have to be applied
for this process of flying the drone.
 Drone test drive has to pay 1000 fees : A license is also required to fly a drone. For this
there is a test drive of the drone. 1000 rupees have to be deposited as fees. A total of Rs
1,100 has to be deposited including the amount of application and test drive.
 Ability to fly a drone : Eligibility has also been set by the Central Government to fly the
drone. Must be over 18 years of age to fly an agricultural drone. Not allowed for younger
age.

GB Pant University of Agri & Tech signs MoU for research to
promote use of drones
MoU was signed by Director of
Experiment Station Dr Ajeet Singh Nain
on behalf of the University, while Dr
Ajeet Singh Tomar, vice president
(R&D) signed on behalf of Dhanuka
Agritech Ltd
G.B Pantnagar University of Agriculture
and Technology has signed the MoU for
research to promote uses of drones With
the help of this drone farmers can
identify the diseases in the crop and
apply chemicals.
36

Government Partnership
37
In 2018, NITI Aayog
partnered with IBM
for developing a crop
yield prediction
model using AI
sensor
Crop yield prediction
model using AI
AI sensors for smart
farming
Indian govt. has
partnered with
Microsoft for
empowering small
farmer.
Drones for monitoring
soil and crop health
The govt. has launched a
project, Sensor based
Smart Agriculture
(SENSAGRI). The
project would be funded
by MCIT, DEITY, ITRA
and ICAR

Research Findings
38

Research Findings:
39

 Location : The Ohio State
University, US
 Sensor used : E-Nose system
(36 cm × 30 cm × 15 cm). It
include four gas sensors (MQ-
138, MQ-135, MQ-3, and TGS
2602)
 Crop : Tomato
 Pest : Whitefly
 Tomato seeds were sown in 10 cm pots for 2–3
weeks under indoor conditions.
 Tomato plants were cultivated under the three
different treatments of whitefly infestation,
mechanical damage, and no stress (Control).
 Plants at 5 weeks of age were used for experiments
and divided into three groups (15 plants for each
group).
 Virus-free greenhouse whiteflies (Trialeurodes
vaporariorum) were obtained from a laboratory
colony raised in the Controlled Environments
Laboratory of Ohio.
40
Experimental Details Treatment Details
Development of Portable E-Nose System for Fast Diagnosis of Whitefly Infestation in Tomato Plant in Greenhouse
Shaoqing et al. (2021)

41
Interface panel of the developed E-nose system Illustration of tomato plant detection using the E-
nose system
HELP TO PULL OUT
THE AIR FLOW
TEFLON AIR BAG
DISCHARGE THE HEADSPACE
AIR OF A PLANT SAMPLE

Sensor performance of E-nose system towards
(a) healthy tomato plants and (b) whitefly-infested plants
42

(a) Sensor performance of E-nose system towards whitefly infested plant
and (b) mechanically damaged plant
43

Result of the three plant group detected by E-nose system
44

A typical GC-MS spectrum of VOCs
45

46

 Location : Canada
 Sensor : Machine
Vision System Design
 RGB complementary
metal-oxide
semiconductors
(CMOS) high
resolution camera was
chosen as a sensor
 Crop: Shallots
47
Experiment details
Sensor fitted on the machine and yield
monitor
Towards A Machine Vision Based Yield Monitor For The Counting And Quality Mapping Of Shallots
Amanda et al. (2020)

48
a) The original image, b) first pre-processed by blurring using a 9 × 9 median filter and 9 × 9 Gaussian
filter, c) it is then converted to HSV colour space and threshold using a predetermined threshold value, d)
morphological operation of opening, e) and closing are applied, f) the distance transform is computed, g)
watershed segmentation is performed on the image to isolate individual onion region, h) identify and
classify them in the original image

Detection result for WST method
49

50
The use of computer vision as an
alternative for yield estimation practices
for specialized vegetable crops.
A fully functional system was developed
to record image and position data of
shallot onion bulbs during harvesting and
to create a geo-tagged image database for
precision yield mapping.
The system used a watershed
segmentation method and had a precision
of 76% and recall of 73% on a sample of
images. The software also reliably
categorized large sized shallots with an
accuracy of 73.3% but was limited when
predicting small (58.6%) and medium
(44.4%) onion sizes.
Onion results using k-means clustering of pixels in thr HSV color
space with k= 6. predicted onion sizes (mm) aare marked directly
on the image

“An Over View of the Implementation of Precision Farming Projects in
Tamil Nadu, India”
 Location : Krishnagiri district of Tamil
Nadu
 Objectives :
To study the cost, benefit and yield under
the precision farming schemes in Tamil
Nadu, and
To understand the status of the projects
implemented under precision faming in
Tamil Nadu.
Period : October 2008 to March 2009 with
the 39 farmers and covered 26 hectare
 The TNPFP was first implemented
Tamil Nadu in Dharmapuri and
Krishnagiri districts during 2004-05.
 The project covered 400 ha with the
main focus on 40 – 60 per cent
enhanced yield and effective market
linkage.
51
Details Project cover

Comparative statement of cost of cultivation under conventional
system and Precision farming
52
Source: Directorate of Extension Education &
Nodal Officer

Details of yield of Precision farming and National Average
53
Source : TNPFP

Biosensor In Precision farming Vs Conventional farming
 It improve crop yield by assisting a good
management, decision of using high
technology sensor and analysis tools
 Increase production
 Detection of pathogen in horticultural
crops
 To determine the variety, distance and
height of any position within required
area
 Labour cost is high
 Skill person required
 Cannot detect diseases or pest in early
stage
 May be chance of errors
 Irrigation about 30-50% of total applied
water is lost due to various conveyance
losses of irrigation system
54
Biosensor in Precision farming Conventional farming

Challenges
55
 Introduction of various Precision farming based technologies requires improved
modern tools.
 In India, 85% of farmers are small and marginal and adoption of these technologies
may not provide comparable benefits, due initial high cost
 Lack of legislation and policies from the government
 Needs large scale demonstration
 Inadequate understanding of agronomic factors,
 Lack of understanding of geo-statistics necessaries for understanding spatial
variability of crop and soil adopting mapping software and
 Limited ability to integrate information from diverse sources with varying
resolution and intensity

Future Thrust
56
Two major problems for implementing precision agriculture in our country are
a. Small size operational holding and
b. Cost of precision farming system.
c. Lack of success stories or cost-benefit stuied on precision farming.
d. Knowledge and technological gaps.
e. Lack of data availability in terms of quality and cost.

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57

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